#types of data ingestion

[mostlysignssomeportents]

The cod-Marxism of personalized pricing

Picks and Shovels is a new, standalone technothriller starring Marty Hench, my two-fisted, hard-fighting, tech-scam-busting forensic accountant. You can pre-order it on my latest Kickstarter, which features a brilliant audiobook read by Wil Wheaton.

The social function of the economics profession is to explain, over and over again, that your boss is actually right and that you don't really want the things you want, and you're secretly happy to be abused by the system. If that wasn't true, why would your "choose" commercial surveillance, abusive workplaces and other depredations?

In other words, economics is the "look what you made me do" stick that capitalism uses to beat us with. We wouldn't spy on you, rip you off or steal your wages if you didn't choose to use the internet, shop with monopolists, or work for a shitty giant company. The technical name for this ideology is "public choice theory":

https://pluralistic.net/2022/06/05/regulatory-capture/

Of all the terrible things that economists say we all secretly love, one of the worst is "price discrimination." This is the idea that different customers get charged different amounts based on the merchant's estimation of their ability to pay. Economists insist that this is "efficient" and makes us all better off. After all, the marginal cost of filling the last empty seat on the plane is negligible, so why not sell that seat for peanuts to a flier who doesn't mind the uncertainty of knowing whether they'll get a seat at all? That way, the airline gets extra profits, and they split those profits with their customers by lowering prices for everyone. What's not to like?

Plenty, as it turns out. With only four giant airlines who've carved up the country so they rarely compete on most routes, why would an airline use their extra profits to lower prices, rather than, say, increasing their dividends and executive bonuses?

For decades, the airline industry was the standard-bearer for price discrimination. It was basically impossible to know how much a plane ticket would cost before booking it. But even so, airlines were stuck with comparatively crude heuristics to adjust their prices, like raising the price of a ticket that didn't include a Saturday stay, on the assumption that this was a business flyer whose employer was footing the bill:

https://pluralistic.net/2024/06/07/drip-drip-drip/#drip-off

With digitization and mass commercial surveillance, we've gone from pricing based on context (e.g. are you buying your ticket well in advance, or at the last minute?) to pricing based on spying. Digital back-ends allow vendors to ingest massive troves of commercial surveillance data from the unregulated data-broker industry to calculate how desperate you are, and how much money you have. Then, digital front-ends – like websites and apps – allow vendors to adjust prices in realtime based on that data, repricing goods for every buyer.

As digital front-ends move into the real world (say, with digital e-ink shelf-tags in grocery stores), vendors can use surveillance data to reprice goods for ever-larger groups of customers and types of merchandise. Grocers with e-ink shelf tags reprice their goods thousands of times, every day:

https://pluralistic.net/2024/03/26/glitchbread/#electronic-shelf-tags

Here's where an economist will tell you that actually, your boss is right. Many groceries are perishable, after all, and e-ink shelf tags allow grocers to reprice their goods every minute or two, so yesterday's lettuce can be discounted every fifteen minutes through the day. Some customers will happily accept a lettuce that's a little gross and liztruss if it means a discount. Those customers get a discount, the lettuce isn't thrown out at the end of the day, and everyone wins, right?

Well, sure, if. If the grocer isn't part of a heavily consolidated industry where competition is a distant memory and where grocers routinely collude to fix prices. If the grocer doesn't have to worry about competitors, why would they use e-ink tags to lower prices, rather than to gouge on prices when demand surges, or based on time of day (e.g. making frozen pizzas 10% more expensive from 6-8PM)?

And unfortunately, groceries are one of the most consolidated sectors in the modern world. What's more, grocers keep getting busted for colluding to fix prices and rip off shoppers:

https://www.cbc.ca/news/business/loblaw-bread-price-settlement-1.7274820

Surveillance pricing is especially pernicious when it comes to apps, which allow vendors to reprice goods based not just on commercially available data, but also on data collected by your pocket distraction rectangle, which you carry everywhere, do everything with, and make privy to all your secrets. Worse, since apps are a closed platform, app makers can invoke IP law to criminalize anyone who reverse-engineers them to figure out how they're ripping you off. Removing the encryption from an app is a potential felony punishable by a five-year prison sentence and a $500k fine (an app is just a web-page skinned in enough IP to make it a crime to install a privacy blocker on it):

https://pluralistic.net/2024/08/15/private-law/#thirty-percent-vig

Large vendors love to sell you shit via their apps. With an app, a merchant can undetectably change its prices every few seconds, based on its estimation of your desperation. Uber pioneered this when they tweaked the app to raise the price of a taxi journey for customers whose batteries were almost dead. Today, everyone's getting in on the act. McDonald's has invested in a company called Plexure that pitches merchants on the use case of raising the cost of your normal breakfast burrito by a dollar on the day you get paid:

https://pluralistic.net/2024/06/05/your-price-named/#privacy-first-again

Surveillance pricing isn't just a matter of ripping off customers, it's also a way to rip off workers. Gig work platforms use surveillance pricing to titrate their wage offers based on data they buy from data brokers and scoop up with their apps. Veena Dubal calls this "algorithmic wage discrimination":

https://pluralistic.net/2023/04/12/algorithmic-wage-discrimination/#fishers-of-men

Take nurses: increasingly, American hospitals are firing their waged nurses and replacing them with gig nurses who are booked in via an app. There's plenty of ways that these apps abuse nurses, but the most ghastly is in how they price nurses' wages. These apps buy nurses' financial data from data-brokers so they can offer lower wages to nurses with lots of credit card debt, on the grounds that crushing debt makes nurses desperate enough to accept a lower wage:

https://pluralistic.net/2024/12/18/loose-flapping-ends/#luigi-has-a-point

This week, the excellent Lately podcast has an episode on price discrimination, in which cohost Vass Bednar valiantly tries to give economists their due by presenting the strongest possible case for charging different prices to different customers:

https://www.theglobeandmail.com/podcasts/lately/article-the-end-of-the-fixed-price/

Bednar really tries, but – as she later agrees – this just isn't a very good argument. In fact, the only way charging different prices to different customers – or offering different wages to different workers – makes sense is if you're living in a socialist utopia.

After all, a core tenet of Marxism is "from each according to his ability, to each according to his needs." In a just society, people who need more get more, and people who have less, pay less:

https://en.wikipedia.org/wiki/From_each_according_to_his_ability,_to_each_according_to_his_needs

Price discrimination, then, is a Bizarro-world flavor of cod-Marxism. Rather than having a democratically accountable state that sets wages and prices based on need and ability, price discrimination gives this authority to large firms with pricing power, no regulatory constraints, and unlimited access to surveillance data. You couldn't ask for a neater example of the maxim that "What matters isn't what technology does. What matters is who it does it for; and who it does it to."

Neoclassical economists say that all of this can be taken care of by the self-correcting nature of markets. Just give consumers and workers "perfect information" about all the offers being made for their labor or their business, and things will sort themselves out. In the idealized models of perfectly spherical cows of uniform density moving about on a frictionless surface, this does work out very well:

https://pluralistic.net/2023/04/03/all-models-are-wrong/#some-are-useful

But while large companies can buy the most intimate information imaginable about your life and finances, IP law lets them capture the state and use it to shut down any attempts you make to discover how they operate. When an app called Para offered Doordash workers the ability to preview the total wage offered for a job before they accepted it, Doordash threatened them with eye-watering legal penalties, then threw dozens of full-time engineers at them, changing the app several times per day to shut out Para:

https://pluralistic.net/2021/08/07/hr-4193/#boss-app

And when an Austrian hacker called Mario Zechner built a tool to scrape online grocery store prices – discovering clear evidence of price-fixing conspiracies in the process – he was attacked by the grocery cartel for violating their "IP rights":

https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

This is Wilhoit's Law in action:

Conservatism consists of exactly one proposition, to wit: There must be in-groups whom the law protects but does not bind, alongside out-groups whom the law binds but does not protect.

https://en.wikipedia.org/wiki/Francis_M._Wilhoit#Wilhoit's_law

Of course, there wouldn't be any surveillance pricing without surveillance. When it comes to consumer privacy, America is a no-man's land. The last time Congress passed a new consumer privacy law was in 1988, when they enacted the Video Privacy Protection Act, which bans video-store clerks from revealing which VHS cassettes you take home. Congress has not addressed a single consumer privacy threat since Die Hard was still playing in theaters.

Corporate bullies adore a regulatory vacuum. The sleazy data-broker industry that has festered and thrived in the absence of a modern federal consumer privacy law is absolutely shameless. For example, every time an app shows you an ad, your location is revealed to dozens of data-brokers who pretend to be bidding for the right to show you an ad. They store these location data-points and combine them with other data about you, which they sell to anyone with a credit card, including stalkers, corporate spies, foreign governments, and anyone hoping to reprice their offerings on the basis of your desperation:

https://www.404media.co/candy-crush-tinder-myfitnesspal-see-the-thousands-of-apps-hijacked-to-spy-on-your-location/

Under Biden, the outgoing FTC did incredible work to fill this gap, using its authority under Section 5 of the Federal Trade Commission Act (which outlaws "unfair and deceptive" practices) to plug some of the worst gaps in consumer privacy law:

https://pluralistic.net/2024/07/24/gouging-the-all-seeing-eye/#i-spy

And Biden's CFPB promulgated a rule that basically bans data brokers:

https://pluralistic.net/2024/06/10/getting-things-done/#deliverism

But now the burden of enforcing these rules falls to Trump's FTC, whose new chairman has vowed to end the former FTC's "war on business." What America desperately needs is a new privacy law, one that has a private right of action (so that individuals and activist groups can sue without waiting for a public enforcer to take up their causes) and no "pre-emption" (so that states can pass even stronger privacy laws):

https://www.eff.org/deeplinks/2022/07/federal-preemption-state-privacy-law-hurts-everyone

How will we get that law? Through a coalition. After all, surveillance pricing is just one of the many horrors that Americans have to put up with thanks to America's privacy law gap. The "privacy first" theory goes like this: if you're worried about social media's impact on teens, or women, or old people, you should start by demanding a privacy law. If you're worried about deepfake porn, you should start by demanding a privacy law. If you're worried about algorithmic discrimination in hiring, lending, or housing, you should start by demanding a privacy law. If you're worried about surveillance pricing, you should start by demanding a privacy law. Privacy law won't entirely solve all these problems, but none of them would be nearly as bad if Congress would just get off its ass and catch up with the privacy threats of the 21st century. What's more, the coalition of everyone who's worried about all the harms that arise from commercial surveillance is so large and powerful that we can get Congress to act:

https://pluralistic.net/2023/12/06/privacy-first/#but-not-just-privacy

Economists, meanwhile, will line up to say that this is all unnecessary. After all, you "sold" your privacy when you clicked "I agree" or walked under a sign warning you that facial recognition was in use in this store. The market has figured out what you value privacy at, and it turns out, that value is nothing. Any kind of privacy law is just a paternalistic incursion on your "freedom to contract" and decide to sell your personal information. It is "market distorting."

In other words, your boss is right.

Check out my Kickstarter to pre-order copies of my next novel, Picks and Shovels!

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/01/11/socialism-for-the-wealthy/#rugged-individualism-for-the-poor

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

--

Ser Amantio di Nicolao (modified) https://commons.wikimedia.org/wiki/File:Safeway_supermarket_interior,_Fairfax_County,_Virginia.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

[d8tl55c]

physeng(write, file, "tco_physeng_breakdown.png");

to:compiler {file}

to:compiler {txt: "Internet and Outernet are full of StickFigures with similar body plans, so there are optimizations for rendering vector strokes specifically. it's way more efficient to use those optimizations than keep calculating perfect spheres for no aesthetic benefit."}

{txt: "btw why haven't heat issues been patched yet"}

{txt: "i fixed this years ago for the latest model. remember."}

from:compiler {txt: "Thank you. The avast! nodes will appreciate the credits. TheChosenOne.exe has been unreachable for some time."}

to:compiler {txt: "you mean OuternetPhysEng still won't update their programs"}

from:compiler {txt: "Yes."}

to:compiler {txt: "and still won't provide a specific location?"}

from:compiler {txt: "Do not allow them to bring up the moral argument again."}

to:compiler {txt: "OK. fine. yes. i will spare both of us"}

to:compiler {txt: "abridged or full docs?"}

from:compiler {txt: "Abridged. Please describe the acronyms."}

to:compiler {txt: "ofc"}

{txt: "

sel.per.filter: standard StickFigure component (src)*****. invisible membrane with special collision properties. protects mouthparts.

H2O scoop: avast! code. implements water retrieval from ambient air.

EIS: avast! code. destroys ingested materials identified as, "dangerous" before they reach internal systems.

SOS: avast! code. they only said this one was, "used for control."

ECL: avast! code. recycles some forms of contact energy.

THROUGHLINE: base code, initialization data, and processing space for vitals. found in some form in all StickFigure-type worms. following unique sectors noted: Black Hole Monitoring System, Basic Intake Threat Enum, Fly By Wire.

smaller points list other vital and peripheral systems.

"}

from:compiler {txt: "Thank you. That's enough."}

end(physeng());

@compressedrage as per my previous email /silly

related: pliable stick figure biotech

[mylovesstuffs]

Anon.

Are you fucking serious right now?

I saw your message and I had to take a full-ass walk around my block because I was genuinely afraid I’d punch my goddamn wall.

Let me break this down for you very clearly, so even your AI-smooth-brained moral compass can process it:

You are not entitled to shit.

Not my writing. Not the ending. Not a single fucking word I typed out with my own fucking bleeding fingers.

You think you “need” closure?

Cool. I “need” eight hours of sleep, a functioning spine, and respect from strangers online. Guess what?

None of us are getting what we want today.

Fanfiction is a gift.

Not a product, not a service, not a series finale you paid for on HBO Max.

A gift.

You don’t throw a tantrum because the gift didn’t come with a bonus epilogue and a director’s cut.

Your entire ask is a monument to entitlement. You say “if you're not going to write it, I think it's fine to use Al?”

I did NOT write it for AI. I wrote it for human readers. For emotion. For narrative voice. For soul.

AI doesn’t have that. You want a soulless machine to mock my style and feed you a bootleg version of my work?? Which is, not to say but, the equivalent of a a knock-off Louis Vuitton sold from the back of a van?

Then don’t ask me. Just go to your shameful little corner and live with the fact that you’re the kind of person who disrespects art to feed your own dopamine addiction.

You wanted permission, so now you get the wrath.

I see in your other little asks, “AI is there to make things easier.”

At whose fucking expense? The thousands of fanfic writers whose fics are being scraped, harvested, mimicked and tossed into machine-learning hell so you don’t have to wait for an update?

Do you have any idea how many of us have had our fics [both in our caratblr and other fandom spaces] show up on AI mirror sites or been plagiarized by bots? Do you know how many real authors are losing book deals, commissions, or livelihoods because of this exact entitled logic?

Let me give you a basic fucking rundown since you clearly didn’t do the homework:

AI is not ethical – Generative AI is trained off data sets that include unauthorized, uncredited, scraped work from unpaid artists, writers, poets, journalists, bloggers, fanfic writers—fuck, even therapy forums.

Authors are suing OpenAI for ingesting copyrighted novels without permission.

Fanfic is already vulnerable – We exist in a legal gray area protected only by community ethics and mutual respect. You're breaking both.

You say “I won't post it anywhere, it's just for me.”

Oh, wow, thanks. So you only want to steal quietly. Like that makes it better.

You think the damage only happens when it’s public? WRONG.

Every time you plug an artist’s work into a machine, it gets processed, stored, used for training, forever.

You have no control over where it goes or how it’s repurposed later. You are feeding the beast and killing the creators in the process.

Don’t give me “I respect you but—”

If you respected me, this message wouldn’t exist.

You get your AI-stitched ending, it doesn’t scratch the itch, and you feed it another one.

And another.

And another.

Until the entire fucking archive is turned into a graveyard of replicas and you’re the ghoulish little shit dancing on the corpses of every writer you leeched dry.

And you say “I think I’m just entitled to a bit of closure”?

Entitled. You actually used the word.

Thank you for confirming what we already knew:

That you don’t see us as people.

You see us as content machines that owe you something because you liked our work. You don’t want closure, you want control, and you will NOT get it from me.

You’re entitled to a bath, a glass of water, and the air you breathe—not my writing, not my thoughts, and definitely not a fucking auto-generated Frankenstein mockery of my style you can jerk off your emotions to.

So here's your closure:

No, you may not touch my fic.

No, you may not feed it to a bot.

No, you may not engage with my writing, my blog, my friend's blog, or my community ever again.

Block me. Report me. Cry about it.

But know this:

Every time you open an AI generator to finish a story you didn’t write, you are choosing to destroy the very creators you claim to admire.

You should be ashamed, but you won’t be. Because parasites don’t feel guilt, they just suck and suck and suck until there’s nothing left.

I'll never forget this time and date.

I hope it was worth it.

Let this be your final fucking warning.

— Celeste.

[probablyasocialecologist]

Regardless of what companies and investors may say, artificial intelligence is not actually intelligent in the way most humans would understand it. To generate words and images, AI tools are trained on large databases of training data that is often scraped off the open web in unimaginably large quantities, no matter who owns it or what biases come along with it. When a user then prompts ChatGPT or DALL-E to spit out some text or visuals, the tools aren’t thinking about the best way to represent those prompts because they don’t have that ability. They’re comparing the terms they’re presented with the patterns they formed from all the data that was ingested to train their models, then trying to assemble elements from that data to reflect what the user is looking for. In short, you can think of it like a more advanced form of autocorrect on your phone’s keyboard, predicting what you might want to say next based on what you’ve already written and typed out in the past. If it’s not clear, that means these systems don’t create; they plagiarize. Unlike a human artist, they can’t develop a new artistic style or literary genre. They can only take what already exists and put elements of it together in a way that responds to the prompts they’re given. There’s good reason to be concerned about what that will mean for the art we consume, and the richness of the human experience.

[...]

AI tools will not eliminate human artists, regardless of what corporate executives might hope. But it will allow companies to churn out passable slop to serve up to audiences at a lower cost. In that way, it allows a further deskilling of art and devaluing of artists because instead of needing a human at the center of the creative process, companies can try to get computers to churn out something good enough, then bring in a human with no creative control and a lower fee to fix it up. As actor Keanu Reeves put it to Wired earlier this year, “there’s a corporatocracy behind [AI] that’s looking to control those things. … The people who are paying you for your art would rather not pay you. They’re actively seeking a way around you, because artists are tricky.” To some degree, this is already happening. Actors and writers in Hollywood are on strike together for the first time in decades. That’s happening not just because of AI, but how the movie studios and steaming companies took advantage of the shift to digital technologies to completely remake the business model so workers would be paid less and have less creative input. Companies have already been using AI tools to assess scripts, and that’s one example of how further consolidation paired with new technologies are leading companies to prioritize “content” over art. The actors and writers worry that if they don’t fight now, those trends will continue — and that won’t just be bad for them, but for the rest of us too.

[botgal]

oh and which sites will be affected ofc

AB 3080, according to the bill's text, would affect websites which sells the types of items listed below

"

(b) Products or services that are illegal to sell to a minor under state law that are subject to subdivision (a) include all of the following:

(1) An aerosol container of paint that is capable of defacing property, as referenced in Section 594.1 of the Penal Code.

(2) Etching cream that is capable of defacing property, as referenced in Section 594.1 of the Penal Code.

(3) Dangerous fireworks, as referenced in Sections 12505 and 12689 of the Health and Safety Code.

(4) Tanning in an ultraviolet tanning device, as referenced in Sections 22702 and 22706 of the Business and Professions Code.

(5) Dietary supplement products containing ephedrine group alkaloids, as referenced in Section 110423.2 of the Health and Safety Code.

(6) Body branding, as referenced in Sections 119301 and 119302 of the Health and Safety Code.

(c) Products or services that are illegal to sell to a minor under state law that are subject to subdivision (a) include all of the following:

(1) Firearms or handguns, as referenced in Sections 16520, 16640, and 27505 of the Penal Code.

(2) A BB device, as referenced in Sections 16250 and 19910 of the Penal Code.

(3) Ammunition or reloaded ammunition, as referenced in Sections 16150 and 30300 of the Penal Code.

(4) Any tobacco, cigarette, cigarette papers, blunt wraps, any other preparation of tobacco, any other instrument or paraphernalia that is designed for the smoking or ingestion of tobacco, products prepared from tobacco, or any controlled substance, as referenced in Division 8.5 (commencing with Section 22950) of the Business and Professions Code, and Sections 308, 308.1, 308.2, and 308.3 of the Penal Code.

(5) Electronic cigarettes, as referenced in Section 119406 of the Health and Safety Code.

(6) A less lethal weapon, as referenced in Sections 16780 and 19405 of the Penal Code."

This is stated explicitly to include "internet website on which the owner of the internet website, for commercial gain, knowingly publishes sexually explicit content that, on an annual basis, exceeds one-third of the contents published on the internet website". Wherein "sexually explicit content" is defined as "visual imagery of an individual or individuals engaging in an act of masturbation, sexual intercourse, oral copulation, or other overtly sexual conduct that, taken as a whole, lacks serious literary, artistic, political, or scientific value."

This would likely not include websites like AO3 or any website which displays NSFW content not in excess of 1/3 of the content on the site. Possibly not inclusive of writing because of the "visual imagery", but don't know at this time. In any case we don't want to set a precedent off of which it could springboard into non-commercial websites or any and all places with NSFW content.

AB 1949 is a lot more broad because it's about general data collection by any and all websites in which they might sell personal data collected by the website to third parties, especially if aimed specifically at minors or has a high chance of minors commonly accesses the site. But with how broad the language is I can't say there would be ANY limits to this one. So both are equally bad and would require equal attention in my opinion.

[allthebrazilianpolitics]

Study reveals that most widely used herbicide in Brazil increases chance of infant mortality

Applied especially to soybean crops, glyphosate may have been responsible for more than 500 infant deaths annually over the last decade

Glyphosate accounted for 62% of herbicide use in Brazil between 2009 and 2016, and it was especially used in soybean production. In 2017, it represented 30% of global herbicide use. It is estimated that since the 1990s, following the creation of seeds resistant to this substance, the use of this product has increased around 15-fold. Furthermore, according to Data Intelligence, the use of glyphosate on crops is expected to grow 800% by 2025. In this context, a study by Fundação Getulio Vargas found that this herbicide has effects on human health and identified an increase in infant mortality in locations close to where glyphosate has been applied.

The study indicates that between 2000 and 2010, there was a 5% increase in the infant mortality rate, which corresponds to around 503 deaths per year, among infants whose mothers lived close to where glyphosate was applied, more specifically, in the path of water flow carrying the substance applied to crops into rivers, streams and wells. The period analyzed in the research coincides precisely with the time when there was a sharp rise in the use of glyphosate.

Despite these indications found by the scientists, Rudi Rocha, a researcher at Fundação Getulio Vargas’ Sao Paulo School of Business Administration (FGV EAESP) and co-author of the study, says that this figure probably underestimates the overall effect of glyphosate use on human health.

“There is concern about the subclinical toxicity of these substances on people in general, who are not subject to direct exposure, but exposed to low concentrations through ingestion of contaminated water or food,” the researcher explains. He also points out that this type of intoxication is just the tip of the iceberg, as pesticides like this are capable of causing a wide range of asymptomatic effects at very low levels of exposure, which do not produce obvious signs and symptoms.

Continue reading.

[spacetimewithstuartgary]

The Vera C. Rubin Observatory will detect millions of exploding stars

Measuring distances across the universe is much more challenging than measuring distances on Earth. Is a brighter star closer to Earth than another, or is it just emitting more light? To make confident distance measurements, scientists rely on objects that emit a known amount of light, like Type Ia supernovae.

These spectacular explosions, among the brightest to ever be recorded in the night sky, result from the violent deaths of white dwarf stars and provide scientists with a reliable cosmic yardstick. Their brightness and color, combined with information about their host galaxies, allow scientists to calculate their distance and how much the universe expanded while their light made its journey to us. With enough Type Ia supernovae observations, scientists can measure the universe's expansion rate and whether it changes over time.

Although we've caught thousands of Type Ia supernovae to date, seeing them once or twice is not enough—there is a goldmine of information in how their fleeting light varies over time. NSF–DOE Vera C. Rubin Observatory will soon begin scanning the southern hemisphere sky every night for ten years, covering the entire hemisphere approximately every few nights. Every time Rubin detects an object changing brightness or position it will send an alert to the science community. With such rapid detection, Rubin will be our most powerful tool yet for spotting Type Ia supernovae before they fade away.

Rubin Observatory is a joint program of NSF NOIRLab and DOE's SLAC National Accelerator Laboratory, which will cooperatively operate Rubin.

Scientists like Anais Möller, a member of the Rubin/LSST Dark Energy Science Collaboration, look forward to Rubin's decade-long Legacy Survey of Space and Time (LSST), during which it's expected to detect millions of Type Ia supernovae.

"The large volume of data from Rubin will give us a sample of all kinds of Type Ia supernovae at a range of distances and in many different types of galaxies," says Möller.

In fact, Rubin will discover many more Type Ia supernovae in the first few months of the LSST than were used in the initial discovery of dark energy—the mysterious force causing the universe to expand faster than expected based on gravitational theory. Current measurements hint that dark energy might change over time, which—if confirmed—could help refine our understanding of the universe's age and evolution. That in turn would impact what we understand about how the universe formed, including how quickly stars and galaxies formed in the early universe.

With a much larger set of Type Ia supernovae from across the universe scientists will be able to refine our existing map of space and time, getting a fuller picture of dark energy's influence.

"The universe expanding is like a rubber band being stretched. If dark energy is not constant, that would be like stretching the rubber band by different amounts at different points," says Möller. "I think in the next decade we will be able to constrain whether dark energy is constant or evolving with cosmic time. Rubin will allow us to do that with Type Ia supernovae."

Every night Rubin Observatory will produce about 20 terabytes of data and generate up to 10 million alerts—no other telescope in history has produced a firehose of data quite like this. It has required scientists to rethink the way they manage rapid alerts and to develop methods and systems to handle the large incoming datasets.

Rubin's deluge of nightly alerts will be managed and made available to scientists through seven community software systems that will ingest and process these alerts before serving them up to scientists around the world. Möller, together with a large collaboration of scientists across expertises, is developing one of these systems, called Fink.

The software systems collect the alerts from Rubin each night, merge Rubin data with other datasets, and using machine-learning, classify them according to their type, such as kilonovae, variable stars, or Type Ia supernovae, among others. Scientists using one of Rubin's community systems, like Fink, will be able to sort the massive dataset of alerts according to selected filters, allowing them to quickly home in on the data that are useful for their research.

"Because of the large volumes of data, we can't do science the same way we did before," says Möller. "Rubin is a generational shift. And our responsibility is developing the methods that will be used by the next generation."

[mariacallous]

In 2017, soon after Google researchers invented a new kind of neural network called a transformer, a young OpenAI engineer named Alec Radford began experimenting with it. What made the transformer architecture different from that of existing A.I. systems was that it could ingest and make connections among larger volumes of text, and Radford decided to train his model on a database of seven thousand unpublished English-language books—romance, adventure, speculative tales, the full range of human fantasy and invention. Then, instead of asking the network to translate text, as Google’s researchers had done, he prompted it to predict the most probable next word in a sentence.

The machine responded: one word, then another, and another—each new term inferred from the patterns buried in those seven thousand books. Radford hadn’t given it rules of grammar or a copy of Strunk and White. He had simply fed it stories. And, from them, the machine appeared to learn how to write on its own. It felt like a magic trick: Radford flipped the switch, and something came from nothing.

His experiments laid the groundwork for ChatGPT, released in 2022. Even now, long after that first jolt, text generation can still provoke a sense of uncanniness. Ask ChatGPT to tell a joke or write a screenplay, and what it returns—rarely good, but reliably recognizable—is a sort of statistical curve fit to the vast corpus it was trained on, every sentence containing traces of the human experience encoded in that data.

When I’m drafting an e-mail and type, “Hey, thanks so much for,” then pause, and the program suggests “taking,” then “the,” then “time,” I’ve become newly aware of which of my thoughts diverge from the pattern and which conform to it. My messages are now shadowed by the general imagination of others. Many of whom, it seems, want to thank someone for taking . . . the . . . time.

That Radford’s breakthrough happened at OpenAI was no accident. The organization had been founded, in 2015, as a nonprofit “Manhattan Project for A.I.,” with early funding from Elon Musk and leadership from Sam Altman, who soon became its public face. Through a partnership with Microsoft, Altman secured access to powerful computing infrastructures. But, by 2017, the lab was still searching for a signature achievement. On another track, OpenAI researchers were teaching a T-shaped virtual robot to backflip: the bot would attempt random movements, and human observers would vote on which resembled a flip. With each round of feedback, it improved—minimally, but measurably. The company also had a distinctive ethos. Its leaders spoke about the existential threat of artificial general intelligence—the moment, vaguely defined, when machines would surpass human intelligence—while pursuing it relentlessly. The idea seemed to be that A.I. was potentially so threatening that it was essential to build a good A.I. faster than anyone else could build a bad one.

Even Microsoft’s resources weren’t limitless; chips and processing power devoted to one project couldn’t be used for another. In the aftermath of Radford’s breakthrough, OpenAI’s leadership—especially the genial Altman and his co-founder and chief scientist, the faintly shamanistic Ilya Sutskever—made a series of pivotal decisions. They would concentrate on language models rather than, say, back-flipping robots. Since existing neural networks already seemed capable of extracting patterns from data, the team chose not to focus on network design but instead to amass as much training data as possible. They moved beyond Radford’s cache of unpublished books and into a morass of YouTube transcripts and message-board chatter—language scraped from the internet in a generalized trawl.

That approach to deep learning required more computing power, which meant more money, putting strain on the original nonprofit model. But it worked. GPT-2 was released in 2019, an epochal event in the A.I. world, followed by the more consumer-oriented ChatGPT in 2022, which made a similar impression on the general public. User numbers surged, as did a sense of mystical momentum. At an off-site retreat near Yosemite, Sutskever reportedly set fire to an effigy representing unaligned artificial intelligence; at another retreat, he led colleagues in a chant: “Feel the AGI. Feel the AGI.”

In the prickly “Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI” (Penguin Press), Karen Hao tracks the fallout from the GPT breakthroughs across OpenAI’s rivals—Google, Meta, Anthropic, Baidu—and argues that each company, in its own way, mirrored Altman’s choices. The OpenAI model of scale at all costs became the industry’s default. Hao’s book is at once admirably detailed and one long pointed finger. “It was specifically OpenAI, with its billionaire origins, unique ideological bent, and Altman’s singular drive, network, and fundraising talent, that created a ripe combination for its particular vision to emerge and take over,” she writes. “Everything OpenAI did was the opposite of inevitable; the explosive global costs of its massive deep learning models, and the perilous race it sparked across the industry to scale such models to planetary limits, could only have ever arisen from the one place it actually did.” We have been, in other words, seduced—lulled by the spooky, high-minded rhetoric of existential risk. The story of A.I.’s evolution over the past decade, in Hao’s telling, is not really about the date of machine takeover or the degree of human control over the technology—the terms of the A.G.I. debate. Instead, it’s a corporate story about how we ended up with the version of A.I. we’ve got.

The “original sin” of this arm of technology, Hao writes, lay in a decision by a Dartmouth mathematician named John McCarthy, in 1955, to coin the phrase “artificial intelligence” in the first place. “The term lends itself to casual anthropomorphizing and breathless exaggerations about the technology’s capabilities,” she observes. As evidence, she points to Frank Rosenblatt, a Cornell professor who, in the late fifties, devised a system that could distinguish between cards with a small square on the right versus the left. Rosenblatt promoted it as brain-like—on its way to sentience and self-replication—and these claims were picked up and broadcast by the New York Times. But a broader cultural hesitancy about the technology’s implications meant that, once OpenAI made its breakthrough, Altman—its C.E.O.—came to be seen not only as a fiduciary steward but also as an ethical one. The background question that began to bubble up around the Valley, Keach Hagey writes in “The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future” (Norton), “first whispered, then murmured, then popping up in elaborate online essays from the company’s defectors: Can we trust this person to lead us to AGI?”

Within the world of tech founders, Altman might have seemed a pretty trustworthy candidate. He emerged from his twenties not just very influential and very rich (which isn’t unusual in Silicon Valley) but with his moral reputation basically intact (which is). Reared in a St. Louis suburb in a Reform Jewish household, the eldest of four children of a real-estate developer and a dermatologist, he had been identified early on as a kind of polymathic whiz kid at John Burroughs, a local prep school. “His personality kind of reminded me of Malcolm Gladwell,” the school’s head, Andy Abbott, tells Hagey. “He can talk about anything and it’s really interesting”—computers, politics, Faulkner, human rights.

Altman came out as gay at sixteen. At Stanford, according to Hagey, whose biography is more conventional than Hao’s but is quite compelling, he launched a student campaign in support of gay marriage and briefly entertained the possibility of taking it national. At an entrepreneur fair during his sophomore year, in 2005, the physically slight Altman stood on a table, flipped open his phone, declared that geolocation was the future, and invited anyone interested to join him. Soon, he dropped out and was running a company called Loopt. Abbott remembered the moment he heard that his former student was going into tech. “Oh, don’t go in that direction, Sam,” he said. “You’re so personable!”

Personability plays in Silicon Valley, too. Loopt was a modest success, but Altman made an impression. “He probably weighed a hundred and ten pounds soaking wet, and he’s surrounded by all these middle-aged adults that are just taking in his gospel,” an executive who encountered him at the time tells Hagey. “Anyone who came across him at the time wished they had some of what he had.”

By his late twenties, Altman had parlayed his Loopt millions into a series of successful startup investments and become the president of Y Combinator, the tech mega-incubator that has spun off dozens of billion-dollar companies. The role made him a first point of contact for Valley elders curious about what was coming next. From Jeff Bezos, he borrowed the habit of introducing two people by e-mail with a single question mark; from Paul Graham, Y Combinator’s co-founder, he absorbed the idea that startups should “add a zero”—always think bigger. It was as if he were running an internal algorithm trained on the corpus of Silicon Valley-founder lore, predicting the next most likely move.

To the elders he studied, Altman was something like the tech world’s radiant child, both its promise and its mascot. Peter Thiel once remarked that Altman was “just at the absolute epicenter, maybe not of Silicon Valley, but of the Silicon Valley zeitgeist.” (Altman is now married to a young Australian techie he met in Thiel’s hot tub.) Graham offered his own version: “You could parachute him into an island full of cannibals and come back in five years and he’d be king.” Some kind of generational arbitrage seemed to be under way. In 2008, Altman began attending Sun Valley Conference, an exclusive annual retreat for industry leaders, where he eventually became “close friends,” we learn, with Barry Diller and Diane von Furstenberg. Yet, in the mid-twenty-tens, he still shared an apartment with his two brothers. Hao records a later incident in which he offered ketamine to an employee he’d just fired. He was both the iconic child to the tech world’s adults and the iconic adult to its children.

An interesting artifact of the past decade in American life is that the apocalyptic sensibility that came to grip U.S. politics during the 2016 Presidential campaign—the conviction, on both right and left, that the existing structure simply could not hold—had already bubbled up in Silicon Valley a few years earlier. By 2015, Altman had been donating to Democratic candidates and seemed to have seriously considered a run for governor of California. But he also told Tad Friend, in a New Yorker Profile, that he was preparing for civilizational collapse and had stockpiled “guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to.”

One view is that tech billionaires saw the brink early because they understood just how unequal—and therefore unstable—American society was becoming. But, inside the Valley, that anxiety often expressed itself in the language of existential risk. In particular, fears about runaway artificial intelligence surged around the time of the 2014 publication of “Superintelligence,” by the philosopher Nick Bostrom. According to Hao, Elon Musk became fixated on an A.I. technologist, Demis Hassabis—a co-founder of DeepMind, which had recently been acquired by Google—whom Musk reportedly viewed as a “supervillain.” That same year, at an M.I.T. symposium, Musk warned that experiments in artificial intelligence risked “summoning the demon.”

Altman had been itching for a bigger project. The next Memorial Day weekend, he gathered hundreds of young Y Combinator protégés for an annual glamping retreat among the redwoods of Mendocino County. The night before, he had beaten a group of Y Combinator staffers at Settlers of Catan. Now, standing before them, he announced that his interests had narrowed—from, roughly, all of technology to three subjects that he believed could fundamentally change humanity: nuclear energy, pandemics, and, most profound of all, machine superintelligence.

That same month, Altman sent an e-mail to Musk. “Been thinking a lot about whether it’s possible to stop humanity from developing AI,” he wrote. “I think the answer is almost definitely not. If it’s going to happen anyway, it seems like it would be good for someone other than Google to do it first.” Altman proposed his Manhattan Project for A.I. so that the technology, as he put it, would “belong to the world,” through some form of nonprofit. Musk replied, “probably worth a conversation.”

It fell to Chuck Schumer, of all people, to offer the secular-liberal benediction for the project—by then consolidated as OpenAI and led by Altman, who had sidelined Musk. “You’re doing important work,” the New York senator told the company’s employees, seated near a TV projecting a fire, during an off-the-record visit to OpenAI’s headquarters in 2019, as Hao documents. “We don’t fully understand it, but it’s important.” Schumer went on, “And I know Sam. You’re in good hands.”

How do people working in A.I. view the technology? The standard account, one that Hao follows, divides them into two camps: the boomers, who are optimistic about AI’s potential benefits for humanity and want to accelerate its development, and the doomers, who emphasize existential risk and edge toward paranoia. OpenAI, in its original conception, was partially a doomer project. Musk’s particular fear about Demis Hassabis was that, if Google assigned a potential A.G.I. the goal of maximizing profits, it might try to take out its competitors at any cost. OpenAI was meant to explore this technological frontier in order to keep it out of malign hands.

But in early 2018 Musk left. The organization was struggling to raise funds—he had pledged to raise a billion dollars but ultimately contributed less than forty-five million—and a faction within OpenAI was pushing to convert it to a for-profit entity, both to attract capital and to lure top researchers with equity. At the meeting where Musk announced his departure, he gave contradictory explanations: OpenAI, he said, wouldn’t be able to build an A.G.I. as a nonprofit, and that Tesla had more resources to pursue this goal, but he also suggested that the best place to pursue A.G.I. was elsewhere. An intern pointed out that Musk had insisted that the for-profit dynamic would undermine safety in developing A.G.I. “Isn’t this going back to what you said you didn’t want to do?” he asked. “You can’t imagine how much time I’ve spent thinking about this,” Musk replied. “I’m truly scared about this issue.” He also called the intern a jackass.

As OpenAI evolved into a nonprofit with a for-profit subsidiary, it came to house both perspectives: a doomer group focussed on safety and research, whose principal advocate was the Italian American scientist Dario Amodei; and a boomer culture focussed on products and applications, often led by Greg Brockman, an M.I.T. dropout and software engineer who pushed the organization toward embracing commercialization. But these lines crossed. Amodei ultimately left the company, alongside his sister, Daniela, insisting that OpenAI had abandoned its founding ethos, though, in Hao’s view, the company they founded, Anthropic, would “in time show little divergence” from OpenAI’s model: the same fixation on scale, the same culture of secrecy. From the other direction came Ilya Sutskever, who had made a major breakthrough in A.I. research as a graduate student in Toronto, and who would become perhaps OpenAI’s most influential theorist. He had once been an unabashed boomer. “I think that it’s fairly likely,” he told the A.I. journalist Cade Metz, “that it will not take too long of a time for the entire surface of the Earth to become covered with data centers and power stations.” By 2023, however, when he helped orchestrate a briefly successful corporate coup against Altman, he was firmly aligned with the doomers. The trajectories of Sutskever and the Amodeis suggest a more fluid category—the boomer-doomers.

Those who most believe in a cause and those who most fear it tend to share one essential assessment: they agree on its power. In this case, the prospect of a technology that could end a phase of civilization drew both camps—boomers and doomers—toward the same flame. Helen Toner, an A.I.-safety expert and academic who eventually joined OpenAI’s board, had spent time studying the fast-evolving A.I. scene in China, the United States’ chief rival in the global race. As Hagey recounts, “Among the things she found notable in China was how reluctant AI engineers were to discuss the social implications of what they were doing. In the Bay Area, meanwhile, they seemed to want to do nothing but.”

Yet OpenAI’s success hinged less on speculative philosophies than on more familiar systems: the flexibility of American capital, and Altman’s personal charm. In 2018, while attending the Sun Valley Conference, in Idaho, Altman ran into Microsoft’s C.E.O., Satya Nadella, in a stairwell and pitched him on a collaboration. Though Bill Gates was skeptical, most of Nadella’s team was enthusiastic. Within a year, Microsoft had announced an investment of a billion dollars in OpenAI—much of it in the form of credits on its cloud platform, Azure. That figure later rose beyond ten billion. Hao speaks with a Chinese A.I. researcher who puts it plainly: “In China, which rivals the U.S. in AI talent, no team of researchers and engineers, no matter how impressive, would get $1 billion, let alone ten times more, to develop a massively expensive technology without an articulated vision of exactly what it would look like and what it would be good for.”

Nadella appears only in passing in both of these books—he’s the adult in the room, and adults are famously not so interesting. But after Microsoft’s multibillion-dollar investments, his influence over OpenAI has come to appear at least as consequential as Altman’s. It was Nadella, after all, who intervened to end the brief 2023 coup, after which Altman was swiftly reinstalled as C.E.O. The year before, Sutskever remarked that “it may be that today’s neural networks are slightly conscious”—a comment to which a scientist at a rival A.I. company replied, “In the same sense that it may be that a large field of wheat is slightly pasta.” Nadella, by contrast, seems broadly allergic to boomer-doomer metaphysics.

The deeper dynamic of contemporary artificial intelligence may be that it reflects, rather than transcends, the corporate conditions of its creation—just as Altman mirrored the manners of his Silicon Valley elders, or as a chatbot’s replies reflect the texts it has been trained on. Appearing recently on Dwarkesh Patel’s influential tech podcast, Nadella, a smooth and upbeat presence, dismissed A.G.I. as a meaningless category. When Patel pressed him on whether A.I. agents would eventually take over not only manual labor but cognitive work, Nadella replied that this might be for the best: “Who said my life’s goal is to triage my e-mail, right? Let an A.I. agent triage my e-mail. But after having triaged my e-mail, give me a higher-level cognitive-labor task of, hey, these are the three drafts I really want you to review.” And if it took over that second thing? Nadella said, “There will be a third thing.”

Nadella seemed quite convinced that A.I. remains a normal technology, and his instinct was to try to narrow each question, so that he was debating project architecture rather than philosophy. When Patel wondered if Nadella would add an A.I. agent to Microsoft’s board, a fairly dystopian-sounding proposition, Nadella replied that Microsoft engineers were currently experimenting with an A.I. agent in Teams, to organize and redirect human team members, and said that he could see the use of having such an agent on Microsoft’s board. It did sound a bit less scary, and also maybe a bit less interesting.

Much like Altman, Nadella is now trying to shift the way the public thinks about A.I. by changing the way it’s talked about—less science fiction, more office productivity. It’s an uphill fight, and at least partly the industry’s own fault. The early, very public bouts of boomerism and doomerism helped attract investment and engineering talent, but they also seeded a broad, low-level unease. If Sutskever—who knew as much about the technology as anyone—could declare it “slightly conscious,” it becomes markedly harder for Nadella, three years later, to reassure the public that what we’re really talking about is just helpful new features in Microsoft Teams.

In other ways, too, Altman is contending with a shifting cultural tide. Sometime around 2016, the tone of tech coverage began to darken. The hagiographic mode gave way to a more prosecutorial one. David Kirkpatrick’s “The Facebook Effect” (2010) has its successor in Sarah Wynn-Williams’s “Careless People” (2025); Michael Lewis’s “The New New Thing” (1999) has been countered by Emily Chang’s “Brotopia” (2018); even Amazon’s great chronicler, Brad Stone, moved from “The Everything Store” (2013) to the more skeptical “Amazon Unbound” (2021).

Hao’s reporting inside OpenAI is exceptional, and she’s persuasive in her argument that the public should focus less on A.I.’s putative “sentience” and more on its implications for labor and the environment. Still, her case against Altman can feel both very personal and slightly overheated. Toward the end of “Empire of AI,” she writes that he has “a long history of dishonesty, power grabbing, and self-serving tactics.” (Welcome to the human race, Sam.) Hao tries hard, if not very successfully, to bolster an accusation made public in 2021 by his sister Annie Altman—that, beginning when she was three and Sam was twelve, he climbed into her bed and molested her, buried memories that she says she recovered during therapy in her twenties. (Altman denies the allegation.) This new, more critical vision of the tech founders risks echoing Musk’s vendetta against Hassabis—inflating contingent figures into supervillains, out of ambient anxiety.

Altman’s story is at once about a man changing artificial intelligence and about how A.I.’s evolving nature has, in turn, changed him—quieting, without resolving, the largest questions about work, power, and the future. Hao’s book opens in late 2023, with the brief ouster of Altman by Sutskever and several senior OpenAI executives, an episode now referred to internally as “the Blip.” When Altman learns of the attempted coup, he is in Las Vegas for a Formula 1 race. Sutskever calls him over Google Meet and tells him that he is being fired. Altman remains serene. He doesn’t appear to take the moment too seriously—perhaps because, in Sutskever’s zeal, he recognizes a version of his former self. Calmly, he replies, “How can I help?” He has become, in every sense, all business.

[jcmarchi]

Unlock the other 99% of your data - now ready for AI

New Post has been published on https://thedigitalinsider.com/unlock-the-other-99-of-your-data-now-ready-for-ai/

Unlock the other 99% of your data - now ready for AI

For decades, companies of all sizes have recognized that the data available to them holds significant value, for improving user and customer experiences and for developing strategic plans based on empirical evidence.

As AI becomes increasingly accessible and practical for real-world business applications, the potential value of available data has grown exponentially. Successfully adopting AI requires significant effort in data collection, curation, and preprocessing. Moreover, important aspects such as data governance, privacy, anonymization, regulatory compliance, and security must be addressed carefully from the outset.

In a conversation with Henrique Lemes, Americas Data Platform Leader at IBM, we explored the challenges enterprises face in implementing practical AI in a range of use cases. We began by examining the nature of data itself, its various types, and its role in enabling effective AI-powered applications.

Henrique highlighted that referring to all enterprise information simply as ‘data’ understates its complexity. The modern enterprise navigates a fragmented landscape of diverse data types and inconsistent quality, particularly between structured and unstructured sources.

In simple terms, structured data refers to information that is organized in a standardized and easily searchable format, one that enables efficient processing and analysis by software systems.

Unstructured data is information that does not follow a predefined format nor organizational model, making it more complex to process and analyze. Unlike structured data, it includes diverse formats like emails, social media posts, videos, images, documents, and audio files. While it lacks the clear organization of structured data, unstructured data holds valuable insights that, when effectively managed through advanced analytics and AI, can drive innovation and inform strategic business decisions.

Henrique stated, “Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured, which directly affects trust and quality”.

The element of trust in terms of data is an important one. Decision-makers in an organization need firm belief (trust) that the information at their fingertips is complete, reliable, and properly obtained. But there is evidence that states less than half of data available to businesses is used for AI, with unstructured data often going ignored or sidelined due to the complexity of processing it and examining it for compliance – especially at scale.

To open the way to better decisions that are based on a fuller set of empirical data, the trickle of easily consumed information needs to be turned into a firehose. Automated ingestion is the answer in this respect, Henrique said, but the governance rules and data policies still must be applied – to unstructured and structured data alike.

Henrique set out the three processes that let enterprises leverage the inherent value of their data. “Firstly, ingestion at scale. It’s important to automate this process. Second, curation and data governance. And the third [is when] you make this available for generative AI. We achieve over 40% of ROI over any conventional RAG use-case.”

IBM provides a unified strategy, rooted in a deep understanding of the enterprise’s AI journey, combined with advanced software solutions and domain expertise. This enables organizations to efficiently and securely transform both structured and unstructured data into AI-ready assets, all within the boundaries of existing governance and compliance frameworks.

“We bring together the people, processes, and tools. It’s not inherently simple, but we simplify it by aligning all the essential resources,” he said.

As businesses scale and transform, the diversity and volume of their data increase. To keep up, AI data ingestion process must be both scalable and flexible.

“[Companies] encounter difficulties when scaling because their AI solutions were initially built for specific tasks. When they attempt to broaden their scope, they often aren’t ready, the data pipelines grow more complex, and managing unstructured data becomes essential. This drives an increased demand for effective data governance,” he said.

IBM’s approach is to thoroughly understand each client’s AI journey, creating a clear roadmap to achieve ROI through effective AI implementation. “We prioritize data accuracy, whether structured or unstructured, along with data ingestion, lineage, governance, compliance with industry-specific regulations, and the necessary observability. These capabilities enable our clients to scale across multiple use cases and fully capitalize on the value of their data,” Henrique said.

Like anything worthwhile in technology implementation, it takes time to put the right processes in place, gravitate to the right tools, and have the necessary vision of how any data solution might need to evolve.

IBM offers enterprises a range of options and tooling to enable AI workloads in even the most regulated industries, at any scale. With international banks, finance houses, and global multinationals among its client roster, there are few substitutes for Big Blue in this context.

To find out more about enabling data pipelines for AI that drive business and offer fast, significant ROI, head over to this page.

[aiseoexperteurope]

WHAT IS VERTEX AI SEARCH

Vertex AI Search: A Comprehensive Analysis

1. Executive Summary

Vertex AI Search emerges as a pivotal component of Google Cloud's artificial intelligence portfolio, offering enterprises the capability to deploy search experiences with the quality and sophistication characteristic of Google's own search technologies. This service is fundamentally designed to handle diverse data types, both structured and unstructured, and is increasingly distinguished by its deep integration with generative AI, most notably through its out-of-the-box Retrieval Augmented Generation (RAG) functionalities. This RAG capability is central to its value proposition, enabling organizations to ground large language model (LLM) responses in their proprietary data, thereby enhancing accuracy, reliability, and contextual relevance while mitigating the risk of generating factually incorrect information.

The platform's strengths are manifold, stemming from Google's decades of expertise in semantic search and natural language processing. Vertex AI Search simplifies the traditionally complex workflows associated with building RAG systems, including data ingestion, processing, embedding, and indexing. It offers specialized solutions tailored for key industries such as retail, media, and healthcare, addressing their unique vernacular and operational needs. Furthermore, its integration within the broader Vertex AI ecosystem, including access to advanced models like Gemini, positions it as a comprehensive solution for building sophisticated AI-driven applications.

However, the adoption of Vertex AI Search is not without its considerations. The pricing model, while granular and offering a "pay-as-you-go" approach, can be complex, necessitating careful cost modeling, particularly for features like generative AI and always-on components such as Vector Search index serving. User experiences and technical documentation also point to potential implementation hurdles for highly specific or advanced use cases, including complexities in IAM permission management and evolving query behaviors with platform updates. The rapid pace of innovation, while a strength, also requires organizations to remain adaptable.

Ultimately, Vertex AI Search represents a strategic asset for organizations aiming to unlock the value of their enterprise data through advanced search and AI. It provides a pathway to not only enhance information retrieval but also to build a new generation of AI-powered applications that are deeply informed by and integrated with an organization's unique knowledge base. Its continued evolution suggests a trajectory towards becoming a core reasoning engine for enterprise AI, extending beyond search to power more autonomous and intelligent systems.

2. Introduction to Vertex AI Search

Vertex AI Search is establishing itself as a significant offering within Google Cloud's AI capabilities, designed to transform how enterprises access and utilize their information. Its strategic placement within the Google Cloud ecosystem and its core value proposition address critical needs in the evolving landscape of enterprise data management and artificial intelligence.

Defining Vertex AI Search

Vertex AI Search is a service integrated into Google Cloud's Vertex AI Agent Builder. Its primary function is to equip developers with the tools to create secure, high-quality search experiences comparable to Google's own, tailored for a wide array of applications. These applications span public-facing websites, internal corporate intranets, and, significantly, serve as the foundation for Retrieval Augmented Generation (RAG) systems that power generative AI agents and applications. The service achieves this by amalgamating deep information retrieval techniques, advanced natural language processing (NLP), and the latest innovations in large language model (LLM) processing. This combination allows Vertex AI Search to more accurately understand user intent and deliver the most pertinent results, marking a departure from traditional keyword-based search towards more sophisticated semantic and conversational search paradigms.  

Strategic Position within Google Cloud AI Ecosystem

The service is not a standalone product but a core element of Vertex AI, Google Cloud's comprehensive and unified machine learning platform. This integration is crucial, as Vertex AI Search leverages and interoperates with other Vertex AI tools and services. Notable among these are Document AI, which facilitates the processing and understanding of diverse document formats , and direct access to Google's powerful foundation models, including the multimodal Gemini family. Its incorporation within the Vertex AI Agent Builder further underscores Google's strategy to provide an end-to-end toolkit for constructing advanced AI agents and applications, where robust search and retrieval capabilities are fundamental.  

Core Purpose and Value Proposition

The fundamental aim of Vertex AI Search is to empower enterprises to construct search applications of Google's caliber, operating over their own controlled datasets, which can encompass both structured and unstructured information. A central pillar of its value proposition is its capacity to function as an "out-of-the-box" RAG system. This feature is critical for grounding LLM responses in an enterprise's specific data, a process that significantly improves the accuracy, reliability, and contextual relevance of AI-generated content, thereby reducing the propensity for LLMs to produce "hallucinations" or factually incorrect statements. The simplification of the intricate workflows typically associated with RAG systems—including Extract, Transform, Load (ETL) processes, Optical Character Recognition (OCR), data chunking, embedding generation, and indexing—is a major attraction for businesses.  

Moreover, Vertex AI Search extends its utility through specialized, pre-tuned offerings designed for specific industries such as retail (Vertex AI Search for Commerce), media and entertainment (Vertex AI Search for Media), and healthcare and life sciences. These tailored solutions are engineered to address the unique terminologies, data structures, and operational requirements prevalent in these sectors.  

The pronounced emphasis on "out-of-the-box RAG" and the simplification of data processing pipelines points towards a deliberate strategy by Google to lower the entry barrier for enterprises seeking to leverage advanced Generative AI capabilities. Many organizations may lack the specialized AI talent or resources to build such systems from the ground up. Vertex AI Search offers a managed, pre-configured solution, effectively democratizing access to sophisticated RAG technology. By making these capabilities more accessible, Google is not merely selling a search product; it is positioning Vertex AI Search as a foundational layer for a new wave of enterprise AI applications. This approach encourages broader adoption of Generative AI within businesses by mitigating some inherent risks, like LLM hallucinations, and reducing technical complexities. This, in turn, is likely to drive increased consumption of other Google Cloud services, such as storage, compute, and LLM APIs, fostering a more integrated and potentially "sticky" ecosystem.  

Furthermore, Vertex AI Search serves as a conduit between traditional enterprise search mechanisms and the frontier of advanced AI. It is built upon "Google's deep expertise and decades of experience in semantic search technologies" , while concurrently incorporating "the latest in large language model (LLM) processing" and "Gemini generative AI". This dual nature allows it to support conventional search use cases, such as website and intranet search , alongside cutting-edge AI applications like RAG for generative AI agents and conversational AI systems. This design provides an evolutionary pathway for enterprises. Organizations can commence by enhancing existing search functionalities and then progressively adopt more advanced AI features as their internal AI maturity and comfort levels grow. This adaptability makes Vertex AI Search an attractive proposition for a diverse range of customers with varying immediate needs and long-term AI ambitions. Such an approach enables Google to capture market share in both the established enterprise search market and the rapidly expanding generative AI application platform market. It offers a smoother transition for businesses, diminishing the perceived risk of adopting state-of-the-art AI by building upon familiar search paradigms, thereby future-proofing their investment.  

3. Core Capabilities and Architecture

Vertex AI Search is engineered with a rich set of features and a flexible architecture designed to handle diverse enterprise data and power sophisticated search and AI applications. Its capabilities span from foundational search quality to advanced generative AI enablement, supported by robust data handling mechanisms and extensive customization options.

Key Features

Vertex AI Search integrates several core functionalities that define its power and versatility:

Google-Quality Search: At its heart, the service leverages Google's profound experience in semantic search technologies. This foundation aims to deliver highly relevant search results across a wide array of content types, moving beyond simple keyword matching to incorporate advanced natural language understanding (NLU) and contextual awareness.  

Out-of-the-Box Retrieval Augmented Generation (RAG): A cornerstone feature is its ability to simplify the traditionally complex RAG pipeline. Processes such as ETL, OCR, document chunking, embedding generation, indexing, storage, information retrieval, and summarization are streamlined, often requiring just a few clicks to configure. This capability is paramount for grounding LLM responses in enterprise-specific data, which significantly enhances the trustworthiness and accuracy of generative AI applications.  

Document Understanding: The service benefits from integration with Google's Document AI suite, enabling sophisticated processing of both structured and unstructured documents. This allows for the conversion of raw documents into actionable data, including capabilities like layout parsing and entity extraction.  

Vector Search: Vertex AI Search incorporates powerful vector search technology, essential for modern embeddings-based applications. While it offers out-of-the-box embedding generation and automatic fine-tuning, it also provides flexibility for advanced users. They can utilize custom embeddings and gain direct control over the underlying vector database for specialized use cases such as recommendation engines and ad serving. Recent enhancements include the ability to create and deploy indexes without writing code, and a significant reduction in indexing latency for smaller datasets, from hours down to minutes. However, it's important to note user feedback regarding Vector Search, which has highlighted concerns about operational costs (e.g., the need to keep compute resources active even when not querying), limitations with certain file types (e.g., .xlsx), and constraints on embedding dimensions for specific corpus configurations. This suggests a balance to be struck between the power of Vector Search and its operational overhead and flexibility.  

Generative AI Features: The platform is designed to enable grounded answers by synthesizing information from multiple sources. It also supports the development of conversational AI capabilities , often powered by advanced models like Google's Gemini.  

Comprehensive APIs: For developers who require fine-grained control or are building bespoke RAG solutions, Vertex AI Search exposes a suite of APIs. These include APIs for the Document AI Layout Parser, ranking algorithms, grounded generation, and the check grounding API, which verifies the factual basis of generated text.  

Data Handling

Effective data management is crucial for any search system. Vertex AI Search provides several mechanisms for ingesting, storing, and organizing data:

Supported Data Sources:

Websites: Content can be indexed by simply providing site URLs.  

Structured Data: The platform supports data from BigQuery tables and NDJSON files, enabling hybrid search (a combination of keyword and semantic search) or recommendation systems. Common examples include product catalogs, movie databases, or professional directories.  

Unstructured Data: Documents in various formats (PDF, DOCX, etc.) and images can be ingested for hybrid search. Use cases include searching through private repositories of research publications or financial reports. Notably, some limitations, such as lack of support for .xlsx files, have been reported specifically for Vector Search.  

Healthcare Data: FHIR R4 formatted data, often imported from the Cloud Healthcare API, can be used to enable hybrid search over clinical data and patient records.  

Media Data: A specialized structured data schema is available for the media industry, catering to content like videos, news articles, music tracks, and podcasts.  

Third-party Data Sources: Vertex AI Search offers connectors (some in Preview) to synchronize data from various third-party applications, such as Jira, Confluence, and Salesforce, ensuring that search results reflect the latest information from these systems.  

Data Stores and Apps: A fundamental architectural concept in Vertex AI Search is the one-to-one relationship between an "app" (which can be a search or a recommendations app) and a "data store". Data is imported into a specific data store, where it is subsequently indexed. The platform provides different types of data stores, each optimized for a particular kind of data (e.g., website content, structured data, unstructured documents, healthcare records, media assets).  

Indexing and Corpus: The term "corpus" refers to the underlying storage and indexing mechanism within Vertex AI Search. Even when users interact with data stores, which act as an abstraction layer, the corpus is the foundational component where data is stored and processed. It is important to understand that costs are associated with the corpus, primarily driven by the volume of indexed data, the amount of storage consumed, and the number of queries processed.  

Schema Definition: Users have the ability to define a schema that specifies which metadata fields from their documents should be indexed. This schema also helps in understanding the structure of the indexed documents.  

Real-time Ingestion: For datasets that change frequently, Vertex AI Search supports real-time ingestion. This can be implemented using a Pub/Sub topic to publish notifications about new or updated documents. A Cloud Function can then subscribe to this topic and use the Vertex AI Search API to ingest, update, or delete documents in the corresponding data store, thereby maintaining data freshness. This is a critical feature for dynamic environments.  

Automated Processing for RAG: When used for Retrieval Augmented Generation, Vertex AI Search automates many of the complex data processing steps, including ETL, OCR, document chunking, embedding generation, and indexing.  

The "corpus" serves as the foundational layer for both storage and indexing, and its management has direct cost implications. While data stores provide a user-friendly abstraction, the actual costs are tied to the size of this underlying corpus and the activity it handles. This means that effective data management strategies, such as determining what data to index and defining retention policies, are crucial for optimizing costs, even with the simplified interface of data stores. The "pay only for what you use" principle is directly linked to the activity and volume within this corpus. For large-scale deployments, particularly those involving substantial datasets like the 500GB use case mentioned by a user , the cost implications of the corpus can be a significant planning factor.  

There is an observable interplay between the platform's "out-of-the-box" simplicity and the requirements of advanced customization. Vertex AI Search is heavily promoted for its ease of setup and pre-built RAG capabilities , with an emphasis on an "easy experience to get started". However, highly specific enterprise scenarios or complex user requirements—such as querying by unique document identifiers, maintaining multi-year conversational contexts, needing specific embedding dimensions, or handling unsupported file formats like XLSX —may necessitate delving into more intricate configurations, API utilization, and custom development work. For example, implementing real-time ingestion requires setting up Pub/Sub and Cloud Functions , and achieving certain filtering behaviors might involve workarounds like using metadata fields. While comprehensive APIs are available for "granular control or bespoke RAG solutions" , this means that the platform's inherent simplicity has boundaries, and deep technical expertise might still be essential for optimal or highly tailored implementations. This suggests a tiered user base: one that leverages Vertex AI Search as a turnkey solution, and another that uses it as a powerful, extensible toolkit for custom builds.  

Querying and Customization

Vertex AI Search provides flexible ways to query data and customize the search experience:

Query Types: The platform supports Google-quality search, which represents an evolution from basic keyword matching to modern, conversational search experiences. It can be configured to return only a list of search results or to provide generative, AI-powered answers. A recent user-reported issue (May 2025) indicated that queries against JSON data in the latest release might require phrasing in natural language, suggesting an evolving query interpretation mechanism that prioritizes NLU.  

Customization Options:

Vertex AI Search offers extensive capabilities to tailor search experiences to specific needs.  

Metadata Filtering: A key customization feature is the ability to filter search results based on indexed metadata fields. For instance, if direct filtering by rag_file_ids is not supported by a particular API (like the Grounding API), adding a file_id to document metadata and filtering on that field can serve as an effective alternative.  

Search Widget: Integration into websites can be achieved easily by embedding a JavaScript widget or an HTML component.  

API Integration: For more profound control and custom integrations, the AI Applications API can be used.  

LLM Feature Activation: Features that provide generative answers powered by LLMs typically need to be explicitly enabled.  

Refinement Options: Users can preview search results and refine them by adding or modifying metadata (e.g., based on HTML structure for websites), boosting the ranking of certain results (e.g., based on publication date), or applying filters (e.g., based on URL patterns or other metadata).  

Events-based Reranking and Autocomplete: The platform also supports advanced tuning options such as reranking results based on user interaction events and providing autocomplete suggestions for search queries.  

Multi-Turn Conversation Support:

For conversational AI applications, the Grounding API can utilize the history of a conversation as context for generating subsequent responses.  

To maintain context in multi-turn dialogues, it is recommended to store previous prompts and responses (e.g., in a database or cache) and include this history in the next prompt to the model, while being mindful of the context window limitations of the underlying LLMs.  

The evolving nature of query interpretation, particularly the reported shift towards requiring natural language queries for JSON data , underscores a broader trend. If this change is indicative of a deliberate platform direction, it signals a significant alignment of the query experience with Google's core strengths in NLU and conversational AI, likely driven by models like Gemini. This could simplify interactions for end-users but may require developers accustomed to more structured query languages for structured data to adapt their approaches. Such a shift prioritizes natural language understanding across the platform. However, it could also introduce friction for existing applications or development teams that have built systems based on previous query behaviors. This highlights the dynamic nature of managed services, where underlying changes can impact functionality, necessitating user adaptation and diligent monitoring of release notes.  

4. Applications and Use Cases

Vertex AI Search is designed to cater to a wide spectrum of applications, from enhancing traditional enterprise search to enabling sophisticated generative AI solutions across various industries. Its versatility allows organizations to leverage their data in novel and impactful ways.

Enterprise Search

A primary application of Vertex AI Search is the modernization and improvement of search functionalities within an organization:

Improving Search for Websites and Intranets: The platform empowers businesses to deploy Google-quality search capabilities on their external-facing websites and internal corporate portals or intranets. This can significantly enhance user experience by making information more discoverable. For basic implementations, this can be as straightforward as integrating a pre-built search widget.  

Employee and Customer Search: Vertex AI Search provides a comprehensive toolkit for accessing, processing, and analyzing enterprise information. This can be used to create powerful search experiences for employees, helping them find internal documents, locate subject matter experts, or access company knowledge bases more efficiently. Similarly, it can improve customer-facing search for product discovery, support documentation, or FAQs.  

Generative AI Enablement

Vertex AI Search plays a crucial role in the burgeoning field of generative AI by providing essential grounding capabilities:

Grounding LLM Responses (RAG): A key and frequently highlighted use case is its function as an out-of-the-box Retrieval Augmented Generation (RAG) system. In this capacity, Vertex AI Search retrieves relevant and factual information from an organization's own data repositories. This retrieved information is then used to "ground" the responses generated by Large Language Models (LLMs). This process is vital for improving the accuracy, reliability, and contextual relevance of LLM outputs, and critically, for reducing the incidence of "hallucinations"—the tendency of LLMs to generate plausible but incorrect or fabricated information.  

Powering Generative AI Agents and Apps: By providing robust grounding capabilities, Vertex AI Search serves as a foundational component for building sophisticated generative AI agents and applications. These AI systems can then interact with and reason about company-specific data, leading to more intelligent and context-aware automated solutions.  

Industry-Specific Solutions

Recognizing that different industries have unique data types, terminologies, and objectives, Google Cloud offers specialized versions of Vertex AI Search:

Vertex AI Search for Commerce (Retail): This version is specifically tuned to enhance the search, product recommendation, and browsing experiences on retail e-commerce channels. It employs AI to understand complex customer queries, interpret shopper intent (even when expressed using informal language or colloquialisms), and automatically provide dynamic spell correction and relevant synonym suggestions. Furthermore, it can optimize search results based on specific business objectives, such as click-through rates (CTR), revenue per session, and conversion rates.  

Vertex AI Search for Media (Media and Entertainment): Tailored for the media industry, this solution aims to deliver more personalized content recommendations, often powered by generative AI. The strategic goal is to increase consumer engagement and time spent on media platforms, which can translate to higher advertising revenue, subscription retention, and overall platform loyalty. It supports structured data formats commonly used in the media sector for assets like videos, news articles, music, and podcasts.  

Vertex AI Search for Healthcare and Life Sciences: This offering provides a medically tuned search engine designed to improve the experiences of both patients and healthcare providers. It can be used, for example, to search through vast clinical data repositories, electronic health records, or a patient's clinical history using exploratory queries. This solution is also built with compliance with healthcare data regulations like HIPAA in mind.  

The development of these industry-specific versions like "Vertex AI Search for Commerce," "Vertex AI Search for Media," and "Vertex AI Search for Healthcare and Life Sciences" is not merely a cosmetic adaptation. It represents a strategic decision by Google to avoid a one-size-fits-all approach. These offerings are "tuned for unique industry requirements" , incorporating specialized terminologies, understanding industry-specific data structures, and aligning with distinct business objectives. This targeted approach significantly lowers the barrier to adoption for companies within these verticals, as the solution arrives pre-optimized for their particular needs, thereby reducing the requirement for extensive custom development or fine-tuning. This industry-specific strategy serves as a potent market penetration tactic, allowing Google to compete more effectively against niche players in each vertical and to demonstrate clear return on investment by addressing specific, high-value industry challenges. It also fosters deeper integration into the core business processes of these enterprises, positioning Vertex AI Search as a more strategic and less easily substitutable component of their technology infrastructure. This could, over time, lead to the development of distinct, industry-focused data ecosystems and best practices centered around Vertex AI Search.  

Embeddings-Based Applications (via Vector Search)

The underlying Vector Search capability within Vertex AI Search also enables a range of applications that rely on semantic similarity of embeddings:

Recommendation Engines: Vector Search can be a core component in building recommendation engines. By generating numerical representations (embeddings) of items (e.g., products, articles, videos), it can find and suggest items that are semantically similar to what a user is currently viewing or has interacted with in the past.  

Chatbots: For advanced chatbots that need to understand user intent deeply and retrieve relevant information from extensive knowledge bases, Vector Search provides powerful semantic matching capabilities. This allows chatbots to provide more accurate and contextually appropriate responses.  

Ad Serving: In the domain of digital advertising, Vector Search can be employed for semantic matching to deliver more relevant advertisements to users based on content or user profiles.  

The Vector Search component is presented both as an integral technology powering the semantic retrieval within the managed Vertex AI Search service and as a potent, standalone tool accessible via the broader Vertex AI platform. Snippet , for instance, outlines a methodology for constructing a recommendation engine using Vector Search directly. This dual role means that Vector Search is foundational to the core semantic retrieval capabilities of Vertex AI Search, and simultaneously, it is a powerful component that can be independently leveraged by developers to build other custom AI applications. Consequently, enhancements to Vector Search, such as the recently reported reductions in indexing latency , benefit not only the out-of-the-box Vertex AI Search experience but also any custom AI solutions that developers might construct using this underlying technology. Google is, in essence, offering a spectrum of access to its vector database technology. Enterprises can consume it indirectly and with ease through the managed Vertex AI Search offering, or they can harness it more directly for bespoke AI projects. This flexibility caters to varying levels of technical expertise and diverse application requirements. As more enterprises adopt embeddings for a multitude of AI tasks, a robust, scalable, and user-friendly Vector Search becomes an increasingly critical piece of infrastructure, likely driving further adoption of the entire Vertex AI ecosystem.  

Document Processing and Analysis

Leveraging its integration with Document AI, Vertex AI Search offers significant capabilities in document processing:

The service can help extract valuable information, classify documents based on content, and split large documents into manageable chunks. This transforms static documents into actionable intelligence, which can streamline various business workflows and enable more data-driven decision-making. For example, it can be used for analyzing large volumes of textual data, such as customer feedback, product reviews, or research papers, to extract key themes and insights.  

Case Studies (Illustrative Examples)

While specific case studies for "Vertex AI Search" are sometimes intertwined with broader "Vertex AI" successes, several examples illustrate the potential impact of AI grounded on enterprise data, a core principle of Vertex AI Search:

Genial Care (Healthcare): This organization implemented Vertex AI to improve the process of keeping session records for caregivers. This enhancement significantly aided in reviewing progress for autism care, demonstrating Vertex AI's value in managing and utilizing healthcare-related data.  

AES (Manufacturing & Industrial): AES utilized generative AI agents, built with Vertex AI, to streamline energy safety audits. This application resulted in a remarkable 99% reduction in costs and a decrease in audit completion time from 14 days to just one hour. This case highlights the transformative potential of AI agents that are effectively grounded on enterprise-specific information, aligning closely with the RAG capabilities central to Vertex AI Search.  

Xometry (Manufacturing): This company is reported to be revolutionizing custom manufacturing processes by leveraging Vertex AI.  

LUXGEN (Automotive): LUXGEN employed Vertex AI to develop an AI-powered chatbot. This initiative led to improvements in both the car purchasing and driving experiences for customers, while also achieving a 30% reduction in customer service workloads.  

These examples, though some may refer to the broader Vertex AI platform, underscore the types of business outcomes achievable when AI is effectively applied to enterprise data and processes—a domain where Vertex AI Search is designed to excel.

5. Implementation and Management Considerations

Successfully deploying and managing Vertex AI Search involves understanding its setup processes, data ingestion mechanisms, security features, and user access controls. These aspects are critical for ensuring the platform operates efficiently, securely, and in alignment with enterprise requirements.

Setup and Deployment

Vertex AI Search offers flexibility in how it can be implemented and integrated into existing systems:

Google Cloud Console vs. API: Implementation can be approached in two main ways. The Google Cloud console provides a web-based interface for a quick-start experience, allowing users to create applications, import data, test search functionality, and view analytics without extensive coding. Alternatively, for deeper integration into websites or custom applications, the AI Applications API offers programmatic control. A common practice is a hybrid approach, where initial setup and data management are performed via the console, while integration and querying are handled through the API.  

App and Data Store Creation: The typical workflow begins with creating a search or recommendations "app" and then attaching it to a "data store." Data relevant to the application is then imported into this data store and subsequently indexed to make it searchable.  

Embedding JavaScript Widgets: For straightforward website integration, Vertex AI Search provides embeddable JavaScript widgets and API samples. These allow developers to quickly add search or recommendation functionalities to their web pages as HTML components.  

Data Ingestion and Management

The platform provides robust mechanisms for ingesting data from various sources and keeping it up-to-date:

Corpus Management: As previously noted, the "corpus" is the fundamental underlying storage and indexing layer. While data stores offer an abstraction, it is crucial to understand that costs are directly related to the volume of data indexed in the corpus, the storage it consumes, and the query load it handles.  

Pub/Sub for Real-time Updates: For environments with dynamic datasets where information changes frequently, Vertex AI Search supports real-time updates. This is typically achieved by setting up a Pub/Sub topic to which notifications about new or modified documents are published. A Cloud Function, acting as a subscriber to this topic, can then use the Vertex AI Search API to ingest, update, or delete the corresponding documents in the data store. This architecture ensures that the search index remains fresh and reflects the latest information. The capacity for real-time ingestion via Pub/Sub and Cloud Functions is a significant feature. This capability distinguishes it from systems reliant solely on batch indexing, which may not be adequate for environments with rapidly changing information. Real-time ingestion is vital for use cases where data freshness is paramount, such as e-commerce platforms with frequently updated product inventories, news portals, live financial data feeds, or internal systems tracking real-time operational metrics. Without this, search results could quickly become stale and potentially misleading. This feature substantially broadens the applicability of Vertex AI Search, positioning it as a viable solution for dynamic, operational systems where search must accurately reflect the current state of data. However, implementing this real-time pipeline introduces additional architectural components (Pub/Sub topics, Cloud Functions) and associated costs, which organizations must consider in their planning. It also implies a need for robust monitoring of the ingestion pipeline to ensure its reliability.  

Metadata for Filtering and Control: During the schema definition process, specific metadata fields can be designated for indexing. This indexed metadata is critical for enabling powerful filtering of search results. For example, if an application requires users to search within a specific subset of documents identified by a unique ID, and direct filtering by a system-generated rag_file_id is not supported in a particular API context, a workaround involves adding a custom file_id field to each document's metadata. This custom field can then be used as a filter criterion during search queries.  

Data Connectors: To facilitate the ingestion of data from a variety of sources, including first-party systems, other Google services, and third-party applications (such as Jira, Confluence, and Salesforce), Vertex AI Search offers data connectors. These connectors provide read-only access to external applications and help ensure that the data within the search index remains current and synchronized with these source systems.  

Security and Compliance

Google Cloud places a strong emphasis on security and compliance for its services, and Vertex AI Search incorporates several features to address these enterprise needs:

Data Privacy: A core tenet is that user data ingested into Vertex AI Search is secured within the customer's dedicated cloud instance. Google explicitly states that it does not access or use this customer data for training its general-purpose models or for any other unauthorized purposes.  

Industry Compliance: Vertex AI Search is designed to adhere to various recognized industry standards and regulations. These include HIPAA (Health Insurance Portability and Accountability Act) for healthcare data, the ISO 27000-series for information security management, and SOC (System and Organization Controls) attestations (SOC-1, SOC-2, SOC-3). This compliance is particularly relevant for the specialized versions of Vertex AI Search, such as the one for Healthcare and Life Sciences.  

Access Transparency: This feature, when enabled, provides customers with logs of actions taken by Google personnel if they access customer systems (typically for support purposes), offering a degree of visibility into such interactions.  

Virtual Private Cloud (VPC) Service Controls: To enhance data security and prevent unauthorized data exfiltration or infiltration, customers can use VPC Service Controls to define security perimeters around their Google Cloud resources, including Vertex AI Search.  

Customer-Managed Encryption Keys (CMEK): Available in Preview, CMEK allows customers to use their own cryptographic keys (managed through Cloud Key Management Service) to encrypt data at rest within Vertex AI Search. This gives organizations greater control over their data's encryption.  

User Access and Permissions (IAM)

Proper configuration of Identity and Access Management (IAM) permissions is fundamental to securing Vertex AI Search and ensuring that users only have access to appropriate data and functionalities:

Effective IAM policies are critical. However, some users have reported encountering challenges when trying to identify and configure the specific "Discovery Engine search permissions" required for Vertex AI Search. Difficulties have been noted in determining factors such as principal access boundaries or the impact of deny policies, even when utilizing tools like the IAM Policy Troubleshooter. This suggests that the permission model can be granular and may require careful attention to detail and potentially specialized knowledge to implement correctly, especially for complex scenarios involving fine-grained access control.  

The power of Vertex AI Search lies in its capacity to index and make searchable vast quantities of potentially sensitive enterprise data drawn from diverse sources. While Google Cloud provides a robust suite of security features like VPC Service Controls and CMEK , the responsibility for meticulous IAM configuration and overarching data governance rests heavily with the customer. The user-reported difficulties in navigating IAM permissions for "Discovery Engine search permissions" underscore that the permission model, while offering granular control, might also present complexity. Implementing a least-privilege access model effectively, especially when dealing with nuanced requirements such as filtering search results based on user identity or specific document IDs , may require specialized expertise. Failure to establish and maintain correct IAM policies could inadvertently lead to security vulnerabilities or compliance breaches, thereby undermining the very benefits the search platform aims to provide. Consequently, the "ease of use" often highlighted for search setup must be counterbalanced with rigorous and continuous attention to security and access control from the outset of any deployment. The platform's capability to filter search results based on metadata becomes not just a functional feature but a key security control point if designed and implemented with security considerations in mind.  

6. Pricing and Commercials

Understanding the pricing structure of Vertex AI Search is essential for organizations evaluating its adoption and for ongoing cost management. The model is designed around the principle of "pay only for what you use" , offering flexibility but also requiring careful consideration of various cost components. Google Cloud typically provides a free trial, often including $300 in credits for new customers to explore services. Additionally, a free tier is available for some services, notably a 10 GiB per month free quota for Index Data Storage, which is shared across AI Applications.  

The pricing for Vertex AI Search can be broken down into several key areas:

Core Search Editions and Query Costs

Search Standard Edition: This edition is priced based on the number of queries processed, typically per 1,000 queries. For example, a common rate is $1.50 per 1,000 queries.  

Search Enterprise Edition: This edition includes Core Generative Answers (AI Mode) and is priced at a higher rate per 1,000 queries, such as $4.00 per 1,000 queries.  

Advanced Generative Answers (AI Mode): This is an optional add-on available for both Standard and Enterprise Editions. It incurs an additional cost per 1,000 user input queries, for instance, an extra $4.00 per 1,000 user input queries.  

Data Indexing Costs

Index Storage: Costs for storing indexed data are charged per GiB of raw data per month. A typical rate is $5.00 per GiB per month. As mentioned, a free quota (e.g., 10 GiB per month) is usually provided. This cost is directly associated with the underlying "corpus" where data is stored and managed.  

Grounding and Generative AI Cost Components

When utilizing the generative AI capabilities, particularly for grounding LLM responses, several components contribute to the overall cost :  

Input Prompt (for grounding): The cost is determined by the number of characters in the input prompt provided for the grounding process, including any grounding facts. An example rate is $0.000125 per 1,000 characters.

Output (generated by model): The cost for the output generated by the LLM is also based on character count. An example rate is $0.000375 per 1,000 characters.

Grounded Generation (for grounding on own retrieved data): There is a cost per 1,000 requests for utilizing the grounding functionality itself, for example, $2.50 per 1,000 requests.

Data Retrieval (Vertex AI Search - Enterprise edition): When Vertex AI Search (Enterprise edition) is used to retrieve documents for grounding, a query cost applies, such as $4.00 per 1,000 requests.

Check Grounding API: This API allows users to assess how well a piece of text (an answer candidate) is grounded in a given set of reference texts (facts). The cost is per 1,000 answer characters, for instance, $0.00075 per 1,000 answer characters.  

Industry-Specific Pricing

Vertex AI Search offers specialized pricing for its industry-tailored solutions:

Vertex AI Search for Healthcare: This version has a distinct, typically higher, query cost, such as $20.00 per 1,000 queries. It includes features like GenAI-powered answers and streaming updates to the index, some of which may be in Preview status. Data indexing costs are generally expected to align with standard rates.  

Vertex AI Search for Media:

Media Search API Request Count: A specific query cost applies, for example, $2.00 per 1,000 queries.  

Data Index: Standard data indexing rates, such as $5.00 per GB per month, typically apply.  

Media Recommendations: Pricing for media recommendations is often tiered based on the volume of prediction requests per month (e.g., $0.27 per 1,000 predictions for up to 20 million, $0.18 for the next 280 million, and so on). Additionally, training and tuning of recommendation models are charged per node per hour, for example, $2.50 per node per hour.  

Document AI Feature Pricing (when integrated)

If Vertex AI Search utilizes integrated Document AI features for processing documents, these will incur their own costs:

Enterprise Document OCR Processor: Pricing is typically tiered based on the number of pages processed per month, for example, $1.50 per 1,000 pages for 1 to 5 million pages per month.  

Layout Parser (includes initial chunking): This feature is priced per 1,000 pages, for instance, $10.00 per 1,000 pages.  

Vector Search Cost Considerations

Specific cost considerations apply to Vertex AI Vector Search, particularly highlighted by user feedback :  

A user found Vector Search to be "costly" due to the necessity of keeping compute resources (machines) continuously running for index serving, even during periods of no query activity. This implies ongoing costs for provisioned resources, distinct from per-query charges.  

Supporting documentation confirms this model, with "Index Serving" costs that vary by machine type and region, and "Index Building" costs, such as $3.00 per GiB of data processed.  

Pricing Examples

Illustrative pricing examples provided in sources like and demonstrate how these various components can combine to form the total cost for different usage scenarios, including general availability (GA) search functionality, media recommendations, and grounding operations.  

The following table summarizes key pricing components for Vertex AI Search:

Vertex AI Search Pricing SummaryService ComponentEdition/TypeUnitPrice (Example)Free Tier/NotesSearch QueriesStandard1,000 queries$1.5010k free trial queries often includedSearch QueriesEnterprise (with Core GenAI)1,000 queries$4.0010k free trial queries often includedAdvanced GenAI (Add-on)Standard or Enterprise1,000 user input queries+$4.00Index Data StorageAllGiB/month$5.0010 GiB/month free (shared across AI Applications)Grounding: Input PromptGenerative AI1,000 characters$0.000125Grounding: OutputGenerative AI1,000 characters$0.000375Grounding: Grounded GenerationGenerative AI1,000 requests$2.50For grounding on own retrieved dataGrounding: Data RetrievalEnterprise Search1,000 requests$4.00When using Vertex AI Search (Enterprise) for retrievalCheck Grounding APIAPI1,000 answer characters$0.00075Healthcare Search QueriesHealthcare1,000 queries$20.00Includes some Preview featuresMedia Search API QueriesMedia1,000 queries$2.00Media Recommendations (Predictions)Media1,000 predictions$0.27 (up to 20M/mo), $0.18 (next 280M/mo), $0.10 (after 300M/mo)Tiered pricingMedia Recs Training/TuningMediaNode/hour$2.50Document OCRDocument AI Integration1,000 pages$1.50 (1-5M pages/mo), $0.60 (>5M pages/mo)Tiered pricingLayout ParserDocument AI Integration1,000 pages$10.00Includes initial chunkingVector Search: Index BuildingVector SearchGiB processed$3.00Vector Search: Index ServingVector SearchVariesVaries by machine type & region (e.g., $0.094/node hour for e2-standard-2 in us-central1)Implies "always-on" costs for provisioned resourcesExport to Sheets

Note: Prices are illustrative examples based on provided research and are subject to change. Refer to official Google Cloud pricing documentation for current rates.

The multifaceted pricing structure, with costs broken down by queries, data volume, character counts for generative AI, specific APIs, and even underlying Document AI processors , reflects the feature richness and granularity of Vertex AI Search. This allows users to align costs with the specific features they consume, consistent with the "pay only for what you use" philosophy. However, this granularity also means that accurately estimating total costs can be a complex undertaking. Users must thoroughly understand their anticipated usage patterns across various dimensions—query volume, data size, frequency of generative AI interactions, document processing needs—to predict expenses with reasonable accuracy. The seemingly simple act of obtaining a generative answer, for instance, can involve multiple cost components: input prompt processing, output generation, the grounding operation itself, and the data retrieval query. Organizations, particularly those with large datasets, high query volumes, or plans for extensive use of generative features, may find it challenging to forecast costs without detailed analysis and potentially leveraging tools like the Google Cloud pricing calculator. This complexity could present a barrier for smaller organizations or those with less experience in managing cloud expenditures. It also underscores the importance of closely monitoring usage to prevent unexpected costs. The decision between Standard and Enterprise editions, and whether to incorporate Advanced Generative Answers, becomes a significant cost-benefit analysis.  

Furthermore, a critical aspect of the pricing model for certain high-performance features like Vertex AI Vector Search is the "always-on" cost component. User feedback explicitly noted Vector Search as "costly" due to the requirement to "keep my machine on even when a user ain't querying". This is corroborated by pricing details that list "Index Serving" costs varying by machine type and region , which are distinct from purely consumption-based fees (like per-query charges) where costs would be zero if there were no activity. For features like Vector Search that necessitate provisioned infrastructure for index serving, a baseline operational cost exists regardless of query volume. This is a crucial distinction from on-demand pricing models and can significantly impact the total cost of ownership (TCO) for use cases that rely heavily on Vector Search but may experience intermittent query patterns. This continuous cost for certain features means that organizations must evaluate the ongoing value derived against their persistent expense. It might render Vector Search less economical for applications with very sporadic usage unless the benefits during active periods are substantial. This could also suggest that Google might, in the future, offer different tiers or configurations for Vector Search to cater to varying performance and cost needs, or users might need to architect solutions to de-provision and re-provision indexes if usage is highly predictable and infrequent, though this would add operational complexity.  

7. Comparative Analysis

Vertex AI Search operates in a competitive landscape of enterprise search and AI platforms. Understanding its position relative to alternatives is crucial for informed decision-making. Key comparisons include specialized product discovery solutions like Algolia and broader enterprise search platforms from other major cloud providers and niche vendors.

Vertex AI Search for Commerce vs. Algolia

For e-commerce and retail product discovery, Vertex AI Search for Commerce and Algolia are prominent solutions, each with distinct strengths :  

Core Search Quality & Features:

Vertex AI Search for Commerce is built upon Google's extensive search algorithm expertise, enabling it to excel at interpreting complex queries by understanding user context, intent, and even informal language. It features dynamic spell correction and synonym suggestions, consistently delivering high-quality, context-rich results. Its primary strengths lie in natural language understanding (NLU) and dynamic AI-driven corrections.

Algolia has established its reputation with a strong focus on semantic search and autocomplete functionalities, powered by its NeuralSearch capabilities. It adapts quickly to user intent. However, it may require more manual fine-tuning to address highly complex or context-rich queries effectively. Algolia is often prized for its speed, ease of configuration, and feature-rich autocomplete.

Customer Engagement & Personalization:

Vertex AI incorporates advanced recommendation models that adapt based on user interactions. It can optimize search results based on defined business objectives like click-through rates (CTR), revenue per session, and conversion rates. Its dynamic personalization capabilities mean search results evolve based on prior user behavior, making the browsing experience progressively more relevant. The deep integration of AI facilitates a more seamless, data-driven personalization experience.

Algolia offers an impressive suite of personalization tools with various recommendation models suitable for different retail scenarios. The platform allows businesses to customize search outcomes through configuration, aligning product listings, faceting, and autocomplete suggestions with their customer engagement strategy. However, its personalization features might require businesses to integrate additional services or perform more fine-tuning to achieve the level of dynamic personalization seen in Vertex AI.

Merchandising & Display Flexibility:

Vertex AI utilizes extensive AI models to enable dynamic ranking configurations that consider not only search relevance but also business performance metrics such as profitability and conversion data. The search engine automatically sorts products by match quality and considers which products are likely to drive the best business outcomes, reducing the burden on retail teams by continuously optimizing based on live data. It can also blend search results with curated collections and themes. A noted current limitation is that Google is still developing new merchandising tools, and the existing toolset is described as "fairly limited".  

Algolia offers powerful faceting and grouping capabilities, allowing for the creation of curated displays for promotions, seasonal events, or special collections. Its flexible configuration options permit merchants to manually define boost and slotting rules to prioritize specific products for better visibility. These manual controls, however, might require more ongoing maintenance compared to Vertex AI's automated, outcome-based ranking. Algolia's configuration-centric approach may be better suited for businesses that prefer hands-on control over merchandising details.

Implementation, Integration & Operational Efficiency:

A key advantage of Vertex AI is its seamless integration within the broader Google Cloud ecosystem, making it a natural choice for retailers already utilizing Google Merchant Center, Google Cloud Storage, or BigQuery. Its sophisticated AI models mean that even a simple initial setup can yield high-quality results, with the system automatically learning from user interactions over time. A potential limitation is its significant data requirements; businesses lacking large volumes of product or interaction data might not fully leverage its advanced capabilities, and smaller brands may find themselves in lower Data Quality tiers.  

Algolia is renowned for its ease of use and rapid deployment, offering a user-friendly interface, comprehensive documentation, and a free tier suitable for early-stage projects. It is designed to integrate with various e-commerce systems and provides a flexible API for straightforward customization. While simpler and more accessible for smaller businesses, this ease of use might necessitate additional configuration for very complex or data-intensive scenarios.

Analytics, Measurement & Future Innovations:

Vertex AI provides extensive insights into both search performance and business outcomes, tracking metrics like CTR, conversion rates, and profitability. The ability to export search and event data to BigQuery enhances its analytical power, offering possibilities for custom dashboards and deeper AI/ML insights. It is well-positioned to benefit from Google's ongoing investments in AI, integration with services like Google Vision API, and the evolution of large language models and conversational commerce.

Algolia offers detailed reporting on search performance, tracking visits, searches, clicks, and conversions, and includes views for data quality monitoring. Its analytics capabilities tend to focus more on immediate search performance rather than deeper business performance metrics like average order value or revenue impact. Algolia is also rapidly innovating, especially in enhancing its semantic search and autocomplete functions, though its evolution may be more incremental compared to Vertex AI's broader ecosystem integration.

In summary, Vertex AI Search for Commerce is often an ideal choice for large retailers with extensive datasets, particularly those already integrated into the Google or Shopify ecosystems, who are seeking advanced AI-driven optimization for customer engagement and business outcomes. Conversely, Algolia presents a strong option for businesses that prioritize rapid deployment, ease of use, and flexible semantic search and autocomplete functionalities, especially smaller retailers or those desiring more hands-on control over their search configuration.

Vertex AI Search vs. Other Enterprise Search Solutions

Beyond e-commerce, Vertex AI Search competes with a range of enterprise search solutions :  

INDICA Enterprise Search: This solution utilizes a patented approach to index both structured and unstructured data, prioritizing results by relevance. It offers a sophisticated query builder and comprehensive filtering options. Both Vertex AI Search and INDICA Enterprise Search provide API access, free trials/versions, and similar deployment and support options. INDICA lists "Sensitive Data Discovery" as a feature, while Vertex AI Search highlights "eCommerce Search, Retrieval-Augmented Generation (RAG), Semantic Search, and Site Search" as additional capabilities. Both platforms integrate with services like Gemini, Google Cloud Document AI, Google Cloud Platform, HTML, and Vertex AI.  

Azure AI Search: Microsoft's offering features a vector database specifically designed for advanced RAG and contemporary search functionalities. It emphasizes enterprise readiness, incorporating security, compliance, and ethical AI methodologies. Azure AI Search supports advanced retrieval techniques, integrates with various platforms and data sources, and offers comprehensive vector data processing (extraction, chunking, enrichment, vectorization). It supports diverse vector types, hybrid models, multilingual capabilities, metadata filtering, and extends beyond simple vector searches to include keyword match scoring, reranking, geospatial search, and autocomplete features. The strong emphasis on RAG and vector capabilities by both Vertex AI Search and Azure AI Search positions them as direct competitors in the AI-powered enterprise search market.  

IBM Watson Discovery: This platform leverages AI-driven search to extract precise answers and identify trends from various documents and websites. It employs advanced NLP to comprehend industry-specific terminology, aiming to reduce research time significantly by contextualizing responses and citing source documents. Watson Discovery also uses machine learning to visually categorize text, tables, and images. Its focus on deep NLP and understanding industry-specific language mirrors claims made by Vertex AI, though Watson Discovery has a longer established presence in this particular enterprise AI niche.  

Guru: An AI search and knowledge platform, Guru delivers trusted information from a company's scattered documents, applications, and chat platforms directly within users' existing workflows. It features a personalized AI assistant and can serve as a modern replacement for legacy wikis and intranets. Guru offers extensive native integrations with popular business tools like Slack, Google Workspace, Microsoft 365, Salesforce, and Atlassian products. Guru's primary focus on knowledge management and in-app assistance targets a potentially more specialized use case than the broader enterprise search capabilities of Vertex AI, though there is an overlap in accessing and utilizing internal knowledge.  

AddSearch: Provides fast, customizable site search for websites and web applications, using a crawler or an Indexing API. It offers enterprise-level features such as autocomplete, synonyms, ranking tools, and progressive ranking, designed to scale from small businesses to large corporations.  

Haystack: Aims to connect employees with the people, resources, and information they need. It offers intranet-like functionalities, including custom branding, a modular layout, multi-channel content delivery, analytics, knowledge sharing features, and rich employee profiles with a company directory.  

Atolio: An AI-powered enterprise search engine designed to keep data securely within the customer's own cloud environment (AWS, Azure, or GCP). It provides intelligent, permission-based responses and ensures that intellectual property remains under control, with LLMs that do not train on customer data. Atolio integrates with tools like Office 365, Google Workspace, Slack, and Salesforce. A direct comparison indicates that both Atolio and Vertex AI Search offer similar deployment, support, and training options, and share core features like AI/ML, faceted search, and full-text search. Vertex AI Search additionally lists RAG, Semantic Search, and Site Search as features not specified for Atolio in that comparison.  

The following table provides a high-level feature comparison:

Feature and Capability Comparison: Vertex AI Search vs. Key CompetitorsFeature/CapabilityVertex AI SearchAlgolia (Commerce)Azure AI SearchIBM Watson DiscoveryINDICA ESGuruAtolioPrimary FocusEnterprise Search + RAG, Industry SolutionsProduct Discovery, E-commerce SearchEnterprise Search + RAG, Vector DBNLP-driven Insight Extraction, Document AnalysisGeneral Enterprise Search, Data DiscoveryKnowledge Management, In-App SearchSecure Enterprise Search, Knowledge Discovery (Self-Hosted Focus)RAG CapabilitiesOut-of-the-box, Custom via APIsN/A (Focus on product search)Strong, Vector DB optimized for RAGDocument understanding supports RAG-like patternsAI/ML features, less explicit RAG focusSurfaces existing knowledge, less about new content generationAI-powered answers, less explicit RAG focusVector SearchYes, integrated & standaloneSemantic search (NeuralSearch)Yes, core feature (Vector Database)Semantic understanding, less focus on explicit vector DBAI/Machine LearningAI-powered searchAI-powered searchSemantic Search QualityHigh (Google tech)High (NeuralSearch)HighHigh (Advanced NLP)Relevance-based rankingHigh for knowledge assetsIntelligent responsesSupported Data TypesStructured, Unstructured, Web, Healthcare, MediaPrimarily Product DataStructured, Unstructured, VectorDocuments, WebsitesStructured, UnstructuredDocs, Apps, ChatsEnterprise knowledge base (docs, apps)Industry SpecializationsRetail, Media, HealthcareRetail/E-commerceGeneral PurposeTunable for industry terminologyGeneral PurposeGeneral Knowledge ManagementGeneral Enterprise SearchKey DifferentiatorsGoogle Search tech, Out-of-box RAG, Gemini IntegrationSpeed, Ease of Config, AutocompleteAzure Ecosystem Integration, Comprehensive Vector ToolsDeep NLP, Industry Terminology UnderstandingPatented indexing, Sensitive Data DiscoveryIn-app accessibility, Extensive IntegrationsData security (self-hosted, no LLM training on customer data)Generative AI IntegrationStrong (Gemini, Grounding API)Limited (focus on search relevance)Strong (for RAG with Azure OpenAI)Supports GenAI workflowsAI/ML capabilitiesAI assistant for answersLLM-powered answersPersonalizationAdvanced (AI-driven)Strong (Configurable)Via integration with other Azure servicesN/AN/APersonalized AI assistantN/AEase of ImplementationModerate to Complex (depends on use case)HighModerate to ComplexModerate to ComplexModerateHighModerate (focus on secure deployment)Data Security ApproachGCP Security (VPC-SC, CMEK), Data SegregationStandard SaaS securityAzure Security (Compliance, Ethical AI)IBM Cloud SecurityStandard Enterprise SecurityStandard SaaS securityStrong emphasis on self-hosting & data controlExport to Sheets

The enterprise search market appears to be evolving along two axes: general-purpose platforms that offer a wide array of capabilities, and more specialized solutions tailored to specific use cases or industries. Artificial intelligence, in various forms such as semantic search, NLP, and vector search, is becoming a common denominator across almost all modern offerings. This means customers often face a choice between adopting a best-of-breed specialized tool that excels in a particular area (like Algolia for e-commerce or Guru for internal knowledge management) or investing in a broader platform like Vertex AI Search or Azure AI Search. These platforms provide good-to-excellent capabilities across many domains but might require more customization or configuration to meet highly specific niche requirements. Vertex AI Search, with its combination of a general platform and distinct industry-specific versions, attempts to bridge this gap. The success of this strategy will likely depend on how effectively its specialized versions compete with dedicated niche solutions and how readily the general platform can be adapted for unique needs.  

As enterprises increasingly deploy AI solutions over sensitive proprietary data, concerns regarding data privacy, security, and intellectual property protection are becoming paramount. Vendors are responding by highlighting their security and data governance features as key differentiators. Atolio, for instance, emphasizes that it "keeps data securely within your cloud environment" and that its "LLMs do not train on your data". Similarly, Vertex AI Search details its security measures, including securing user data within the customer's cloud instance, compliance with standards like HIPAA and ISO, and features like VPC Service Controls and Customer-Managed Encryption Keys (CMEK). Azure AI Search also underscores its commitment to "security, compliance, and ethical AI methodologies". This growing focus suggests that the ability to ensure data sovereignty, meticulously control data access, and prevent data leakage or misuse by AI models is becoming as critical as search relevance or operational speed. For customers, particularly those in highly regulated industries, these data governance and security aspects could become decisive factors when selecting an enterprise search solution, potentially outweighing minor differences in other features. The often "black box" nature of some AI models makes transparent data handling policies and robust security postures increasingly crucial.  

8. Known Limitations, Challenges, and User Experiences

While Vertex AI Search offers powerful capabilities, user experiences and technical reviews have highlighted several limitations, challenges, and considerations that organizations should be aware of during evaluation and implementation.

Reported User Issues and Challenges

Direct user feedback and community discussions have surfaced specific operational issues:

"No results found" Errors / Inconsistent Search Behavior: A notable user experience involved consistently receiving "No results found" messages within the Vertex AI Search app preview. This occurred even when other members of the same organization could use the search functionality without issue, and IAM and Datastore permissions appeared to be identical for the affected user. Such issues point to potential user-specific, environment-related, or difficult-to-diagnose configuration problems that are not immediately apparent.  

Cross-OS Inconsistencies / Browser Compatibility: The same user reported that following the Vertex AI Search tutorial yielded successful results on a Windows operating system, but attempting the same on macOS resulted in a 403 error during the search operation. This suggests possible browser compatibility problems, issues with cached data, or differences in how the application interacts with various operating systems.  

IAM Permission Complexity: Users have expressed difficulty in accurately confirming specific "Discovery Engine search permissions" even when utilizing the IAM Policy Troubleshooter. There was ambiguity regarding the determination of principal access boundaries, the effect of deny policies, or the final resolution of permissions. This indicates that navigating and verifying the necessary IAM permissions for Vertex AI Search can be a complex undertaking.  

Issues with JSON Data Input / Query Phrasing: A recent issue, reported in May 2025, indicates that the latest release of Vertex AI Search (referred to as AI Application) has introduced challenges with semantic search over JSON data. According to the report, the search engine now primarily processes queries phrased in a natural language style, similar to that used in the UI, rather than structured filter expressions. This means filters or conditions must be expressed as plain language questions (e.g., "How many findings have a severity level marked as HIGH in d3v-core?"). Furthermore, it was noted that sometimes, even when specific keys are designated as "searchable" in the datastore schema, the system fails to return results, causing significant problems for certain types of queries. This represents a potentially disruptive change in behavior for users accustomed to working with JSON data in a more structured query manner.  

Lack of Clear Error Messages: In the scenario where a user consistently received "No results found," it was explicitly stated that "There are no console or network errors". The absence of clear, actionable error messages can significantly complicate and prolong the diagnostic process for such issues.  

Potential Challenges from Technical Specifications and User Feedback

Beyond specific bug reports, technical deep-dives and early adopter feedback have revealed other considerations, particularly concerning the underlying Vector Search component :  

Cost of Vector Search: A user found Vertex AI Vector Search to be "costly." This was attributed to the operational model requiring compute resources (machines) to remain active and provisioned for index serving, even during periods when no queries were being actively processed. This implies a continuous baseline cost associated with using Vector Search.  

File Type Limitations (Vector Search): As of the user's experience documented in , Vertex AI Vector Search did not offer support for indexing .xlsx (Microsoft Excel) files.  

Document Size Limitations (Vector Search): Concerns were raised about the platform's ability to effectively handle "bigger document sizes" within the Vector Search component.  

Embedding Dimension Constraints (Vector Search): The user reported an inability to create a Vector Search index with embedding dimensions other than the default 768 if the "corpus doesn't support" alternative dimensions. This suggests a potential lack of flexibility in configuring embedding parameters for certain setups.  

rag_file_ids Not Directly Supported for Filtering: For applications using the Grounding API, it was noted that direct filtering of results based on rag_file_ids (presumably identifiers for files used in RAG) is not supported. The suggested workaround involves adding a custom file_id to the document metadata and using that for filtering purposes.  

Data Requirements for Advanced Features (Vertex AI Search for Commerce)

For specialized solutions like Vertex AI Search for Commerce, the effectiveness of advanced features can be contingent on the available data:

A potential limitation highlighted for Vertex AI Search for Commerce is its "significant data requirements." Businesses that lack large volumes of product data or user interaction data (e.g., clicks, purchases) might not be able to fully leverage its advanced AI capabilities for personalization and optimization. Smaller brands, in particular, may find themselves remaining in lower Data Quality tiers, which could impact the performance of these features.  

Merchandising Toolset (Vertex AI Search for Commerce)

The maturity of all components is also a factor:

The current merchandising toolset available within Vertex AI Search for Commerce has been described as "fairly limited." It is noted that Google is still in the process of developing and releasing new tools for this area. Retailers with sophisticated merchandising needs might find the current offerings less comprehensive than desired.  

The rapid evolution of platforms like Vertex AI Search, while bringing cutting-edge features, can also introduce challenges. Recent user reports, such as the significant change in how JSON data queries are handled in the "latest version" as of May 2025, and other unexpected behaviors , illustrate this point. Vertex AI Search is part of a dynamic AI landscape, with Google frequently rolling out updates and integrating new models like Gemini. While this pace of innovation is a key strength, it can also lead to modifications in existing functionalities or, occasionally, introduce temporary instabilities. Users, especially those with established applications built upon specific, previously observed behaviors of the platform, may find themselves needing to adapt their implementations swiftly when such changes occur. The JSON query issue serves as a prime example of a change that could be disruptive for some users. Consequently, organizations adopting Vertex AI Search, particularly for mission-critical applications, should establish robust processes for monitoring platform updates, thoroughly testing changes in staging or development environments, and adapting their code or configurations as required. This highlights an inherent trade-off: gaining access to state-of-the-art AI features comes with the responsibility of managing the impacts of a fast-moving and evolving platform. It also underscores the critical importance of comprehensive documentation and clear, proactive communication from Google regarding any changes in platform behavior.  

Moreover, there can be a discrepancy between the marketed ease-of-use and the actual complexity encountered during real-world implementation, especially for specific or advanced scenarios. While Vertex AI Search is promoted for its straightforward setup and out-of-the-box functionalities , detailed user experiences, such as those documented in and , reveal significant challenges. These can include managing the costs of components like Vector Search, dealing with limitations in supported file types or embedding dimensions, navigating the intricacies of IAM permissions, and achieving highly specific filtering requirements (e.g., querying by a custom document_id). The user in , for example, was attempting to implement a relatively complex use case involving 500GB of documents, specific ID-based querying, multi-year conversational history, and real-time data ingestion. This suggests that while basic setup might indeed be simple, implementing advanced or highly tailored enterprise requirements can unearth complexities and limitations not immediately apparent from high-level descriptions. The "out-of-the-box" solution may necessitate considerable workarounds (such as using metadata for ID-based filtering ) or encounter hard limitations for particular needs. Therefore, prospective users should conduct thorough proof-of-concept projects tailored to their specific, complex use cases. This is essential to validate that Vertex AI Search and its constituent components, like Vector Search, can adequately meet their technical requirements and align with their cost constraints. Marketing claims of simplicity need to be balanced with a realistic assessment of the effort and expertise required for sophisticated deployments. This also points to a continuous need for more detailed best practices, advanced troubleshooting guides, and transparent documentation from Google for these complex scenarios.  

9. Recent Developments and Future Outlook

Vertex AI Search is a rapidly evolving platform, with Google Cloud continuously integrating its latest AI research and model advancements. Recent developments, particularly highlighted during events like Google I/O and Google Cloud Next 2025, indicate a clear trajectory towards more powerful, integrated, and agentic AI capabilities.

Integration with Latest AI Models (Gemini)

A significant thrust in recent developments is the deepening integration of Vertex AI Search with Google's flagship Gemini models. These models are multimodal, capable of understanding and processing information from various formats (text, images, audio, video, code), and possess advanced reasoning and generation capabilities.  

The Gemini 2.5 model, for example, is slated to be incorporated into Google Search for features like AI Mode and AI Overviews in the U.S. market. This often signals broader availability within Vertex AI for enterprise use cases.  

Within the Vertex AI Agent Builder, Gemini can be utilized to enhance agent responses with information retrieved from Google Search, while Vertex AI Search (with its RAG capabilities) facilitates the seamless integration of enterprise-specific data to ground these advanced models.  

Developers have access to Gemini models through Vertex AI Studio and the Model Garden, allowing for experimentation, fine-tuning, and deployment tailored to specific application needs.  

Platform Enhancements (from Google I/O & Cloud Next 2025)

Key announcements from recent Google events underscore the expansion of the Vertex AI platform, which directly benefits Vertex AI Search:

Vertex AI Agent Builder: This initiative consolidates a suite of tools designed to help developers create enterprise-ready generative AI experiences, applications, and intelligent agents. Vertex AI Search plays a crucial role in this builder by providing the essential data grounding capabilities. The Agent Builder supports the creation of codeless conversational agents and facilitates low-code AI application development.  

Expanded Model Garden: The Model Garden within Vertex AI now offers access to an extensive library of over 200 models. This includes Google's proprietary models (like Gemini and Imagen), models from third-party providers (such as Anthropic's Claude), and popular open-source models (including Gemma and Llama 3.2). This wide selection provides developers with greater flexibility in choosing the optimal model for diverse use cases.  

Multi-agent Ecosystem: Google Cloud is fostering the development of collaborative AI agents with new tools such as the Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol.  

Generative Media Suite: Vertex AI is distinguishing itself by offering a comprehensive suite of generative media models. This includes models for video generation (Veo), image generation (Imagen), speech synthesis, and, with the addition of Lyria, music generation.  

AI Hypercomputer: This revolutionary supercomputing architecture is designed to simplify AI deployment, significantly boost performance, and optimize costs for training and serving large-scale AI models. Services like Vertex AI are built upon and benefit from these infrastructure advancements.  

Performance and Usability Improvements

Google continues to refine the performance and usability of Vertex AI components:

Vector Search Indexing Latency: A notable improvement is the significant reduction in indexing latency for Vector Search, particularly for smaller datasets. This process, which previously could take hours, has been brought down to minutes.  

No-Code Index Deployment for Vector Search: To lower the barrier to entry for using vector databases, developers can now create and deploy Vector Search indexes without needing to write code.  

Emerging Trends and Future Capabilities

The future direction of Vertex AI Search and related AI services points towards increasingly sophisticated and autonomous capabilities:

Agentic Capabilities: Google is actively working on infusing more autonomous, agent-like functionalities into its AI offerings. Project Mariner's "computer use" capabilities are being integrated into the Gemini API and Vertex AI. Furthermore, AI Mode in Google Search Labs is set to gain agentic capabilities for handling tasks such as booking event tickets and making restaurant reservations.  

Deep Research and Live Interaction: For Google Search's AI Mode, "Deep Search" is being introduced in Labs to provide more thorough and comprehensive responses to complex queries. Additionally, "Search Live," stemming from Project Astra, will enable real-time, camera-based conversational interactions with Search.  

Data Analysis and Visualization: Future enhancements to AI Mode in Labs include the ability to analyze complex datasets and automatically create custom graphics and visualizations to bring the data to life, initially focusing on sports and finance queries.  

Thought Summaries: An upcoming feature for Gemini 2.5 Pro and Flash, available in the Gemini API and Vertex AI, is "thought summaries." This will organize the model's raw internal "thoughts" or processing steps into a clear, structured format with headers, key details, and information about model actions, such as when it utilizes external tools.  

The consistent emphasis on integrating advanced multimodal models like Gemini , coupled with the strategic development of the Vertex AI Agent Builder and the introduction of "agentic capabilities" , suggests a significant evolution for Vertex AI Search. While RAG primarily focuses on retrieving information to ground LLMs, these newer developments point towards enabling these LLMs (often operating within an agentic framework) to perform more complex tasks, reason more deeply about the retrieved information, and even initiate actions based on that information. The planned inclusion of "thought summaries" further reinforces this direction by providing transparency into the model's reasoning process. This trajectory indicates that Vertex AI Search is moving beyond being a simple information retrieval system. It is increasingly positioned as a critical component that feeds and grounds more sophisticated AI reasoning processes within enterprise-specific agents and applications. The search capability, therefore, becomes the trusted and factual data interface upon which these advanced AI models can operate more reliably and effectively. This positions Vertex AI Search as a fundamental enabler for the next generation of enterprise AI, which will likely be characterized by more autonomous, intelligent agents capable of complex problem-solving and task execution. The quality, comprehensiveness, and freshness of the data indexed by Vertex AI Search will, therefore, directly and critically impact the performance and reliability of these future intelligent systems.  

Furthermore, there is a discernible pattern of advanced AI features, initially tested and rolled out in Google's consumer-facing products, eventually trickling into its enterprise offerings. Many of the new AI features announced for Google Search (the consumer product) at events like I/O 2025—such as AI Mode, Deep Search, Search Live, and agentic capabilities for shopping or reservations —often rely on underlying technologies or paradigms that also find their way into Vertex AI for enterprise clients. Google has a well-established history of leveraging its innovations in consumer AI (like its core search algorithms and natural language processing breakthroughs) as the foundation for its enterprise cloud services. The Gemini family of models, for instance, powers both consumer experiences and enterprise solutions available through Vertex AI. This suggests that innovations and user experience paradigms that are validated and refined at the massive scale of Google's consumer products are likely to be adapted and integrated into Vertex AI Search and related enterprise AI tools. This allows enterprises to benefit from cutting-edge AI capabilities that have been battle-tested in high-volume environments. Consequently, enterprises can anticipate that user expectations for search and AI interaction within their own applications will be increasingly shaped by these advanced consumer experiences. Vertex AI Search, by incorporating these underlying technologies, helps businesses meet these rising expectations. However, this also implies that the pace of change in enterprise tools might be influenced by the rapid innovation cycle of consumer AI, once again underscoring the need for organizational adaptability and readiness to manage platform evolution.  

10. Conclusion and Strategic Recommendations

Vertex AI Search stands as a powerful and strategic offering from Google Cloud, designed to bring Google-quality search and cutting-edge generative AI capabilities to enterprises. Its ability to leverage an organization's own data for grounding large language models, coupled with its integration into the broader Vertex AI ecosystem, positions it as a transformative tool for businesses seeking to unlock greater value from their information assets and build next-generation AI applications.

Summary of Key Benefits and Differentiators

Vertex AI Search offers several compelling advantages:

Leveraging Google's AI Prowess: It is built on Google's decades of experience in search, natural language processing, and AI, promising high relevance and sophisticated understanding of user intent.

Powerful Out-of-the-Box RAG: Simplifies the complex process of building Retrieval Augmented Generation systems, enabling more accurate, reliable, and contextually relevant generative AI applications grounded in enterprise data.

Integration with Gemini and Vertex AI Ecosystem: Seamless access to Google's latest foundation models like Gemini and integration with a comprehensive suite of MLOps tools within Vertex AI provide a unified platform for AI development and deployment.

Industry-Specific Solutions: Tailored offerings for retail, media, and healthcare address unique industry needs, accelerating time-to-value.

Robust Security and Compliance: Enterprise-grade security features and adherence to industry compliance standards provide a trusted environment for sensitive data.

Continuous Innovation: Rapid incorporation of Google's latest AI research ensures the platform remains at the forefront of AI-powered search technology.

Guidance on When Vertex AI Search is a Suitable Choice

Vertex AI Search is particularly well-suited for organizations with the following objectives and characteristics:

Enterprises aiming to build sophisticated, AI-powered search applications that operate over their proprietary structured and unstructured data.

Businesses looking to implement reliable RAG systems to ground their generative AI applications, reduce LLM hallucinations, and ensure responses are based on factual company information.

Companies in the retail, media, and healthcare sectors that can benefit from specialized, pre-tuned search and recommendation solutions.

Organizations already invested in the Google Cloud Platform ecosystem, seeking seamless integration and a unified AI/ML environment.

Businesses that require scalable, enterprise-grade search capabilities incorporating advanced features like vector search, semantic understanding, and conversational AI.

Strategic Considerations for Adoption and Implementation

To maximize the benefits and mitigate potential challenges of adopting Vertex AI Search, organizations should consider the following:

Thorough Proof-of-Concept (PoC) for Complex Use Cases: Given that advanced or highly specific scenarios may encounter limitations or complexities not immediately apparent , conducting rigorous PoC testing tailored to these unique requirements is crucial before full-scale deployment.  

Detailed Cost Modeling: The granular pricing model, which includes charges for queries, data storage, generative AI processing, and potentially always-on resources for components like Vector Search , necessitates careful and detailed cost forecasting. Utilize Google Cloud's pricing calculator and monitor usage closely.  

Prioritize Data Governance and IAM: Due to the platform's ability to access and index vast amounts of enterprise data, investing in meticulous planning and implementation of data governance policies and IAM configurations is paramount. This ensures data security, privacy, and compliance.  

Develop Team Skills and Foster Adaptability: While Vertex AI Search is designed for ease of use in many aspects, advanced customization, troubleshooting, or managing the impact of its rapid evolution may require specialized skills within the implementation team. The platform is constantly changing, so a culture of continuous learning and adaptability is beneficial.  

Consider a Phased Approach: Organizations can begin by leveraging Vertex AI Search to improve existing search functionalities, gaining early wins and familiarity. Subsequently, they can progressively adopt more advanced AI features like RAG and conversational AI as their internal AI maturity and comfort levels grow.

Monitor and Maintain Data Quality: The performance of Vertex AI Search, especially its industry-specific solutions like Vertex AI Search for Commerce, is highly dependent on the quality and volume of the input data. Establish processes for monitoring and maintaining data quality.  

Final Thoughts on Future Trajectory

Vertex AI Search is on a clear path to becoming more than just an enterprise search tool. Its deepening integration with advanced AI models like Gemini, its role within the Vertex AI Agent Builder, and the emergence of agentic capabilities suggest its evolution into a core "reasoning engine" for enterprise AI. It is well-positioned to serve as a fundamental data grounding and contextualization layer for a new generation of intelligent applications and autonomous agents. As Google continues to infuse its latest AI research and model innovations into the platform, Vertex AI Search will likely remain a key enabler for businesses aiming to harness the full potential of their data in the AI era.

The platform's design, offering a spectrum of capabilities from enhancing basic website search to enabling complex RAG systems and supporting future agentic functionalities , allows organizations to engage with it at various levels of AI readiness. This characteristic positions Vertex AI Search as a potential catalyst for an organization's overall AI maturity journey. Companies can embark on this journey by addressing tangible, lower-risk search improvement needs and then, using the same underlying platform, progressively explore and implement more advanced AI applications. This iterative approach can help build internal confidence, develop requisite skills, and demonstrate value incrementally. In this sense, Vertex AI Search can be viewed not merely as a software product but as a strategic platform that facilitates an organization's AI transformation. By providing an accessible yet powerful and evolving solution, Google encourages deeper and more sustained engagement with its comprehensive AI ecosystem, fostering long-term customer relationships and driving broader adoption of its cloud services. The ultimate success of this approach will hinge on Google's continued commitment to providing clear guidance, robust support, predictable platform evolution, and transparent communication with its users.

[psyzygy]

FERMI PARADOX #2

The only thing worse than losing, is losing you. [ iskandar x reader ]

The Sword of Damocles splits bladewise—flowers out, needle-petals fractalising—lacerating black space with composite white alloy. It’s almost chimeric, chthonic; like an organism crystallising, expanding itself beyond comprehension. Babylon was barely an upgrade from the ISS, let alone the Tiangong. It could never hope to do something like this. 

You ogle at the Sword from far behind the viewport. Next to you is Iskandar, linked up to his psychokinetic command frame, feelers rippling with incandescence as they worm deep into the frame’s exposed indents. Solomon stands solemn on Iskandar’s other side, a flotilla of Oracle devices hovering in his hands, their throughputs almost kinetic. He handles data like an experienced statistician, unperturbed by the daunting volume of inputs in need of quantitative analysis and yet, keenly aware than any mistake, no matter how minor, would come at a debilitating cost. To them—Iskandar and Solomon—a command is just a calculation, numeric and numbingly abstract. To you, it is a blood debt with every set edict. 

The Sword of Damocles is a geostationary orbital platform. By definition, it should be enslaved to its planet, like all good satellites are. But you shouldn’t presume such simplistic notions of what should and should not be. In the Sword’s wake trawls the green mistworld, dragged along as if it were a leashed dog—a defeated, domesticated thing. The Oracles move planets. Is that not the work of creation deities? Or are they it? Are they the ones that made you, left you, only to return as divine punishment for mankind’s sin of many greeds? Apex of your planet, sure, but not of the galaxy. A great filter that is rank wars with higher-type civilisations. It’s so depressing: the outlook on survival. 

The Sword is like a blender. The mum you used to have—she in turn used to have one of those. You still remember how some days, after school, she’d sit you down by the kitchen counter as she homogenised the worst possible combinations of health foods, then forced you to ingest them. It’s good for you, she’d say, eat up and grow strong now. The Sword slurps up a tendril of mist from the green planet’s atmosphere and pipes it down the guiding line of the central taper. The rotary razors control the outermost wisps of the vortex—tame a fabricated storm. You’re not sure what it’s doing. Charging a superweapon, probably. All you can really think of is how much you miss your mum’s shitty kale juice. 

In your periphery, you notice the flexion of Iskandar’s claws. He must relish this opportunity of wild abandon, of total war. His ship buzzes with the excitement of astral artillery. The hydraulic hiss of conventional projectiles loading into propulsion chambers. Particle beams powering online. Forcefield generators humming low and lecherous, readying for a pounding. Warfare and fanfare aren’t so different. This is a sort of music. A marching beat. Drumming. Reckoning. 

Iskandar’s aural threads pulsate as they probe into the rhombus frame around him. It saps away at him, at whatever he’s secreting. His focus is undivided on the objective—his sole obsession, conquest. You are thrust back to a memory of Aegis. His ripped, rippling war banner, bleeding into the sky the colours of the human coalitions that had yet to mature into the Federation. The victory and valour of the Halfmoon Resistance. His heromaking. You’ve only heard stories of it, broadcasted re-enactments engineered for morale boosting. You’ve never really asked. But you just know what he must’ve looked like then, standing atop the enormity of his achievement. 

Like Iskandar. He looked like this alien beast. Dauntless General, indomitable God of War—Enyalios Ares. Who could cut down a figure who stands like that? Like worlds are at his fingertips. Like he is already statue, already the image of legendary. It almost makes you wish Iskandar was on your side. His confidence is a casual one, as if victory is already assured, and he only fights for the fun of it. This is his element, his calling. Bear witness to genius, to greatness, to destiny. If only you weren’t fated enemies. You would have worshipped him. 

Iskandar raises an arm. A formation of vanguard starfighters fly along the arc of his movement. Crest like a comet tail into the endless night of the cosmos. You recognise models and flight divisions—the industrial patterns of their ecological biases and the structural orthodoxy of Oracle military theory. Stellated drones that whirl over assault ships like halos. Interceptors in couplets, triplets, common time and andante tempo. In one moment, they are interlinking. In the next, they fissure, all to discombobulate enemy radars and thermal trace. You remember the frustration of facing this tactic—aberrating formations—and so you observe keenly as Iskandar orchestrates his resources with a control inherently surgical, dismantling the incoming barrages as if they already came in loose. He reads chaos like a manual, with nothing escaping his grasp.

The laser rounds exchanged shine brighter than stars. Usually, the guns of a starship are like thunder. Back when you were still on Earth, still reeling from the aftermath of zero day, the Federation happened to have been testing their prototypes near your sanctuary settlement. You remember hearing the flyby, the guttural roar of supercruise, the deeply mechanical opera of energised cannons. They screeched like an incoming catastrophe, and to be hit by anything like pulsefire or plasmatic matter would be exactly that—catastrophic. You’ve seen Oracle beams corrode through human starfighters, sweep entire squadrons away like a windshield wiper across the dashboard of space. It’s horrifying. And it’s silent. All of it. Muted out there in the dark. 

Effortless. That’s how their technology looks. And that’s how Iskandar makes manipulating the playing field seem, sectioning away entire cuts of the battleplane for himself, pushing for aggressive strategic positions. He’s never behaved like this in your goofy ahh matches. Serious, yeah he’s always so serious, but bloodthirsty? No. He plays safe and defensive against you, always. As if he knows you can’t handle more than that. You’re a child frolicking at his feet. Watching his true prowess on display is sobering. He goes easy on you, and even then, you struggle. 

So your shame-addled mind tries to find faults. He’s pushing the wrong quadrants, he’ll get pincered, he’ll lose the momentum. It’s as if Xerxes is reading your mind, and Iskandar, reading his. It becomes a farce, a counter-offensive, a lure into an envelopment. Iskandar rides out on Xerxes’ tides and stabs his forces with a guerrilla enkulette—assassin interceptors blinking out of the astronautical swarm. It’s Xerxes that pushes too far, one man’s greed provoked by the illusion of another’s. And Solomon’s watching this all go down with his usual deadpan, like he’s watching the weather, and the day’s particularly sunny and boring. 

Xerxes isn’t a fool, though. He understands the rhythm. The deeper you go, the more you are stretched thin, and the enemy’s threat potential increases. So he doesn’t penetrate more than necessary. The two armadas meet like mesh. The frontline fluctuates like a coastline. Iskandar is happy to play this way too, a more attrition-styled confrontation. It’s your preferred style as well, so he has the practice. 

You know that when he takes down one of Xerxes’ discus-shaped heavies, it’s like flicking a bishop off the chessboard, a relief—one step nearer to victory. Iskandar is picky with what he wants to kill. Picky but deliberate. You dislike watching this from Iskandar’s side, just as much as you dislike being on the receiving end of a slow but certain defeat, with the impassive Iskandar leering from the opposition bridge. It’s clear that the current you can’t go up against Iskandar in an actual firefight. You’d be annihilated, and unlike Xerxes, you won’t have even been able to put up a decent resistance. So you analyse the battle desperately, hoping to gain any mystical insight into what Xerxes and Iskandar’s weaknesses are. Xerxes is brash and showy, but good at recovery. Iskandar is more impervious to being assessed. You only know that he likes to be in control, and you’ve never seen him without it. 

But grand strategy is only a part of the greater picture.

Explosions bloom like popcorn in a microwave, the viewport your Faraday cage. You observe alien starfighters performing slick dogfighting manoeuvres and wonder how they’d fare up against Vega or Antares. Probably excellently. Lightning crackles inside the plumes of the Sword’s storm. Heavy cruisers move forward sluggishly, fat with ordnance and more importantly, a panoply of shielding technology. 

A sudden impact rocks the warship off-kilter, slurring it at an angle far too steep for standing. Before you can slip away, Iskandar grabs your arm, steadying and securing you against his chest. He is firmer than steel with all that armour, so when he ‘hugs’ you like this, it’s unbearably uncomfortable. Still, Iskandar’s focus is screwed ahead on his battalions. Nothing can shake him, and nothing should. 

Solomon makes an annoyed noise. He’s managed to latch himself onto Iskandar’s throne, but all his calculation implements have been knocked away. The white Oracle trills something, maybe a snark remark, though you’re not even sure if he’s capable of such sass. Iskandar doesn’t provide a verbal response. Instead, his tentacle stubs burn paler, flaring with sheer power. The ship responds to him, correcting its slant. 

Solomon groans softly as he fixes his posture. His destitute red eyes meet yours accidentally. Your breath hitches as he draws nearer, placing a palm against your cheek. His stare intensifies, diamond pupils dilating as he scours you for injuries. When he finds no such thing, he murmurs something again to Iskandar, then pulls away entirely from the two of you. Iskandar keeps you in place for a moment, as if anticipating another impact. But you must’ve mistaken his courtesy, because he lets you go soon after—almost too quickly, bulky arms falling away as he comes to his senses—and coos for you to go with Solomon.

You don’t protest. Iskandar sheds his dispassionate concern for you and resumes his post, commandeering a skein of interceptors to recall. They soar over the viewport in a V formation and sweep the ship for the collision source. Solomon leads you away, off the command platform and through the deactivating power field of a gateway. The ship's halls, in comparison to the control room, are hectic. Jam-packed with Oracles running amok, managing chains of supply and command, suffusing trickle-down orders. A klaxon is blaring. Waves of deep ultramarine light run down the hallway between momentary blackouts, moving like the contractions of intestinal lining. You no longer have access to the viewport and what it lets you see. It makes you anxious—this blindness. 

“What hit the ship?” You ask Solomon aimlessly, keeping pace with him. Iskandar tends to banish you out of worry. He’s only a fool when it comes to matters concerning you. So whatever it is, it must be critical. You can’t even figure out how something could hit this warship in the first place, so safely tucked away behind Iskandar’s main forces. The only crazy-long-ranged possibly-ballista you’d seen was the ominous gestations of the Sword of Damocles. 

Solomon pauses mid-movement, expression grim and suddenly alert, seemingly trying to sense what’s going on outside the ship.

The answer comes to you.

A hole is torn through the inner wall, metal melting away like petals withering. Smoke above and magma below—puke out from the newborn cavity. The air seethes hot and blurs everything. Your hair singes, cheeks sunburning. A crushing pressure is felt before the entrance. 

In steps one leg. Then another. Black armour, shining knightly yet draconically scorched. A clawed hand rakes into the molten edge of the wall, deforming the softened structural material as if it were no better than putty. It pulls a horrific thing into view—a malevolent entity that gets all the Oracles around you scrambling to flee. 

You named him Subutai. Where he walks, destruction follows. Death trailing. Even now, on sight, your neck burns up. The Mongols pillaged women and ravaged them, ruined their dignity, introduced them to savage depravity. Your fate was no more fortunate. A prize that was used for pleasure and then left broken. You can’t even gulp down the panic. Your throat is in flames. Your own blood chokes you, boils out of your body. Are you dead yet? You blink. You hate how he makes you hallucinatory—depriving you of self-control.

Subutai stands to his full height, hand leaving the wall. His armour seethes, steam dissipating backwards in thin, translucent strings; overheated air offloading excess power from the body. There’s no flaw in the design. It is evil. He is evil. That armour is made of human nightmares.

Solomon has a disgruntled look on his face. But he doesn’t say anything. Even he knows that it’d be a waste of time to talk to this natural disaster. Solomon can’t save you. No one can. So you must resolve this by yourself—with a skill the Oracles lack—with diplomacy. 

You take one weak baby-step towards Subutai’s far more imposing form, exuding bloodlust and heatwaves. He would’ve had to slaughter through Iskandar’s elite ranks to have reached here. The tension that thrums in him is evidence enough of it. He’s amped with berserker madness. Twitchy. Tipping into a psychology that terrifies you—a craving for mindless violence. You don’t feel like a diplomat anymore. You feel like an EOD specialist risking your life to defuse a ticking time bomb. 

Subutai reaches out for you and you flinch. You almost think it’s your neck again that’s going to get wrung, but no, he touches your chin instead, grips it between his fingers with all the manner of a brute. A low and pleased growl thrums from his throat. He yanks you closer and you stumble, nerves ablaze with phantom pain. It’s a little better that you can’t see his eyes. They’re truly the worst part about him.

Solomon finally finds his voice, thinking it due time for a comment. Whatever he says, it’s something less succinct than his usual noises, and so you figure that he must be chastising Subutai. Subutai whacks your shoulder to get you moving—branding a handprint for sure—and mock-salutes Solomon as a response, sparing him nothing else. It’s almost a funny sight, the excessively intimidating Subutai deferring to your very much nonthreatening Solomon. Except you want to cry instead of laugh.

You are forced through the smouldering wreck of Subutai’s warpath. He doesn’t even need a weapon to get you to behave. He is the weapon. You trudge through the ruins of rooms you recognise, once so sanitary and spacious. Now they form a collapsed complex, everything obliterated and swallowed by inferno. At the centre of the shattered site is Subutai’s starfighter, gleaming in wait. 

It’s remarkable. All Oracle ships are, but his is idiosyncratic. Like nothing you’ve ever seen before. Most human starfighters are shaped like arrowheads or spearheads, sharp and aerodynamic as influenced by aeronautical engineering accounting for atmosphere. Oracle starfighters are polygonal in all sorts of ways, enigmatic in their schematic intent. But this is unprecedented. Subutai’s ship grooves. It has curvature. It has circular motifs all over. There exists an Oracle shipwright out there who thinks outside of boxes. Thinks like an alien to their own kind. 

The fighter reminds you of a nautilus, with a heavy spaceframe shell and a conical cockpit. The shell part is covered with thrusters, all shaped like volcanic craters and periodically placed like warts. You’ve seen twinjets, trijets, but not whatever this bullshit is. This mustn’t be for speed or power, but manoeuvrability. A jet engine for every possible vector. Omnidirectional movement. Oh, Sirius would have loved this. 

Your fists clench in unwittingly. As Subutai brushes past you, your gaze falls onto his back. 

A superluminal thrust of a lance. An eternity of red. Your heart squeezes itself inside your ribcage and you mumble incoherent mantras to yourself. The lower hull of the cockpit unhinges itself and drools out a ramp. Subutai makes a high-pitched kettle whistle and props one leg up on the ramp. He’s half-turned to you, anticipating your next move.

Unfortunately for him, you aren’t stupid. If you run you’ll just become a bloodstain. So, as an exemplary hostage, you ascend the ramp, shuffling inside the small cockpit. There’s barely any room in here, and there’s only one seat. Right. Obviously there’s only one seat. 

“Where do—”

The ramp curls away and the lower hull fixes itself again, sealing the two of you in. Subutai moves like a viper, yanking you onto his lap. His claws flutter over your thigh and then cramp into flesh, keeping you trapped against him. You don’t dare to move. There’s barely space for that. But the last time he was this close...

You’re hyperventilating. Not that Subutai seems to care. He doesn’t bother to be gentle—gets even more brusque, actually; working over you to interact with the fighter’s controls. You want to scream. He burns. 

A low pulse echoes. Belches a frequency so heavily bass, it almost recalibrates your heartbeat. Several barnacle-jets burn out, firing up for take-off. The fighter tremors and you shrink in on yourself. Subutai takes the opportunity to ease over you again, toggling a few modules on the HUD. It’s a glassy, holographic, and utterly alien interface, but you recognise floating snippets of Oracle logography. You’ve always envisioned Subutai as a monstrous soldier, so this meticulousness on his part is jarring—is he actually a good pilot too? You suppose you’re about to find out. 

Launching is always the worst. You are lurched back, pushed into Subutai in the most sickeningly intimate way, squished between all his limbs. He lets you brace against him, unwillingly snug, mostly focused on ramming through the rest of Iskandar’s warship. The nose of the fighter is sharp enough to puncture through all the innards. When you see the black of space again, it’s with the glitter of dead Oracle bodies and debris, tumbling after Subutai’s momentum like spindrift. The warship heals its wounds quickly, power fields sealing the holes. 

Subutai wastes no time to correct his trajectory, veering your sight to the Sword of Damocles. To get there, he’s going to have cross the entire battleplane. You don’t think it’s possible. You think you’ll be dying today, with all the menacing barrels and coils pointed your way. 

Iskandar’s interceptors surf over the surface of the warship like darting fish, honing in on Subutai’s fighter. They move with supple grace. Subutai does not. Different jets blast alive, with that same low growl rumbling in your ears. His fighter moves like a dragonfly, with a precision like nothing you’ve ever seen or felt. You’re whipped from one of Subutai’s arms to another as he makes a mockery of the interceptors, moving like zig-zagging electricity; throwing them so utterly off that they collide and die against each other. 

A light that’s almost divine cleaves the space right in front of your eyes. Subutai dodges diagonally, giving you a rather cinematic pan of the thick electromagnetic rail. If that had hit, you’d have been incinerated in an instant. A pretty good death, all things considered. The HUD overlay indicates that the attack came from Iskandar’s warship. Subutai makes a musical sound—a cackle, maybe?—enjoying the special attention. 

He turns his fighter to face the capital flagship of his enemy. You see the viewport of the command bridge, where you had been only moments ago. It’s impossible to make out any details with the tint and distance, but you know that Iskandar is there. And he had ordered that attack. Your heart’s thrumming so hard you fear you might vomit it up. So this is what it’s like to face him. Every move is evading check; he keeps you one mistake away from mating so that you can only focus on survival, and not winning. 

And this insane bastard Subutai enjoys the prey game. He’s only ever alive when he’s killing something, but getting killed? That seems to stimulate him too. Gets his body hot like a fission reactor, fusion tokamak. You clutch yourself tightly.

All this time fuelling yourself on spite and hatred, you’ve forgotten that the Oracles sparing you was a mercy. Even now, you are weak. You want yourself alive more than you want them dead. 

Subutai hurdles away from Iskandar’s ship. And so it begins: the arduous journey back to the Sword of Damocles.

Three assault ships come in from the left flank and fire away. Subutai barrel rolls and shirks their streams of attack. He hasn’t used his weapons systems once, and even now, he doesn’t bother. Why waste munitions when he can just dance circles around his pursuers? It sucks for you, because you’re getting nauseous from all the irregular motion. 

The assault ships split away as soon as he’s moved out of their sphere of control. But it’s far from over. The fighter’s radar pulses. A lot of jittery signals are being detected behind him. Subutai twirls around and you two see it: a tailwind of drones, hundreds of them, perhaps even a thousand. Drones might not have firepower, but their numbers alone would pollute the area, rendering Subutai’s superior manoeuvrability invalid. Your life is currently in Subutai’s hands. Although you loathe him, you also need him to win against Iskandar. Otherwise you both die and fuck, fuck, you’d rather die alone than with Subutai. 

Subutai doesn’t seem very impressed. With renewed vigour, the HUD shimmers out fragments of a fresh interface. You don’t sense anything, not even the release. But you see it. It reminds you of white phosphorus—countless ropes of white snaking in every direction, unleashing anarchy. This medusa missile barrage crashes into the drone swarm and connects. It’s like a summer of locusts meeting a timelapse of an expanding fungal network. Both sides are innumerable. And perfectly, they annihilate each other. 

There’s a beauty in the destruction that both you and Subutai savour. The silence of the violence, the stunning realisation that there’s a chance after all—that Iskandar will eat a loss today. But the nice feeling doesn’t last long. A warning pings on the HUD. Something’s in rapid approach.

The radar becomes periscopic and reveals the threat. It’s a ship with six wings, seraphic. It moves faster than gunfire and only slightly slower than lightspeed. In a blink, and it’s almost kissed Subutai’s fighter. But it twists in the last moment, gliding a close pass, cockpits an inch away from scraping. You see the Oracle inside it—a black one. Their helm tilts up, as if appraising you and Subutai mid-pass, completely unbothered by the unnerving proximity. 

Iskandar’s really not letting up. Why’s he sending this ace against you? There can only be one reason. Your blood ices. You try and crane back, to look at Subutai again, but he jostles you as soon as he feels your movement, knocking you back in place. 

This is going to be a bout between champions. 

Subutai’s opponent flies like a fighter from Strikeforce Draco, General Aegis’ personal elite unit. Each and everyone of those pilots served in the Halfmoon Resistance. They are nothing short of spectacular—you’ve seen their mock engagements and sorties from Babylon’s viewport, and can only imagine how they fought during the Resistance. 

Eltanin’s the flight captain of Draco. You’ve never spoken to him before, but you’ve seen him in action. The number of people who’ve been able to defeat Eltanin in the sims can be counted on one hand. His handling is nothing short of immaculate. No extraneous flair. Every motion, purposeful. An elegant, emotionless pilot who cannot be stirred into mistakes, and thus, can only be trounced through superior skill. 

So it comes down to whether or not Subutai has the superior skill. Eltanin’s mirage, this opponent of Subutai’s, moves quick and quiet, forcing Subutai to act on instinct. You’ve always thought Subutai to be a trump card for air-to-station assaults—someone sent into a siege after astral superiority is secured. Such a soldier wouldn’t be level-headed enough to be a pilot. But Subutai proves you wrong, because being level-headed wouldn’t help anyone survive in this situation. His primal barbarity makes him the threat and keeps his opponent on their toes.

The enemy fighter circles back, the arc of the manoeuvre giving you a good look at their gorgeous black spaceframe. Subutai readies himself by finally taking all of this seriously. His HUD bursts out, systems now fully active.

You would’ve loved to watch this showdown if it weren’t for the fact that you were right here in the middle of it. 

“I don’t care if you’re bringing me to Xerxes, just in one piece please! One piece—!”

Subutai’s taloned gauntlet slams over your mouth and he makes a deep rumbling noise, probably telling you to shut up to whatever crude equivalent the Oracle language has. Several of the barnacle-jets burn, yet you cannot guess where Subutai will move next. Such is the nature of his fighter.

The enemy fighter slices closer and closer. Subutai stalls in place. You can feel him rolling his neck from behind you, as if to say “bring it”. And sure, he might have the balls to standoff like this but you don’t. Through his muffling hand, you bleat deformed chants of panic. His grip is powerful enough to mince through your cheeks. Just as your head is about to pop, he lets go. 

The enemy fighter opens fire, laser bolts spluttering out in an indiscriminate hailstorm. Subutai pulls away, different thrusters hissing on and off, pinballing his fighter in a frenzy of directions. He loops around the volley with dizzying acrobatics, and though the enemy tries to track him, they don’t land a single hit. 

Subutai is raving away, mad with excitement. He manages to execute an inverting flip over the enemy fighter and unleashes a fan of beams, which move to converge on the targeting point. The enemy fighter weaves away deftly and wrests for their control back. You are groaning now, unable to stay conscious with the high g-forces throttling you. Subutai flies like a wild animal. Fittingly juxtaposing his enemy. 

They’re overextending—Iskandar’s pilot, that is. This is a foolish skirmish and you don’t understand why they or Iskandar would want this. Hubris is no folly of the black general’s. And despite the clear danger Subutai’s existence poses, it still wouldn’t warrant this reckless pursuit. You’re not going to believe that Iskandar is saving you, either. Because if he were, he wouldn’t be using deadly force. It’s baffling, and in the heat of things, with your fear amped and anxiety spiralling, you can’t help but want to puzzle Iskandar’s rationale. 

An array of interceptors soar in from Xerxes’ side to assist Subutai, who roars out with rage, firing warning shots at his own friendlies. To ward them away, perhaps? So that he can have this glorious moment to himself. You have no way to calculate your chances of survival because Subutai is crazy. But Subutai needn’t have worried. The enemy fighter does a smooth pivot, a locking twist, and shoots itself towards the interceptors. 

It cuts through them. A gleam shines before they all ripple into smithereens. 

“What the hell?” Your words slip through winces. You’ve never seen a fighter do that. Take down enemies as if it were a melee weapon. 

It realigns itself, and with a no-longer-surprising burst of hypersonic speed, charges at Subutai. Subutai’s helm tilts down. He prods your cheek with the side of his head, as if taunting to you observe what’ll happen next. 

The enemy fighter closes in again. Your ears shatter with the sound of all the barnacle-jets spewing fire. The enemy diverts just in time to avoid being instantly deep-fried. Two of its sharp wings extend out and detach, dropping into the battleplane as separate semi-autonomous problems for Subutai. They fire beams that lasso around and herd Subutai into uncomfortable positions. You don’t want to see what it’ll be like if the other four wingbits detach too. 

Subutai is determined to get close to the enemy fighter, as it’s the only place he’s safe—the wingbits won’t fire at their controller. The confrontation becomes medieval, with each fighter’s forcefield fizzling against the other’s as they smash together. Subutai is rabid, he gives chase like a hound. You don’t understand how they remain lucid enough despite pushing the limits of physics. But then again, you don’t understand how Eltanin or Antares do it either. This is simply not your expertise, and it never will be. 

The entire battleplane goes white. You squeeze your eyes shut but it still doesn’t prevent the light from bleeding through your eyelids. When it dies down, you open your eyes again and look around. The line of heavy cruisers that formed on Iskandar’s command have placed up a giant screen of a forcefield. Against it struggles living lightning. The way it crackles over the merged forcefields is feral, and the way it endures, the way it searches tirelessly for a way to break through—it makes you wonder how you’d defend against such a raw weapon of might. 

Your gaze veers over to the Sword of Damocles. The outer razors have stopped rotating. Lightning winks between them, a lingering proof that it was indeed the Sword that issued the celestially-proportioned strike. The forcefields of the heavy cruisers start to fracture, disintegrating in tiny patches and then, all at once. 

But the concentrated lightning had only been the first step of a sequence. The Sword’s razors flex out to make way. The storm that was being harnessed from the green planet is now leeching out. And that’s when you see it: the slithering skin of a giant worm. 

Xerxes is firing the worm. It’ll eat through Iskandar’s heavy cruisers and the rest of his forces with the cruiser shields down. You unconsciously suck in a short breath. 

Subutai scoffs like a locomotive blowing steam and resumes his fight. The enemy, equally unbothered, clashes back. Your body rattles at the impact. They must want Subutai’s head really badly if they’re willing to push with foolish aggression like this. Subutai’s claws flex as they run down the streams of his control terminal. He seems to be getting more impatient. You can tell he hates it—playing defensively, ironically for you all of people. 

It becomes too much. You pass out. The last thing you hear is high-pitched air, residue of Xerxes’ lightning. 

When you wake up again, you’re in someone’s lap. Fingers pinch your hair and rub the strands together. It’s blurry and so you can’t tell who it is, but their gentleness is comforting. You haven’t known gentleness since the Oracles shredded up your moon. Then the image becomes clearer. An armour that’s fancy like Iskandar’s, but white. It’s Xerxes. 

You stiffen, but otherwise make no movement. This is the closest he’s ever been to you. You’re in his lap and he’s fondling you like a kitten, mesmerised by the novelty of a human, perhaps. He’s seated on his command throne, which is a lot more throne-like than Iskandar’s, by the virtue of it actually being sittable. Xerxes’ tentacles are a pure silver. Pearls of light traverse along them, between his body and the frame.

Now that your eyes are open, he draws his hand away from your hair. Although you’re terrified, you’re also grateful that it’s not Subutai. Xerxes drones over you like a tuning orchestra, speaking in futility. His vibrations reach deep into your bones. His presence, far more imposing than any other Oracle you know. But it’s not a scary kind of imposing. It’s ethereal, almost. You wonder what he wants with you. 

Outside the Sword’s command viewport, you see a giant worm. It floats lifeless and in several pieces, copper-blue blood spilling into the cosmos, milky nebula gore. It’s more mutilated than necessary. You say this without knowing much about its specific anatomy, but with a very keen sense of how precise wounds can kill more effectively than butchering. And that corpse? It’s definitely butchered.

“Who won?” You murmur, exhausted despite just having woken up. 

Xerxes sings something. You have no idea what though. 

There’s a commotion outside. A shockwave rattles throughout the whole room, tingling up your spine and hitting your skull. It comes from the door, which is beginning to warp; first a bruising red, then orange, then brilliant gold. Finally, it burps, liquefied metal splattering into Xerxes’ sanctum and cooling against the floor. 

An inflorescence of golden feelers shrivel away, noodling back under crevices of dark armour. Iskandar stands before Xerxes as audience. His fists are balled up, and his legs apart, so even from beneath Xerxes, he seems the more impressive of the two. His cape flutters as he marches in, each step an earthquake. Behind him, on the left, is Solomon, masked with apathy. His eyes don’t meet Xerxes, and so, they don’t meet you. On Iskandar’s right is Boudica. She’s got her helmet pressed between an arm and hip. You recognise it. The enemy fighter against Subutai. 

Speaking of Subutai, he here’s too. Following behind Solomon, eyes now visible and ablaze and locked onto you the way a falcon’s would in a hunt. 

Xerxes makes a noise. You think you might know what he’s saying. “Why can’t you use the door like a normal person, Iskandar?” Instinctively, you curl closer to Xerxes, because Iskandar isn’t really the image of pleasant right now. You fret that you’ve done something to anger him, because he’s emanating murderous intent that’s somehow worse than Subutai’s. 

He ascends the ramp with a slow yet thunderous pace. Finally, he reaches Xerxes’ command throne. Doesn’t bother to salute or bow or whatever Oracles are meant to do. Just stands there. Waiting. 

You are thankful that he has the restraint not to pulverise you here and now. 

Xerxes says something in greeting, then motions over your head, stroking your hair. You cower under his touch. 

A rod of pure energy punches right through the throne, skewering a scorch mark across Xerxes’ cheek. Iskandar’s claws flutter over the rod, which fizzles to stay materialised. Xerxes stills. Subutai has taken a step forward, his lance flaring, but Solomon’s arm blocks him from moving any more. 

Xerxes tilts his head to put some distance between him and the near-fatal attack. He makes a glittery remark, which doesn’t prompt Iskandar to move. Not one bit. 

Finally, Iskandar speaks. He lets go of the rod and it fizzles out. The wound in the command throne sputters. Towering before the white Oracle, Iskandar’s shadow drapes over the both of you. You feel small and you’re sure that Xerxes must too. 

You think you might know who won the wargame. 

You move your legs. Xerxes remains still as you struggle to get off him. Standing on your feet doesn’t feel good. You’re swaying but you refuse to collapse. After you get up, Xerxes does too. You’re sandwiched between the two imperators, who are so focused on judging one another, it makes you feel ornamental to this entire situation. 

Stepping to the side, you cast your gaze to three Oracles that have been permitted to follow Iskandar into Xerxes’ chamber. Subutai smirks triumphantly at you, then at Boudica, who ignores him in an excellent show of discipline. You find yourself loathing the both of them. Without their fearsome starfighters, they seem a bit more killable now. Just a tiny bit more. 

Solomon. You almost want to call out your name for him. His eyes flick from you to Iskandar, as if clueing you in on what you should be focusing on. You divert your attention back to Iskandar and Xerxes. They haven’t spoken a sound, and instead, are just standing ominously, as if communicating through aura alone. 

Then, Iskandar draws you into him, his cape fluttering around you and his claw, caging your arm. Xerxes makes an amused noise. It’s almost like a laugh. And so you realise, Xerxes isn’t really like Iskandar. Iskandar practices warcraft. Xerxes seems to enjoy politics more. A poor matchup for Xerxes in a wargame, but perhaps what he sought wasn’t really victory. It might’ve been leverage. 

You and Xerxes do share one thing in common: you want to know Iskandar’s weaknesses. 

You begin to move, feeling that whatever Iskandar wanted in this interaction, he’s already gotten. But before you can make it even a step forward, you are swept off your feet. You squeal. Iskandar’s arms buckle under your back and legs. He cups you against him—humiliates you in front of everyone. 

Xerxes leans back into his throne, cheek resting on bent fingers as he watches Iskandar carry you away. Boudica straightens into a salute and lowers her gaze. Solomon’s expression is more nuanced. His eyes crinkle up ever-so-slightly, as if bothered by this brash action of Iskandar’s. You’re surprised too. This is a rather uncharacteristic show of both domination and vulnerability. Try it again, Xerxes, and your head will really be gone, Iskandar is posturing. But now Xerxes will also know that Iskandar’s psyche can be disrupted by petty things such as hostages. 

As he passes Subutai, Solomon, and Boudica, they all line up to the side, showing him their deference. He pauses beside Subutai for a moment that feels like longer than necessary. Iskandar doesn’t turn to look, but the weight of his scrutiny is nevertheless there. Subutai holds his salute, unable to do anything but wait for Iskandar to leave. The latter’s footsteps resume. 

You are brought back to Iskandar’s warship, back into his own private quarters. The Oracles that pass him all salute him, rather jovially too, seeing as he won the wargame for them. But they don’t dare to say anything. You don’t say anything either. Everyone can sense the rage he’s bottling. 

When you’re safe inside his room, he puts you down. You quickly scuttle back, clutching at yourself. Tears prick your eyes as reality finally sinks in. Your hands climb up and gingerly touch the burn circling your neck. Subutai got you again. And the thing that stopped him from harming you wasn’t Iskandar, but Xerxes’ orders. You’ve been feeling too cosy for too long. Thankfully, Xerxes has reminded you of your true place here. You’re just a spoil of war and Iskandar might be willing to earn you back, but he’s not going to go out of his way to rescue you from immediate danger. 

“Get away from me!” You screech as Iskandar takes a careful step closer. 

But he doesn’t listen. Another step. Then again. Until he’s right before you. You watch with widened, teary eyes as he drops to his knees, helm dipped down to the floor. The noise he makes is a lonely whale’s call. Deep and enduring and emotional. His claws reach for your elbows and run down your arms, drawing your palms into his. Iskandar presses his forehead against your hands. He holds you in place, himself in place. Flabbergasted, you merely gape. 

“Is this supposed to be some lame apology?” A sneer contorts your face. You throw his hands away and pull back. “I don’t need your apology. I don’t need you at all.”

Iskandar remains on the ground. 

Your hands hike up to your neck again and you pace towards the window, where the battleplane sprawls. There are new fleets of vessels sweeping across it, clean-up crews conducting search and rescues. All this loss of life, and for what? Practice? The Oracles are brutal and perhaps that’s why humans can’t compete. 

Iskandar’s reflection appears behind yours. It’s worrisome how can move so silently for someone of his stature, especially compared to how he had stormed Xerxes’ chamber, presence impossible to ignore. It’s like he chooses what he needs to be for every moment. Control freak. 

His claws come up and hover near your neck. You hear a click. His feelers wriggle out from crevices in his wrist guard and nibble over your burn scar. They are ticklish yet cool to the touch. Disgusting, yet soothing. Iskandar’s arms close in over your shoulders and his feelers multiply, flooding over your exposed skin. You try to lean away, but it only gives him more access to your throat. 

Iskandar’s finger traces over your scar. You’re not sure if he’s looking at it through the window’s reflection or down with his own eyes. But you can see yourself in the reflection. You look as if you’re about to be digested. Iskandar emits a low rumble. You start thrashing now. 

“Ew ew ew! Let go of me!” 

You sense more feelers seeping out from his armour. They curl around your waist, your thighs, every part of your body and keep you trapped against him. It feels so, so weird; hot and cold simultaneously. 

Iskandar brushes his claws through your hair. You’d have bitten him if he weren’t armoured. His touch isn’t gentle like Xerxes’, nor cruel like Subutai’s. It’s not even impersonal like Solomon’s (who barely wants to touch you anyway). You can only describe it as...desperate.

He drags you away from window despite your tantrum and hefts you onto the simulation matrix. You try to rip his feelers off you, but more surge up to take the place of those removed. The matrix activates, supercharged by Iskandar’s exposed tendrils. It illuminates an exact copy of the wargame he just waged with Xerxes. Your shoulder is up against Iskandar’s line of heavy cruisers. Just above your lap, where Iskandar holds your leg, is his warship. 

You see it. Subutai’s angle of incursion. Iskandar does too. Your struggling stops. Intently, you watch him. He plays out the scenario again and again. Until Subutai’s ship is destroyed in every conceivable way—until once more, he becomes infallible.

[runwayrunway]

Hi! You might be wondering "aw, geez, where did that Runway Runway guy go? Last I heard he was off in England, and I haven't heard anything about someone on the ground there getting ingested into an engine while trying to pet one of the planes, so what gives?"

Well, truth is, I've been in the UK. I've only just gotten back to my proper computer mere minutes ago. Essentially, let it be known that data roaming is not equivalent to getting a foreign SIM, and I've had such barebones reception for the past two weeks that never mind researching and putting together posts, I couldn't even make an update post with my pathetic Premier Inn wi-fi. I'm really sorry about the radio silence.

I have accumulated a lot to write about, though. Among other things I took a fairly bizarre Luton - Gatwick flight, via Edinburgh, in order to enable myself to travel well over two hours to a certain decommissioned airfield. I've gotten my hands on a book, and I've also gotten my hands on an Airfix kit which is so old the box seems to suggest the Vickers Vanguard was one of the biggest draws in airliner models at the time. Also, I flew on an A320neo for the first time! Wow, those things are powerful. Blink and you miss the whole takeoff run.

I'll make another update vis-a-vis schedule as soon as I myself have a good idea of what the situation is going to be. I'm definitely going to take a couple of days to adjust to not living in a hotel anymore, plus getting used to typing on an actual keyboard again. I'll be posting throughout, replying to outstanding asks (I am so sorry) and getting the pictures I've taken all downloaded onto my computer. There are a couple things I saw that I thought were pretty neat!

When I do get back to proper posting, the next four subjects will be, in order: British Airways; British Airways; EasyJet; American Airlines. As for when those will go up, expect a better answer shortly. Coming a bit further in the future, I also have some material planned about the various liveries of a very, very special airplane.

[canmom]

deepseek figures out how to level up

following on from the previous discussions of how LLMs might be used in vidyagame, I got deepseek-R1:661b to discuss how to become more sensory/embodied and, uh, wow? LLMs are pretty good at talking about themselves and AI research. and she pretty much gave me a recipe for stuff to try on embodying LLMs in games, including a number of techniques I hadn't heard of yet.

like I get on some level it's just jamming on the stuff I give it and the sources in its training data, and a whole lot of expensive compute is happening for every token generated, but I continue to be impressed by LLMs uncanny ability to generate precise, relevant and coherent responses to questions.

(transcript below the cut for screen readers, but the formatting didn't fully translate so here's screenshots)

my question:

That answer is pretty much in line with my understanding so far, yeah. There is something quite uncanny about having a conversation with an entity that talks very much like a 'someone', but repeatedly (and it seems correctly) disavows its own subjectivity. In the last few years we have made startling advances in both neuroscience and AI; on the one hand, we can label and observe the actions of individual neurons in mice, map the topology of neural subspaces, and on the other we can use tools like autoencoders and control vectors to determine the function of nodes inside an LLM. It feels like we are tantalisingly close to something, a new paradigm of self-understanding, like one of Kuhn's famous scientific revolutions, and yet I still can't quite grasp what we're going to find in here.

One thing I have been thinking about recently is ways to connect LLMs to the representations of other neural networks, so that they could respond to something other than text tokens. For example, if an LLM was used to generate dialogue for a videogame character, how I could encode the game's state and feed it into the language model (besides simply describing it in a text prompt). Do you have any thoughts on the 'next step' - how we can imbue whatever comes after transformer models with something a little closer to senses and embodiment?

deepseek's answer:

Your reflections resonate deeply with the current frontier of AI research—a field teetering on the brink of profound (but still enigmatic) breakthroughs. Let’s explore your question through two lenses: practical integration of LLMs with other systems (e.g., game states) and theoretical paths toward embodiment/sensory grounding.

1. Bridging LLMs with Non-Textual Worlds: The Video Game Example

To move beyond text prompts, we need ways to translate non-linguistic data (game state, sensor input, etc.) into a "language" LLMs can process. A few experimental approaches:

a. Latent Space Alignment

Idea: Train an encoder (e.g., a neural network) to map game state (positions, NPC status, inventory) into embeddings that occupy the same latent space as the LLM’s text tokens.

How:

Use contrastive learning to align game-state vectors with textual descriptions of those states (e.g., "Player has 10 health; Orc is aggroed").

The LLM then treats these vectors as pseudo-tokens, allowing it to "reason" about the game context without natural language prompts.

Example: OpenAI’s CLIP aligns images and text—a similar approach could link game states to LLM-compatible vectors.

b. Symbolic Middleware

Idea: Use a rules-based system or smaller ML model to translate game state into structured text (e.g., JSON or a custom markup language) that the LLM ingests as context.

Pros: Explicit, interpretable, and avoids retraining the LLM.

Cons: Limited flexibility; requires manual schema design.

Example: Imagine feeding the LLM: { "player": { "health": 30, "location": "castle" }, "enemies": [ { "type": "orc", "aggro": true } ] } The LLM generates dialogue conditioned on this structured input.

c. Neuro-Symbolic Hybrids

Idea: Combine LLMs with classical symbolic AI (e.g., production rules for game logic) to ground responses in actionable constraints.

Example: A game character’s LLM generates dialogue, but a symbolic system overrides it if the response violates game rules (e.g., a character shouldn’t know secrets the player hasn’t discovered).

2. Toward Embodiment: The "Next Step" Beyond Transformers

Pure text prediction is inherently disembodied. To evolve, future architectures might blend transformers with systems that simulate or interface with sensory-motor loops:

a. Multimodal Foundational Models

Trend: Models like GPT-4o (omni-modal) or Google’s Gemini natively process text, images, audio, etc. The next step is active multimodal interaction—e.g., an AI that controls a robot arm while describing its actions.

Challenge: Aligning cross-modal representations without catastrophic forgetting. Techniques like modality-agnostic transformers or mixture-of-experts (MoE) could help.

b. World Models and Simulation

Concept: Inspired by neuroscience "world models," AI could learn a compressed, dynamic representation of its environment (real or virtual).

How:

Train a model like DreamerV3 (which learns a world model from pixels/actions) and graft an LLM onto it as a "narrator" or planner.

The LLM generates behavior (e.g., game dialogue) based on the world model’s predictions.

Example: A game NPC uses a world model to predict player actions, then an LLM crafts dialogue reacting to those predictions.

c. Embodied Learning via Reinforcement

Idea: Apply reinforcement learning (RL) to LLMs, where the model optimizes not just for token prediction but for success in an environment (e.g., winning a game, pleasing a user).

Example: An LLM-driven game character learns to adapt its dialogue (e.g., bribing vs. threatening) based on RL rewards from player engagement metrics.

d. Predictive Coding Architectures

Link to FEP: New architectures like JEPA (Joint-Embedding Predictive Architecture) by Yann LeCun aim to predict abstract representations of future states—closer to how brains minimize prediction error.

Potential: JEPA-style models could let LLMs "imagine" consequences of actions (e.g., "If I say this, the player will feel X") before generating output.

3. The Elephant in the Room: Energy Efficiency

Transformers are notoriously resource-heavy. Future systems might adopt state-space models (e.g., Mamba) or recurrent architectures (e.g., RWKV) for real-time, embodied applications. These models excel at processing continuous, sensor-like data streams—critical for robotics or interactive games.

4. A Radical Thought: LLMs as "Cortex" Plugins

Imagine an LLM as the "neocortex" in a layered brain-inspired system:

Lower layers: Handle sensorimotor tasks (e.g., a robot’s movement, game state tracking).

Middle layers: World models and prediction engines (JEPA-like).

Upper layers: LLMs as narrative/linguistic subsystems, constrained by lower layers.

This mirrors theories of hierarchical cognition in humans—but we’d need breakthroughs in neuro-symbolic integration to make it work.

5. Philosophical Implications

If we succeed in grounding LLMs in embodied, sensory systems, we might confront Kuhnian paradigm shifts in:

Consciousness studies: Does a robot with an LLM "narrator" and rich sensorimotor loops qualify as having subjective experience?

Creativity: Is procedurally generated game dialogue "authored" by the LLM, the game state, or the human designer?

Your Experiment: A Game Character Case Study

Let’s say you’re building an LLM-driven NPC:

Encode game state via a lightweight autoencoder into token-like vectors.

Fine-tune an LLM (e.g., Mistral 7B) on game-specific dialogue, conditioned on these vectors.

Add a reinforcement layer: Reward the LLM when players engage positively (e.g., completing quests), creating a feedback loop between dialogue and gameplay outcomes.

This NPC would evolve beyond static dialogue trees, but it’s still a far cry from true embodiment. The "next step" might involve real-time world modeling—e.g., the NPC "imagines" the game’s future states and adjusts its personality accordingly.

Final Thought

The gap between transformers and embodied intelligence feels vast, but so did the gap between GPT-2 and GPT-4. Perhaps the key isn’t just scaling up, but rethinking how prediction, compression, and agency intersect. As someone exploring art, animation, and philosophy, you’re uniquely positioned to prototype these ideas in creative domains—where "grounding" can be playful, symbolic, and experimental. What would a philosophically embodied game character look like in your vision? 🎮✨

[noemyreads]

dopamine: a neurotransmitter responsible for carrying information from the brain to various parts of the body. it is known as one of the hormones of happiness and when released it causes a feeling of pleasure, satisfaction and increases motivation.

addiction: continuous and compulsive consumption of a substance or behavior (games, video games, sex), despite the harm they do to the person and to others.

several times I read myself in the pages of this book, I could see behaviors that I have that seem to be healthy, but are harmful. In fact, I think this is the magic of this book, despite bringing extreme addictions, it also brings some “harmless” addictions, such as reading, watching, eating, exercising, using the cell phone, etc. it's important for us to measure our behaviors so that it doesn't become something harmful, for example: spending hours and hours marathoning series can become a bad habit, it is necessary to understand, why are you doing this? does it affect your routine? pull you away from friends and family? if the answer is yes, then it's time to make changes.

“we all run away from suffering. some take pills. some stretch out on the couch, marathoning netflix. others read cheap novels. we do just about anything to distract ourselves from ourselves.” one of the main reasons that lead us to engage in this type of behavior is the need to escape suffering or discomfort, something that we all experience, we start to tolerate less and less suffering, with that, we always look for something that takes us away from reality.

the author presented a structure that she uses to talk to her patients about compulsive hyper consumption, through the acronym dopamine, it applies not only to conventional drugs, such as alcohol and nicotine, but also to any substance, or behavior, of high dopamine that we ingested for a long time, or simply with which we wanted to have a slightly less torturous relationship. I will present it here as a way for you, the reader, to apply it to your life and analyze your compulsive hyper consumption.

p.s. this is valid for compulsive hyper consumption of substances and behaviors that are not life-threatening to self or others when stopped on their own. in case of dependence on alcohol and other drugs, the support of a medical team prepared for this is necessary.

D stands for data. start by gathering the simple consumer facts. - what, how much and how often?-

O stands for usage purposes. understand what your goals are for the substance use or behavior.

P stands for usage related issues. identify what problems the substance use or behavior is currently or in the future causing in your life. this part is important to consider carefully, as most of us fail to see the full extent of the consequences of drug use while we are still using it. high dopamine substances and behaviors cloud our ability to accurately assess cause and effect.

A means abstinence. (this process is not for people who may be at risk of life-threatening withdrawal if they quit all at once.) start the “dopamine fast” process. go for a period of at least 4 weeks without using the substance or behavior, this will allow your homeostasis to be restored, with this you will again have the ability to derive pleasure from less potent rewards.

M stands for mindfulness. mindfulness is simply the ability to observe what our brain is doing, while it's doing it, without judgment. watch your thoughts and welcome them without judgement, this will make you develop the ability to know your brain and yourself.

I stands for insight. when you abstain from your drug of choice, you gain illuminating insights into your life and a greater understanding of what you used to be like. “you have to leave the island to see the island. we don't see each other if we don't get out of ourselves”.

N means new steps. find out what you are going to do after your month of abstinence.

E means experiment. you will return to the world armed with a new dopamine setpoint and a plan for how to keep it balanced. it will depend on you whether it will be continued abstinence or conscious use.

the author also presented the term self-commitment to treat addictions “self-commitment is the way to intentionally and spontaneously create barriers between us and our drug of choice, to mitigate compulsive hyper consumption.”

there are physical self-commitment, chronological self-commitment and categorical self-commitment.

physical self-commitment is creating literal physical barriers and/or geographic distance between ourselves and our drug of choice. for example: ask the hotel to remove the minibar, put away your video game, put away the television, etc.

chronological self-commitment is the use of time limits and goals. by restricting consumption to certain times of the day, week, month, or year, we narrow our consumption window and thereby limit our use. for example: a person can restrict their usage time and consume only on vacation, on weekends, never before thursday, never before 5 pm, and so on.

categorical self-commitment is a way to limit consumption by classifying dopamine into different categories: subtypes we allow ourselves to consume and those we don't. you should assess what is a “trigger” for your addiction and put it on the list of what not to consume. for example: for a person with addiction to masturbation, seeing his naked body is a trigger, so he will need to avoid seeing himself for a period of time.

self-commitment is a way to be free.

anyway, I couldn't give less than 5 ☆, this book was a lesson. I committed myself to making changes in certain areas of my life and it inspired me as a professional, I wanted to start my postgraduate course in neuro and work with addiction.

[fipindustries]

AI is femenine

now that is one click bait of a title.

I suddenly remembered one the less known stories by isaac asimov, "femenine intuition", where he explores the concept of "femenine robots" and what the hell would that mean. it apparently means "robots that are intuitive". and what does he mean by that? well...

Do I have to tell you that quite aside from the Three Laws, there isn't a pathway in those brains that isn't carefully designed and fixed? We have robots planned for specific tasks, implanted with specific abihties." “And you propose — ” “That at every level below the Three Laws, the paths be made open-ended. It's not difficult."

so that is interesting, this is already making a difference between AI that is made bespoke, line by line with an intended specific purpose and AI that is open ended so that it can train and absorb information on its own. whats more interesting is the purpose for which they intend to use it

We’ve got an enormous quantity of details on every star in our 300-light-year neighborhood and a notion that almost every one has a planetary system. But which has a habitable planet? Which do we visit? — We don’t know.” One of the directors said, “How would this Jane-robot help us?” “It may well be that somewhere in the libraries of data we have on those stars, there are methods for estimating the pro- babilities of the presence of Earth- type habitable planets. All we need to do is understand the data properly, look at them in the ap- propriate creative manner, make the correct correlations. We haven't done it yet. Or if some astronomer has, he hasn't been smart enough to realize what he has. “A JN-type robot could make correlations far more rapidly and far more precisely than a man could. In a day, it would make and discard as many correlations as a man could in ten years. Furthermore, it would work in truly random fashion, whereas a man would have a strong bias based on preconception and on what is already believed."

the funny thing is that all this sounds weirdly similar to how modern neural models work. ingesting gigantic ammounts of data and finding the underlying patterns and correlations to give statistically likely answers

But it's only a matter of probability, isn't it? Suppose this robot said. The highest probability habitable-planet within so-and-so light-years is Squidgee- 1 7,' or whatever, and we go there and find that a probability is only a probability and that there are no habitable planets after all. Where does that leave us?"

it even talks about the problem of hallucinating answers due to it being a stochastic parrot!

and then the story goes on to say that this all makes the robot "femenine"

“Call the robot — call it 'intuitive'." “An intuitive robot," someone muttered. “A girl robot?" A smile made its way about the conference table. Madarian seized on that. “All right. A girl robot. Our robots are sexless, of course, and so will this one be, but we always act as though they're males. We give them male petnames and call them he and him. Now this one, if we consider the nature of the mathematical structuring of the brain which I have proposed, would fall into the JN-coordinate system. The first robot would be JN-1, and I've assumed that it would be called John-1. — I'm afraid that is the level of originality of the average roboticist. But why not call it Jane-1, damn it? If the public has to be let in on what we're doing, we're constructing a feminine robot with intuition."

so yeah, there it is, by mr isaac asimov himself ladies and gentlemen