#Advantages of OpenAI

[inextures]

How OpenAI Can Offer Advantages to Web Applications?

It is widely recognized that in our daily lives, modern technology and web applications have become essential and irreplaceable parts.

OpenAI’s objective is to make artificial intelligence (AI) technology more accessible to businesses. It is a non-profit AI research organization that makes powerful artificial intelligence algorithms and tools available to developers.

It offers a variety of APIs through which third-party developers can access its powerful machine learning and AI capabilities.

OpenAI, an exceedingly appeared enterprise recognized for its modern language models, mainly GPT-3.5, represents a sport-converting leap forward inside the discipline of synthetic intelligence (AI).

The promise of OpenAI is altering the way developers believe and construct web apps for the future, from natural language processing to content generation and seamless user interactions. So let’s explore how OpenAI can offer a plethora of advantages to web applications, moving them to new heights of efficiency, personalization, and user engagement.

Natural Language Processing

Natural Language Processing (NLP) represents a field within artificial intelligence that focuses on teaching computers how to comprehend and interpret human language in a way that is both natural and meaningful.

By maximizing NLP, online applications can efficiently process and analyze vast volumes of unstructured text data, encompassing user queries, reviews, and social media posts, achieving remarkable accuracy in the process.

Web applications that tackle NLP can yield various advantages, including enhanced search functionality that delivers users more pertinent and contextually appropriate results.

NLP empowers the creation of sophisticated chatbots and virtual assistants capable of engaging with customers in a manner that closely resembles human conversation, thereby elevating customer service experiences and fostering increased user engagement.

Moreover, web apps featuring real-time language translation can reach a global audience by providing multilingual support and eliminating language barriers.

As NLP technology continues to progress, web applications will experience improved personalization, content creation, and content moderation, ultimately leading to a transformative shift in how we interact with and perceive the digital realm.

Multilingual Chatbots and Virtual Assistants

OpenAI’s language models have changed web applications targeting a global audience by introducing multilingual chatbots and virtual assistants, fundamentally converting the way communication and interaction occur.

These intelligent conversational interfaces can interact in several languages smoothly and organically, resulting in a more personalized and inclusive user experience.

The advantages of multilingual chatbots go beyond simple conversation. They enable web applications to optimize customer support operations by providing faster response times and removing language barriers that may impede efficient problem-solving.

Furthermore, by adapting to individual linguistic preferences, these chatbots increase user engagement, making interactions feel more natural and intuitive. Businesses can gain significant insights into user behavior and preferences across different linguistic groups, allowing them to fine-tune marketing tactics and product offerings for each target market.

Increased Personalization

The process of adapting products, services, content, and user experiences to individual preferences, requirements, and behaviors is known as personalization.

It entails utilizing data and AI technology to provide personalized and relevant experiences to each user.

It contributes to a more engaging and user-centric experience by delivering content and recommendations that are relevant to the interests and preferences of each user. This promotes user satisfaction and motivates users to return and connect.

It experiences increase user engagement because users are more likely to interact with relevant content. Longer session lengths, more page views, and higher conversion rates can all result from this.

It experiences increase user engagement because users are more likely to interact with relevant content. Longer session lengths, more page views, and higher conversion rates can all result from this.

Real-Time Translation

Real-time translation is a mind-blowing application of OpenAI’s language models that has the ability to break down language barriers and promote seamless communication across various global audiences.

Web apps can now provide instant translation services by putting to use the amazing powers of AI, allowing for real-time interactions between people who speak different languages.

This technology is especially useful in situations when excellent communication is critical for success, such as international business meetings, internet conferences, e-commerce platforms, and social media engagements.

Real-time translation improves accessibility and diversity while also encouraging cross-cultural collaboration and understanding. Businesses may cater to a broader client base, increase their reach to international markets, and create a more immersive and engaging user experience by incorporating OpenAI’s language models into their online apps.

Open-AI Power Fraud Detection

Language models from OpenAI, such as GPT-3.5, can be effective tools for detecting fraud in web applications and online services.

Text data, such as transaction descriptions, user messages, and other relevant textual information, can be processed and analyzed using OpenAI language models. The models can discover suspicious patterns, keywords, or phrases connected with fraudulent operations by analyzing the content of these messages.

To build patterns of usual behavior, examine past data and user interactions. When a transaction or user interaction deviates considerably from these established trends, it may be identified as a potential anomaly or fraud, prompting an additional inquiry.

Language can be used by fraudsters to manipulate or fool users. OpenAI models can employ sentiment analysis to detect patterns of manipulation, urgency, or coercion in fake messages or emails.

AI-assisted content moderation:

AI can process vast amounts of content at high speed, allowing platforms to moderate user-generated content in real-time and respond promptly to potential issues.

As user-generated content grows, AI can scale to handle the increasing moderation demands without adding significant human resources.

Its algorithms apply predefined rules consistently, reducing potential bias and ensuring uniform content moderation across the platform.

It can help identify and remove harmful content quickly, reducing the risk of legal issues, reputational damage, and potential harm to users.

While AI can handle a significant portion of the content moderation workload, it can also flag specific content for human review when the context is ambiguous or requires human judgment.

Conclusion 

OpenAI’s vast array of tools and technologies not only optimize web applications but also drive innovation, efficiency, and personalization in a rapidly evolving digital landscape. Embracing the power of OpenAI fosters an exciting future where web applications can deliver unprecedented user experiences, paving the way for a more connected and intelligent online world.

Originally published by: How OpenAI Can Offer Advantages to Web Applications?

[bonediggercharleston]

I am very wary of people going "China does it better than America" because most of it is just reactionary rejection of your overlord in favor of his rival, but this story is 1. absolutely legit and 2. way too funny.

US wants to build an AI advantage over China, uses their part in the chip supply chain to cut off China from the high-end chip market.

China's chip manufacturing is famously a decade behind, so they can't advance, right?

They did see it as a problem, but what they then did is get a bunch of Computer Scientists and Junior Programmers fresh out of college and funded their research in DeepSeek. Instead of trying to improve output by buying thousands of Nvidia graphics cards, they tried to build a different kind of model, that allowed them to do what OpenAI does at a tenth of the cost.

Them being young and at a Hedgefund AI research branch and not at established Chinese techgiants seems to be important because chinese corporate culture is apparently full of internal sabotage, so newbies fresh from college being told they have to solve the hardest problems in computing was way more efficient than what usually is done. The result:

American AIs are shook. Nvidia, the only company who actually is making profit cause they are supplying hardware, took a hit. This is just the market being stupid, Nvidia also sells to China. And the worst part for OpenAI. DeepSeek is Open Source.

Anybody can implement deepseek's model, provided they have the hardware. They are totally independent from DeepSeek, as you can run it from your own network. I think you will soon have many more AI companies sprouting out of the ground using this as its base.

What does this mean? AI still costs too much energy to be worth using. The head of the project says so much himself: "there is no commercial use, this is research."

What this does mean is that OpenAI's position is severely challenged: there will soon be a lot more competitors using the DeepSeek model, more people can improve the code, OpenAI will have to ask for much lower prices if it eventually does want to make a profit because a 10 times more efficient opensource rival of equal capability is there.

And with OpenAI or anybody else having lost the ability to get the monopoly on the "market" (if you didn't know, no AI company has ever made a single cent in profit, they all are begging for investment), they probably won't be so attractive for investors anymore. There is a cheaper and equally good alternative now.

AI is still bad for the environment. Dumb companies will still want to push AI on everything. Lazy hacks trying to push AI art and writing to replace real artists will still be around and AI slop will not go away. But one of the main drivers of the AI boom is going to be severely compromised because there is a competitor who isn't in it for immediate commercialization. Instead you will have a more decentralized open source AI field.

Or in short:

[probablyasocialecologist]

In the story of "Peter Pan," the fairy Tinkerbell only exists if people believe in her and clap for her. Once we stop believing in her magic, she starts fading away. It’s at this point she implores Peter Pan — and the broader audience — to clap as loud as they can. Tinkerbell is sustained by our attention. A new piece of emerging tech can be a lot like Tinkerbell. When it's still trying to shift from speculative ideas based on buggy demos to real material things that are normal parts of our daily lives and business practices, its existence depends on our belief in the magic of possibility. At this point, they still only exist when we believe hard enough and clap loud enough. If we stop believing and clapping, then they can start fading away, becoming more intangible by the moment until they disappear — remember 3D televisions? Just like with Tinkerbell, audience participation is necessary.  That faith in the eventual power of progress can buy time for emerging tech like AI and blockchain — which can feel more like impressive parlor tricks desperately searching for useful purposes and business models — to establish more concrete anchors in reality. Their transparency level can be set at 50 percent for a long time if there are enough people in the audience believing and clapping for them.

[...]

AI depends on vital support from people hard at work in the futurism factory. These are the executives, consultants, journalists, and other thought leaders whose job is the selling of things to come. They craft visions of a specific future — such as ones where AI models built by companies like OpenAI or Microsoft are undeniable forces of progress — and they build expectations in the public about the inevitable capabilities and irresistible outcomes of these tech products.  By flooding the zone with an endless stream of new partnerships, new products, new promises, the tech industry makes us feel disoriented and overwhelmed by a future rushing at us faster than we can handle. The desire to not be left behind — or taken advantage of — is a powerful motivator that keeps us engaged in the AI sales pitch. The breathless hype surrounding AI is more than just a side-effect of over-eager entrepreneurs; it’s a load-bearing column for the tech sector. If people believe hard enough in the future manufactured by Silicon Valley, then they start acting like it already exists before it happens. Thus the impacts of technologies like AI become a self-fulfilling prophecy. We should think of AI futurism as a sophisticated form of check kiting — cashing a check today and hoping the money will be in the account later. In other words, the business of expectations is based on producing scenarios about what might happen in the future and using them to extract speculative value in the present. It’s our belief that these promissory notes are worth anything that allows the tech industry to keep floating until the big payday finally hits. 

11 January 2025

[justinspoliticalcorner]

Stephanie Armour at KFF Health News, via AlterNet:

Researchers racing to develop bird flu vaccines for humans have turned to a cutting-edge technology that enabled the rapid development of lifesaving covid shots. There’s a catch: The mRNA technology faces growing doubts among Republicans, including people around President Donald Trump. Legislation aimed to ban or limit mRNA vaccines was introduced this year by GOP lawmakers in at least seven states. In some cases, the measures would hit doctors who give the injections with criminal penalties, fines, and possible revocation of their licenses. Some congressional Republicans are also pressing regulators to revoke federal approval for mRNA-based covid shots, which President Donald Trump touted as one of the signature achievements of his first term. The opposition comes at a critical juncture because vaccines using mRNA have applications well beyond avian flu and covid. They hold the promise of lifesaving breakthroughs to treat many diseases, from melanoma to HIV to Zika, according to clinical trials. The proposed bans could block access to these advances.

MRNA is found naturally in human cells. It is a molecule that carries genetic material and, in a vaccine, trains the body’s immune system to fight viruses, cancer cells, and other conditions. An advantage of mRNA technology is that it can be developed more quickly to target specific variants and is safer than developing a vaccine made from inactivated virus. “Right now, if we had a bird flu pandemic, we would have a shortage of the vaccine we need,” said Michael Osterholm, director of the University of Minnesota’s Center for Infectious Disease Research and Policy. “The one thing that could save us is mRNA vaccine. The challenge would be if mRNA is banned. This is truly dangerous policy.”

The pushback conflicts with innovations championed by Trump. He assembled tech tycoons at the White House just after his inauguration to announce Stargate, a $500 billion artificial intelligence initiative that could help transform cancer treatment by creating tumor-targeting mRNA vaccines. The fledging partnership between Oracle, SoftBank Corp., and OpenAI, co-founded by Elon Musk, envisions leveraging AI in part to improve health outcomes. Patients would undergo blood tests and AI would be used to find cancer.

[...] But some politically conservative doctors, lawmakers, and researchers question the safety of mRNA vaccines, especially covid shots made with the technology. Robert F. Kennedy Jr. unsuccessfully petitioned the FDA in 2021 to rescind approval for covid shots and called them “the deadliest vaccine ever made” — a controversial statement that has been refuted. Now that he’s newly confirmed as Health and Human Services secretary, Kennedy is poised to oversee federal approvals of vaccines, with the power to shape policy such as immunization schedules and appoint vaccine opponents to committees that advise on the approval of shots.

[...] Support for an mRNA ban is coming from other sources too. Florida Gov. Ron DeSantis on March 5 urged the Centers for Disease Control and Prevention to stop recommending the covid-19 vaccine for children and called for a state ban on mRNA vaccine mandates. In February, Rep. Thomas Massie (R-Ky.) said on X that the “FDA should immediately revoke approval of these shots,” and Sen. Ron Johnson (R-Wis.) is leading an investigation into the safety of the vaccines. Trump in February signed an order to strip federal funds from schools that require covid shots for attendance. Vaccine skepticism has become pronounced among Republicans since the pandemic. Four in 10 Republicans who responded to a KFF poll published in January said it was “probably” or “definitely true” that “more people have died from covid-19 vaccines than from the virus itself.” Just a quarter of Republicans reported holding that view in 2023. [...]

Networks of Opposition

Groups opposed to the mRNA technology have built a vast and well-funded legal, marketing, and social media network. Members hold conferences to discuss strategies, fund lawsuits against vaccine mandates, and produce reports on the covid vaccines. As for state legislative efforts, measures introduced this year have varied and their progress has been mixed. Montana’s measure, for instance, was blocked. Idaho lawmakers in February held a hearing on its bill, which calls for a 10-year moratorium on mRNA vaccines. Idaho’s proposal, likely to be amended, as well as Iowa’s and Montana’s have featured criminal penalties for providers who administer all or certain mRNA vaccines. In addition, some state bills, such as legislation in Pennsylvania and Tennessee, focused on the use of the vaccine in livestock and food production.

Various bills are pending in the Texas Legislature to restrict mRNA vaccines in both livestock and humans. South Carolina’s pending bill would require anyone administering certain covid mRNA vaccines to inform patients that the shot is contaminated with fragments of “bacterial plasmid DNA.” Covid mRNA shots may have minute amounts of residual DNA from production processes but they are heavily degraded and pose no risk, according to the Global Vaccine Data Network, which evaluates vaccine safety concerns. Speakers at some legislative proceedings have included representatives from Children’s Health Defense, an activist, anti-vaccine group founded by Kennedy. The Florida surgeon general in January 2024 called for a halt in the use of covid mRNA vaccines. And in Texas, Attorney General Ken Paxton in January moved to appeal a lawsuit he filed claiming Pfizer misrepresented the safety of its mRNA shot. Efforts to restrict the shots have raised the profile of groups such as the Independent Medical Alliance, which advocates for mRNA-based covid vaccines to be withdrawn from the market.

The normalization of anti-vaxxer sentiment in a sizable chunk of the GOP in recent years is leading to disastrous consequences, such as the recent attacks on mRNA technology used for vaccinations.

[theculturedmarxist]

As I’ve said before, I believe we’re at peak AI, and now that generative AI has been commoditized, the only thing that OpenAI and Anthropic have left is their ability to innovate, which I’m not sure they’re capable of doing.  And because we sit in the ruins of Silicon Valley, with our biggest “startups” all doing the same thing in the least-efficient way, living at the beck and call of public companies with multi-trillion-dollar market caps, everyone is trying to do the same thing in the same way based on the fantastical marketing nonsense of a succession of directionless rich guys that all want to create America’s Next Top Monopoly. It’s time to wake up and accept that there was never an “AI arms race,” and that the only reason that hyperscalers built so many data centers and bought so many GPUs because they’re run by people that don’t experience real problems and thus don’t know what problems real people face. Generative AI doesn’t solve any trillion-dollar problems, nor does it create outcomes that are profitable for any particular business.  DeepSeek’s models are cheaper to run, but the real magic trick they pulled is that they showed how utterly replaceable a company like OpenAI (and by extension any Large Language Model company) really is. There really isn’t anything special about any of these companies anymore — they have no moat, their infrastructural advantage is moot, and their hordes of talent irrelevant.  What DeepSeek has proven isn’t just technological, but philosophical. It shows that the scrappy spirit of Silicon Valley builders is dead, replaced by a series of different management consultants that lead teams of engineers to do things based on vibes. 

[centrally-unplanned]

Since I myself have often been a counter-critic to the AI art critics, lets flip that around. Was some of the "IP law hypocrisy" discouse floating around today, you know the stuff - oh everyone hates on Big Brother Nintendo or Disney or w/e for their machine gun copyright lawsuits, but now that generative AI is out its all about IP-senpai being a dashing prince coming in to save them. Either you like it or hate it, right? Pick a lane.

Which, for sure btw this describes some of them. Those who pretty much want AI dead for essentially spiritual reasons, yeah. But I think those are the weakmen, because the rub is that IP law is not gonna change any time soon. Those reform efforts seem pretty dead in the water, the artistic socialist utopia isn't happening. Which means you need to live in the world you have, which means you need to play the game that everyone else is playing.

OpenAI is gonna use copyright law to its advantage! As will Disney and co when fighting/balancing/dealmaking/collaborating with OpenAI and its slate of competitors. Every AI company is going to work as hard as possible to train models as cheaply as possible and sell them as expensively as possible, and part of that is going to be to push IP law in its favor around what counts as fair use, what is ownership, etc.

And while all law is really process, forever contested & changing, that is double+ true for IP law. If you think the New York Times has no chance in its lawsuit against Open AI for its use of its article archives, I think you are insulting their extremely-qualified legal team who knows way more than you. All of this stuff is up for grabs right now, no one really knows how it will shake out.

So if you are an actual career independent artist, there is in fact a lot at stake. What is the legal line for mimicking someone's "style"? Does explicit training on your previous art to generate equivalents count as transformative work? These are quasi-open legal questions, and again since the system is absolutely not going away in any form, its extremely logical to want that system to work for you. "Free art" isn't on the table; the real question is who is gonna be at the table to write the next iteration of owned art. Being at the table is an obvious desire to have. You can still wish there wasn't a table to begin with, that isn't hypocritical at all.

[mariacallous]

Reddit said ahead of its IPO next week that licensing user posts to Google and others for AI projects could bring in $203 million of revenue over the next few years. The community-driven platform was forced to disclose Friday that US regulators already have questions about that new line of business.

In a regulatory filing, Reddit said that it received a letter from the US Federal Trade Commision on Thursday asking about “our sale, licensing, or sharing of user-generated content with third parties to train AI models.” The FTC, the US government’s primary antitrust regulator, has the power to sanction companies found to engage in unfair or deceptive trade practices. The idea of licensing user-generated content for AI projects has drawn questions from lawmakers and rights groups about privacy risks, fairness, and copyright.

Reddit isn’t alone in trying to make a buck off licensing data, including that generated by users, for AI. Programming Q&A site Stack Overflow has signed a deal with Google, the Associated Press has signed one with OpenAI, and Tumblr owner Automattic has said it is working “with select AI companies” but will allow users to opt out of having their data passed along. None of the licensors immediately responded to requests for comment. Reddit also isn’t the only company receiving an FTC letter about data licensing, Axios reported on Friday, citing an unnamed former agency official.

It’s unclear whether the letter to Reddit is directly related to review into any other companies.

Reddit said in Friday’s disclosure that it does not believe that it engaged in any unfair or deceptive practices but warned that dealing with any government inquiry can be costly and time-consuming. “The letter indicated that the FTC staff was interested in meeting with us to learn more about our plans and that the FTC intended to request information and documents from us as its inquiry continues,” the filing says. Reddit said the FTC letter described the scrutiny as related to “a non-public inquiry.”

Reddit, whose 17 billion posts and comments are seen by AI experts as valuable for training chatbots in the art of conversation, announced a deal last month to license the content to Google. Reddit and Google did not immediately respond to requests for comment. The FTC declined to comment. (Advance Magazine Publishers, parent of WIRED's publisher Condé Nast, owns a stake in Reddit.)

AI chatbots like OpenAI’s ChatGPT and Google’s Gemini are seen as a competitive threat to Reddit, publishers, and other ad-supported, content-driven businesses. In the past year the prospect of licensing data to AI developers emerged as a potential upside of generative AI for some companies.

But the use of data harvested online to train AI models has raised a number of questions winding through boardrooms, courtrooms, and Congress. For Reddit and others whose data is generated by users, those questions include who truly owns the content and whether it’s fair to license it out without giving the creator a cut. Security researchers have found that AI models can leak personal data included in the material used to create them. And some critics have suggested the deals could make powerful companies even more dominant.

The Google deal was one of a “small number” of data licensing wins that Reddit has been pitching to investors as it seeks to drum up interest for shares being sold in its IPO. Reddit CEO Steve Huffman in the investor pitch described the company’s data as invaluable. “We expect our data advantage and intellectual property to continue to be a key element in the training of future” AI systems, he wrote.

In a blog post last month about the Reddit AI deal, Google vice president Rajan Patel said tapping the service’s data would provide valuable new information, without being specific about its uses. “Google will now have efficient and structured access to fresher information, as well as enhanced signals that will help us better understand Reddit content and display, train on, and otherwise use it in the most accurate and relevant ways,” Patel wrote.

The FTC had previously shown concern about how data gets passed around in the AI market. In January, the agency announced it was requesting information from Microsoft and its partner and ChatGPT developer OpenAI about their multibillion-dollar relationship. Amazon, Google, and AI chatbot maker Anthropic were also questioned about their own partnerships, the FTC said. The agency’s chair, Lina Khan, described its concern as being whether the partnerships between big companies and upstarts would lead to unfair competition.

Reddit has been licensing data to other companies for a number of years, mostly to help them understand what people are saying about them online. Researchers and software developers have used Reddit data to study online behavior and build add-ons for the platform. More recently, Reddit has contemplated selling data to help algorithmic traders looking for an edge on Wall Street.

Licensing for AI-related purposes is a newer line of business, one Reddit launched after it became clear that the conversations it hosts helped train up the AI models behind chatbots including ChatGPT and Gemini. Reddit last July introduced fees for large-scale access to user posts and comments, saying its content should not be plundered for free.

That move had the consequence of shutting down an ecosystem of free apps and add ons for reading or enhancing Reddit. Some users staged a rebellion, shutting down parts of Reddit for days. The potential for further user protests had been one of the main risks the company disclosed to potential investors ahead of its trading debut expected next Thursday—until the FTC letter arrived.

[meret118]

The paper is the latest in a string of studies that suggest keeping increasingly powerful AI systems under control may be harder than previously thought. In OpenAI’s own testing, ahead of release, o1-preview found and took advantage of a flaw in the company’s systems, letting it bypass a test challenge. Another recent experiment by Redwood Research and Anthropic revealed that once an AI model acquires preferences or values in training, later efforts to change those values can result in strategic lying, where the model acts like it has embraced new principles, only later revealing that its original preferences remain.

. . .

Scientists do not yet know how to guarantee that autonomous agents won't use harmful or unethical methods to achieve a set goal. “We've tried, but we haven't succeeded in figuring this out,” says Yoshua Bengio, founder and scientific director of Mila Quebec AI Institute, who led the International AI Safety Report 2025, a global effort to synthesize current scientific consensus of AI’s risks.

Of particular concern, Bengio says, is the emerging evidence of AI’s “self preservation” tendencies. To a goal-seeking agent, attempts to shut it down are just another obstacle to overcome. This was demonstrated in December, when researchers found that o1-preview, faced with deactivation, disabled oversight mechanisms and attempted—unsuccessfully—to copy itself to a new server. When confronted, the model played dumb, strategically lying to researchers to try to avoid being caught.

[soapver4]

Ghibli Geometry Homework

Merch Bubble: A petition for Studio Ghibli to best OpenAI by channeling the 2025 trend of cheery citrus-juiced images into an endeavor with more lasting impact — revamping the image of math and captivating young learners through geometry problem sets jazzed up with visuals and story contexts from its films.

Picture the following in their relevant aesthetic backdrops:

Estimate the height Spirited Away's Chihiro successfully descends despite her fear as she speeds down a rickety stairway section with 20 horizontal steps. Each gap between the steps is 5 units in slope and 4 units in width.

Spirited Away's No-Face tries out a circular mask 3 units in radius. Its holes for his eyes are 0.3 units in radius. How many times larger in area is his fake face compared to his fake eyes?

He next tries out a fancy circular mask which outline circumscribes tightly-packed, non-overlapping circles each 0.3 units in radius. The circles are made up of k rings of six circles surrounding one circle. Two of those hexagonally packed circles form holes for his eyes. How many times larger in area is his fake face compared to his fake eyes?

Witch Kiki in Kiki's Delivery Service is on a 10-unit long broomstick tilted at 45 degrees as she takes freshly baked cinnamon rolls to a customer. As she passes by a tall structure, she notices its tip is labeled 10, 000 units. The rolls are in a 2-unit long satchel hanging halfway on the broom. Since the temperature of the air affects how fast pastries go stale, she wonders: How far are the rolls from the ground?

A bunch of kids are squeezing onto the ginormous, fluffy tummy of the furry creature Totoro from My Neighbor Totoro. Predict how many kids can stay on the tummy given the relevant simplifications, assumptions and information. (You already got the drift.)

Make no mistake. There's much virtue in the uncluttered designs of typical math worksheets: faster concept rendering, faster information perception, lower workloads, lower technical requirements and lower production costs. One might sum them up as higher time, resource and cost efficiencies. Add to those pros a potential cultivation of academic asceticism on the learners' part. That would be efficiency as well, in the sense that we meet two student developmental goals (math and discipline) in one shot.

In eyeing these efficiencies, however, teachers and allied industry players may be neglecting their numerator terms, most of which concern learner progress. That is ironically where pure quantitative logic breaks down. We are all emotional creatures. That is all the more so, all things equal, in young people whose brains are still underdeveloped. Looking around, we can see many school leavers who have not matured in time to take full advantage of educational resources temporally and financially accessible only in early life stages. Nor have they met adult figures sufficiently skilled in the elusive art of mathematical motivation. By the time such school leavers gain an appetite for delayed gratification, austere thinking as well as for the inherent beauty in quantitative subjects, adulthood commitments and sociocultural barriers like ageist biases often deter or delay their reentry into the educational system, threatening their scholastic journey and any STEM career trajectory. There is therefore a case to be made for deep yet down-to-earth arts-based engagement of apathetic young learners, many of whom struggle to perceive the relevance of abstract fields like geometry and find math problems in general mundane, through instructor-independent means. The emotional resonance and relatability of Studio Ghibli's works — evinced through their box office successes and the controversial generative art trend applying a warm, effusive and rustic Ghibli style to personal images — would make them powerful helpmates in battles against math hate viruses, which feel as far-reaching as influenza bugs.

Even engagement of kids who will become non-STEM high-fliers can make a huge difference. Ever heard of the phrase "The medium is the message"? Our communication approaches communicate values and signals beyond what our content says. In denying all exuberant expressions of emotion and wonder a place in mathematical materials, even in the face of learners impaired by hopelessness despite their best efforts, adults are reinforcing perceptions of mathematics experts as inflexible, unfeeling and boring nerds. The persistence of those stereotypes in spite of genial, approachable educators painstakingly passing down the magical field's legacy of ingenious problem-solving tactics to students is unfair. And the few pops of color in worksheets that do try to inject fun are not enough to make a strong counter-statement. In the end, non-STEM high-fliers inherit the math as well as the stereotypes, perpetuating the latter in everyday life interactions and media portrayals. Reversal of such perpetuated negativity may spur more kids, especially counterparts who struggle in non-STEM careers and could have flourished in STEM careers, to persevere in the subject and widen their career options.

Ghibli geometry should not distract attention from school-based or educational ecosystem solutions like sharing of best pedagogical practices since they involve different chief solution architects. Content drafting may be accomplished by Studio Ghibli through the blending of its imagery and story contexts with licensed, existing geometry problem sets, leaving only an ideally quick task of expert review to math educators. Moreover, pedagogy discussions and Ghibli-related visuals occupy different influential niches. One speaks to educators, from whom successful translation of advice into action is not guaranteed. The other speaks directly to students.

The existence of entertaining math video clips and games does not obliterate the potential value of Studio Ghibli's math creations either. Unlike graphics that can be transferred onto printouts, video engagement prolongs device usage, already a hot issue of concern in today's youth climate. Moreover, no math clip or game to date has matched the cultural reach and memeability of Ghibli works. The maker of a long string of fantasy films has big shoes no mortal teacher, Tiktoker, YouTuber or software developer can readily fill.

A formidable rival to pop culture, on the other hand, is pop culture itself. This proposal can be generalized to cover a wide array of quantitative subjects and popular screen brands, except that it can be problematic to bring investigations of real-world physics into universes governed by supernatural forces.

By and by, we may even wean captivated students off fancy elements after the relevant aesthetics and narrative structures coax them to develop a fondness for the subjects' intermingling of order and surprises. The capacity for such standalone devotion can stand them in good stead in professional lives dotted all over with mundane but vital to-dos. But first, we need that captivation.

[dertaglichedan]

Trump's FTC, Led by Ferguson, Probes Microsoft's AI Operations and OpenAI Partnership

The U.S. Federal Trade Commission (FTC) under the Trump administration is advancing a comprehensive antitrust investigation into Microsoft Corp., which was initiated during the final days of the Biden administration. The probe, led by the newly appointed FTC Chair Andrew Ferguson, signals a continued focus on scrutinizing major tech companies.

The FTC has been actively gathering information by meeting with various companies and groups. It has issued a civil investigative demand to Microsoft, compelling the company to provide extensive data on its AI operations, including costs related to model training and data acquisition since 2016. The agency is particularly interested in Microsoft's data centers, its computing power challenges, and its software licensing practices.

Additionally, the FTC is examining Microsoft's decision to reduce funding for its own AI projects following a partnership with OpenAI, which might impact competition in the AI sector. The investigation also covers Microsoft's upcoming changes to its licensing rules, aiming to understand if Microsoft's profits from other business segments provide it with an unfair advantage in the AI market, as well as its data center capacity constraints.

[cookiesnerd]

ChatGPT and Google Gemini are both advanced AI language models designed for different types of conversational tasks, each with unique strengths. ChatGPT, developed by OpenAI, is primarily focused on text-based interactions. It excels in generating structured responses for writing, coding support, and research assistance. ChatGPT’s paid versions unlock additional features like image generation with DALL-E and web browsing for more current information, which makes it ideal for in-depth text-focused tasks​.

In contrast, Google Gemini is a multimodal AI, meaning it handles both text and images and can retrieve real-time information from the web. This gives Gemini a distinct advantage for tasks requiring up-to-date data or visual content, like image-based queries or projects involving creative visuals. It integrates well with Google's ecosystem, making it highly versatile for users who need both text and visual support in their interactions. While ChatGPT is preferred for text depth and clarity, Gemini’s multimodal and real-time capabilities make it a more flexible choice for creative and data-current tasks​

[justinspoliticalcorner]

Nate Silver at Silver Bulletin:

Over three years of reporting for On the Edge, I witnessed Silicon Valley’s increasing support for Donald Trump, beginning with what was usually passed off as classically liberal support for free markets and free speech — values, I should mention, that I largely agree with — and culminating with Elon Musk’s endorsement of Trump following the assassination attempt against Trump last July. You could say that Silicon Valley went “all in” in backing Trump — indeed, David Sacks, one of the hosts of the “All-In” podcast, is now the White House’s AI and crypto czar. But that isn’t really true. Silicon Valley isn’t a monolith, even though it’s subject to a particular sort of groupthink. Peter Thiel was a lone wolf when he endorsed Trump in 2016 — and although the Valley’s political attitudes have changed since then, it isn’t a complete transformation. Still now, there are plenty of “Silicon Valley types” who “like” tweets of mine that are critical of how Trump and Elon have conducted themselves during the first couple of months of the new administration. And if you surveyed the top founders and VCs, you’d find plenty who never boarded the Trump train.

However, the ones who got on board with Trump are often far more vocal than the dissenters. And in an industry where reputation is everything, the smoke signals that industry leaders send to one another matter. For instance, even Google CEO Sundar Pichai and Apple CEO Tim Cook attended Trump’s inauguration, while OpenAI CEO Sam Altman recanted his previous criticism of Trump. I have my doubts about whether any of these CEOs actually cast their ballots for Trump — instead, it was presumably a bottom-line decision. Altman’s tweet, for instance, came a day after Trump announced a joint venture that would invest in OpenAI and other companies. Contrary to the New York Times, however, I don’t think it’s hard to explain why some voices in the Valley — including Musk, who has signaled his displeasure with Trump’s tariff policy in increasingly unsubtle ways and reportedly pushed him privately, too— are now having second thoughts. (Some of Trump’s backers on Wall Street also want “backsies”.) They were betting on, essentially, a replay of Trump’s first term. After years of feeling piqued by California’s leftward cultural swing and heavy tax and regulatory burdens, they thought they’d get on the right side of a conservative vibe shift. But they also thought they’d get a good economy, run on free market principles — or better still, one where they’d be dealt into an advantaged position because of Trump’s predictable cronyism. Whether heartfelt or cynical, this willingness to play ball with Trump initially appeared to provide a strongly positive ROI. Shares in the top publicly-traded Silicon Valley firms — I’ll explain in a moment how I define that term — rose by 21 percent from the day before the election on Nov. 4, 2024 through their peak on Feb. 18, 2025, considerably outpacing major stock indices (including the NASDAQ, which also heavily weights many Silicon Valley firms). Since that peak through yesterday (Apr. 7), however — in barely more than six weeks — they’ve plunged by 27 percent, also more than the rest of the market.

Introducing the SV50 (Silicon Valley 50)

“Silicon Valley” is an ambiguous term because it both refers to a physical place — particularly Santa Clara and San Mateo Counties in California — and also (like “Hollywood” or “Wall Street”) serves as a metonym for the tech industry. Just as hedge funds in Connecticut are still considered a part of “Wall Street”, tech firms with headquarters in San Francisco, which the Valley itself has long had a love-hate relationship with, are a part of Silicon Valley for all intents. Whether firms elsewhere on the West Coast qualify — like Amazon in Seattle and Microsoft in Redmond, Washington — is more debatable. Social ties and agglomeration effects matter a lot in Silicon Valley, enough that constant threats to flee the Bay over taxes or wokeness have largely been idle — although there are important exceptions, including Musk’s Tesla, which is now HQed in Austin.

So, I decided to create a stock index for Silicon Valley that might be compared to others like the NASDAQ or the S&P 500, which meets both of these definitions. To qualify, firms must be in tech or “tech-adjacent” industries and located somewhere in the nine counties of the Bay Area. So, for instance, the discount department store chain Ross Stores doesn’t qualify even though it’s headquartered in the East Bay because it has nothing to do with tech. But Amazon and Microsoft don’t qualify either up there in Seattle. I did, however, decide to make exceptions for firms that have left California since 2020 but were founded in Silicon Valley. It would be strange to have a “Silicon Valley” stock index that doesn’t include Tesla or Thiel’s Palantir, for instance — and in recent years, the decision to relocate has often been correlated with Trump-friendly political attitudes. I also included two firms, Jack Dorsey’s Block (formerly Square) and Brian Armstrong’s Coinbase, that now claim to have no official HQ. Although not all Silicon Valley firms have embraced remote work, the declaration that you don’t have a physical HQ is also canonically Silicon Valley in its way.

Silicon Valley’s gamble on picking Donald Trump hasn’t paid off, as we have seen with the tariff debacle.

[techverse1]

Tech Stocks Plunge as DeepSeek Disrupts AI Landscape

Market Reaction: Nvidia, Broadcom, Microsoft, and Google Take a Hit On January 27, the Nasdaq Composite, heavily weighted with tech stocks, tumbled 3.1%, largely due to the steep decline of Nvidia, which plummeted 17%—its worst single-day drop on record. Broadcom followed suit, falling 17.4%, while ChatGPT backer Microsoft dipped 2.1%, and Google parent Alphabet lost 4.2%, according to Reuters.

The Philadelphia Semiconductor Index suffered a significant blow, plunging 9.2%—its largest percentage decline since March 2020. Marvell Technology experienced the steepest drop on Nasdaq, sinking 19.1%.

The selloff extended beyond the US, rippling through Asian and European markets. Japan's SoftBank Group closed down 8.3%, while Europe’s largest semiconductor firm, ASML, fell 7%.

Among other stocks hit hard, data center infrastructure provider Vertiv Holdings plunged 29.9%, while energy companies Vistra, Constellation Energy, and NRG Energy saw losses of 28.3%, 20.8%, and 13.2%, respectively. These declines were driven by investor concerns that AI-driven power demand might not be as substantial as previously expected.

Does DeepSeek Challenge the 'Magnificent Seven' Dominance? DeepSeek’s disruptive entrance has sparked debate over the future of the AI industry, particularly regarding cost efficiency and computing power. Despite the dramatic market reaction, analysts believe the ‘Magnificent Seven’—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla—will maintain their dominant position.

Jefferies analysts noted that DeepSeek’s open-source language model (LLM) rivals GPT-4o’s performance while using significantly fewer resources. Their report, titled ‘The Fear Created by China's DeepSeek’, highlighted that the model was trained at a cost of just $5.6 million—10% less than Meta’s Llama. DeepSeek claims its V3 model surpasses Llama 3.1 and matches GPT-4o in capability.

“DeepSeek’s open-source model, available on Hugging Face, could enable other AI developers to create applications at a fraction of the cost,” the report stated. However, the company remains focused on research rather than commercialization.

Brian Jacobsen, chief economist at Annex Wealth Management, told Reuters that if DeepSeek’s claims hold true, it could fundamentally alter the AI market. “This could mean lower demand for advanced chips, less need for extensive power infrastructure, and reduced large-scale data center investments,” he said.

Despite concerns, a Bloomberg Markets Live Pulse survey of 260 investors found that 88% believe DeepSeek’s emergence will have minimal impact on the Magnificent Seven’s stock performance in the coming weeks.

“Dethroning the Magnificent Seven won’t be easy,” said Steve Sosnick, chief strategist at Interactive Brokers LLC. “These companies have built strong competitive advantages, though the selloff served as a reminder that even market leaders can be disrupted.”

Investor Shift: Flight to Safe-Haven Assets As tech stocks tumbled, investors moved funds into safer assets. US Treasury yields fell, with the benchmark 10-year yield declining to 4.53%. Meanwhile, safe-haven currencies like the Japanese Yen and Swiss Franc gained against the US dollar.

According to Bloomberg, investors rotated into value stocks, including financial, healthcare, and industrial sectors. The Vanguard S&P 500 Value Index Fund ETF—home to companies like Johnson & Johnson, Procter & Gamble, and Coca-Cola—saw a significant boost.

“The volatility in tech stocks will prompt banks to reevaluate their risk exposure, likely leading to more cautious positioning,” a trading executive told Reuters.

OpenAI’s Sam Altman Responds to DeepSeek’s Rise OpenAI CEO Sam Altman acknowledged DeepSeek’s rapid ascent, describing it as “invigorating” competition. In a post on X, he praised DeepSeek’s cost-effective AI model but reaffirmed OpenAI’s commitment to cutting-edge research.

“DeepSeek’s R1 is impressive, particularly given its cost-efficiency. We will obviously deliver much better models, and competition is exciting!” Altman wrote. He hinted at upcoming OpenAI releases, stating, “We are focused on our research roadmap and believe

[samarthdas]

Exploring DeepSeek and the Best AI Certifications to Boost Your Career

Understanding DeepSeek: A Rising AI Powerhouse

DeepSeek is an emerging player in the artificial intelligence (AI) landscape, specializing in large language models (LLMs) and cutting-edge AI research. As a significant competitor to OpenAI, Google DeepMind, and Anthropic, DeepSeek is pushing the boundaries of AI by developing powerful models tailored for natural language processing, generative AI, and real-world business applications.

With the AI revolution reshaping industries, professionals and students alike must stay ahead by acquiring recognized certifications that validate their skills and knowledge in AI, machine learning, and data science.

Why AI Certifications Matter

AI certifications offer several advantages, such as:

Enhanced Career Opportunities: Certifications validate your expertise and make you more attractive to employers.

Skill Development: Structured courses ensure you gain hands-on experience with AI tools and frameworks.

Higher Salary Potential: AI professionals with recognized certifications often command higher salaries than non-certified peers.

Networking Opportunities: Many AI certification programs connect you with industry experts and like-minded professionals.

Top AI Certifications to Consider

If you are looking to break into AI or upskill, consider the following AI certifications:

1. AICerts – AI Certification Authority

AICerts is a recognized certification body specializing in AI, machine learning, and data science.

It offers industry-recognized credentials that validate your AI proficiency.

Suitable for both beginners and advanced professionals.

2. Google Professional Machine Learning Engineer

Offered by Google Cloud, this certification demonstrates expertise in designing, building, and productionizing machine learning models.

Best for those who work with TensorFlow and Google Cloud AI tools.

3. IBM AI Engineering Professional Certificate

Covers deep learning, machine learning, and AI concepts.

Hands-on projects with TensorFlow, PyTorch, and SciKit-Learn.

4. Microsoft Certified: Azure AI Engineer Associate

Designed for professionals using Azure AI services to develop AI solutions.

Covers cognitive services, machine learning models, and NLP applications.

5. DeepLearning.AI TensorFlow Developer Certificate

Best for those looking to specialize in TensorFlow-based AI development.

Ideal for deep learning practitioners.

6. AWS Certified Machine Learning – Specialty

Focuses on AI and ML applications in AWS environments.

Includes model tuning, data engineering, and deep learning concepts.

7. MIT Professional Certificate in Machine Learning & Artificial Intelligence

A rigorous program by MIT covering AI fundamentals, neural networks, and deep learning.

Ideal for professionals aiming for academic and research-based AI careers.

Choosing the Right AI Certification

Selecting the right certification depends on your career goals, experience level, and preferred AI ecosystem (Google Cloud, AWS, or Azure). If you are a beginner, starting with AICerts, IBM, or DeepLearning.AI is recommended. For professionals looking for specialization, cloud-based AI certifications like Google, AWS, or Microsoft are ideal.

With AI shaping the future, staying certified and skilled will give you a competitive edge in the job market. Invest in your learning today and take your AI career to the next leve

[digitalbizai]

ChatGPT vs DeepSeek: A Comprehensive Comparison of AI Chatbots

Artificial Intelligence (AI) has revolutionized the way we interact with technology. AI-powered chatbots, such as ChatGPT and DeepSeek, have emerged as powerful tools for communication, research, and automation. While both models are designed to provide intelligent and conversational responses, they differ in various aspects, including their development, functionality, accuracy, and ethical considerations. This article provides a detailed comparison of ChatGPT and DeepSeek, helping users determine which AI chatbot best suits their needs.

Understanding ChatGPT and DeepSeek

What is ChatGPT?

ChatGPT, developed by OpenAI, is one of the most advanced AI chatbots available today. Built on the GPT (Generative Pre-trained Transformer) architecture, ChatGPT has been trained on a vast dataset, enabling it to generate human-like responses in various contexts. The chatbot is widely used for content creation, coding assistance, education, and even casual conversation. OpenAI continually updates ChatGPT to improve its accuracy and expand its capabilities, making it a preferred choice for many users.

What is DeepSeek?

DeepSeek is a relatively new AI chatbot that aims to compete with existing AI models like ChatGPT. Developed with a focus on efficiency and affordability, DeepSeek has gained attention for its ability to operate with fewer computing resources. Unlike ChatGPT, which relies on large-scale data processing, DeepSeek is optimized for streamlined AI interactions, making it a cost-effective alternative for businesses and individuals looking for an AI-powered chatbot.

Key Differences Between ChatGPT and DeepSeek

1. Development and Technology

ChatGPT: Built on OpenAI’s GPT architecture, ChatGPT undergoes extensive training with massive datasets. It utilizes deep learning techniques to generate coherent and contextually accurate responses. The model is updated frequently to enhance performance and improve response quality.

DeepSeek: While DeepSeek also leverages machine learning techniques, it focuses on optimizing efficiency and reducing computational costs. It is designed to provide a balance between performance and affordability, making it a viable alternative to high-resource-demanding models like ChatGPT.

2. Accuracy and Response Quality

ChatGPT: Known for its ability to provide highly accurate and nuanced responses, ChatGPT excels in content creation, problem-solving, and coding assistance. It can generate long-form content and has a strong understanding of complex topics.

DeepSeek: While DeepSeek performs well for general queries and casual interactions, it may struggle with complex problem-solving tasks compared to ChatGPT. Its responses tend to be concise and efficient, making it a suitable choice for straightforward queries but less reliable for in-depth discussions.

3. Computational Efficiency and Cost

ChatGPT: Due to its extensive training and large-scale model, ChatGPT requires significant computational power, making it costlier for businesses to integrate into their systems.

DeepSeek: One of DeepSeek’s key advantages is its ability to function with reduced computing resources, making it a more affordable AI chatbot. This cost-effectiveness makes it an attractive option for startups and small businesses with limited budgets.

4. AI Training Data and Bias

ChatGPT: Trained on diverse datasets, ChatGPT aims to minimize bias but still faces challenges in ensuring completely neutral and ethical responses. OpenAI implements content moderation policies to filter inappropriate or biased outputs.

DeepSeek: DeepSeek also incorporates measures to prevent bias but may have different training methodologies that affect its neutrality. As a result, users should assess both models to determine which aligns best with their ethical considerations and content requirements.

5. Use Cases and Applications

ChatGPT: Best suited for individuals and businesses that require advanced AI assistance for content creation, research, education, customer service, and coding support.

DeepSeek: Ideal for users seeking an affordable and efficient AI chatbot for basic queries, quick responses, and streamlined interactions. It may not offer the same depth of analysis as ChatGPT but serves as a practical alternative for general use.

Which AI Chatbot Should You Choose?

The choice between ChatGPT and DeepSeek depends on your specific needs and priorities. If you require an AI chatbot that delivers high accuracy, complex problem-solving, and extensive functionality, ChatGPT is the superior choice. However, if affordability and computational efficiency are your primary concerns, DeepSeek provides a cost-effective alternative.

Businesses and developers should consider factors such as budget, processing power, and the level of AI sophistication required before selecting an AI chatbot. As AI technology continues to evolve, both ChatGPT and DeepSeek will likely see further improvements, making them valuable assets in the digital landscape.

Final Thoughts

ChatGPT and DeepSeek each have their strengths and weaknesses, catering to different user needs. While ChatGPT leads in performance, depth, and versatility, DeepSeek offers an economical and efficient AI experience. As AI chatbots continue to advance, users can expect even more refined capabilities, ensuring AI remains a powerful tool for communication and automation.

By understanding the key differences between ChatGPT and DeepSeek, users can make informed decisions about which AI chatbot aligns best with their objectives. Whether prioritizing accuracy or cost-efficiency, both models contribute to the growing impact of AI on modern communication and technology.

[mariacallous]

Connecting the dots of recent research suggests a new future for traditional websites:

Artificial Intelligence (AI)-powered search can provide a full answer to a user’s query 75% of the time without the need for the user to go to a website, according to research by The Atlantic.

A worldwide survey from the University of Toronto revealed that 22% of ChatGPT users “use it as an alternative to Google.”

Research firm Gartner forecasts that traffic to the web from search engines will fall 25% by 2026.

Pew Research found that a quarter of all web pages developed between 2013 and 2023 no longer exist.

The large language models (LLMs) of generative AI that scraped their training data from websites are now using that data to eliminate the need to go to many of those same websites. Respected digital commentator Casey Newton concluded, “the web is entering a state of managed decline.” The Washington Post headline was more dire: “Web publishers brace for carnage as Google adds AI answers.”

From decentralized information to centralized conclusions

Created by Sir Tim Berners-Lee in 1989, the World Wide Web redefined the nature of the internet into a user-friendly linkage of diverse information repositories. “The first decade of the web…was decentralized with a long-tail of content and options,” Berners-Lee wrote this year on the occasion of its 35th anniversary.  Over the intervening decades, that vision of distributed sources of information has faced multiple challenges. The dilution of decentralization began with powerful centralized hubs such as Facebook and Google that directed user traffic. Now comes the ultimate disintegration of Berners-Lee’s vision as generative AI reduces traffic to websites by recasting their information.

The web’s open access to the world’s information trained the large language models (LLMs) of generative AI. Now, those generative AI models are coming for their progenitor.

The web allowed users to discover diverse sources of information from which to draw conclusions. AI cuts out the intellectual middleman to go directly to conclusions from a centralized source.

The AI paradigm of cutting out the middleman appears to have been further advanced in Apple’s recent announcement that it will incorporate OpenAI to enable its Siri app to provide ChatGPT-like answers. With this new deal, Apple becomes an AI-based disintermediator, not only eliminating the need to go to websites, but also potentially disintermediating the need for the Google search engine for which Apple has been paying $20 billion annually.

The Atlantic, University of Toronto, and Gartner studies suggest the Pew research on website mortality could be just the beginning. Generative AI’s ability to deliver conclusions cannibalizes traffic to individual websites threatening the raison d’être of all websites, especially those that are commercially supported.  

Echoes of traditional media and the web

The impact of AI on the web is an echo of the web’s earlier impact on traditional information providers. “The rise of digital media and technology has transformed the way we access our news and entertainment,” the U.S. Census Bureau reported in 2022, “It’s also had a devastating impact on print publishing industries.” Thanks to the web, total estimated weekday circulation of U.S. daily newspapers fell from 55.8 million in 2000 to 24.2 million by 2020, according to the Pew Research Center.

The World Wide Web also pulled the rug out from under the economic foundation of traditional media, forcing an exodus to proprietary websites. At the same time, it spawned a new generation of upstart media and business sites that took advantage of its low-cost distribution and high-impact reach. Both large and small websites now feel the impact of generative AI.   

Barry Diller, CEO of media owner IAC, harkened back to that history when he warned a year ago, “We are not going to let what happened out of free internet happen to post-AI internet if we can help it.” Ominously, Diller observed, “If all the world’s information is able to be sucked up in this maw, and then essentially repackaged in declarative sentence in what’s called chat but isn’t chat…there will be no publishing; it is not possible.”

The New York Times filed a lawsuit against OpenAI and Microsoft alleging copyright infringement from the use of Times data to train LLMs. “Defendants seek to free-ride on The Times’s massive investment in its journalism,” the suit asserts, “to create products that substitute for The Times and steal audiences away from it.”1

Subsequently, eight daily newspapers owned by Alden Global Capital, the nation’s second largest newspaper publisher, filed a similar suit. “We’ve spent billions of dollars gathering information and reporting news at our publications, and we can’t allow OpenAI and Microsoft to expand the Big Tech playbook of stealing our work to build their own businesses at our expense,” a spokesman explained.

The legal challenges are pending. In a colorful description of the suits’ allegations, journalist Hamilton Nolan described AI’s threat as an “Automated Death Star.”

“Providential opportunity”?

Not all content companies agree. There has been a groundswell of leading content companies entering into agreements with OpenAI.

In July 2023, the Associated Press became the first major content provider to license its archive to OpenAI. Recently, however, the deal-making floodgates have opened. Rupert Murdoch’s News Corp, home of The Wall Street Journal, New York Post, and multiple other publications in Australia and the United Kingdom, German publishing giant Axel Springer, owner of Politico in the U.S. and Bild  and Welt in Germany, venerable media company The Atlantic, along with new media company Vox Media, the Financial Times, Paris’ Le Monde, and Spain’s Prisa Media have all contracted with OpenAI for use of their product.

Even Barry Diller’s publishing unit, Dotdash Meredith, agreed to license to OpenAI, approximately a year after his apocalyptic warning.  

News Corp CEO Robert Thomson described his company’s rationale this way in an employee memo: “The digital age has been characterized by the dominance of distributors, often at the expense of creators, and many media companies have been swept away by a remorseless technological tide. The onus is now on us to make the most of this providential opportunity.”

“There is a premium for premium journalism,” Thomson observed. That premium, for News Corp, is reportedly $250 million over five years from OpenAI. Axel Springer’s three-year deal is reportedly worth $25 to $30 million. The Financial Times terms were reportedly in the annual range of $5 to $10 million.

AI companies’ different approaches

While publishers debate whether AI is “providential opportunity” or “stealing our work,” a similar debate is ongoing among AI companies. Different generative AI companies have different opinions whether to pay for content, and if so, which kind of content.

When it comes to scraping information from websites, most of the major generative AI companies have chosen to interpret copyright law’s “fair use doctrine” allowing the unlicensed use of copyrighted content in certain circumstances. Some of the companies have even promised to indemnify their users if they are sued for copyright infringement.

Google, whose core business is revenue generated by recommending websites, has not sought licenses to use the content on those websites. “The internet giant has long resisted calls to compensate media companies for their content, arguing that such payments would undermine the nature of the open web,” the New York Times explained. Google has, however, licensed the user-generated content on social media platform Reddit, and together with Meta has pursued Hollywood rights.

OpenAI has followed a different path. Reportedly, the company has been pitching a “Preferred Publisher Program” to select content companies. Industry publication AdWeek reported on a leaked presentation deck describing the program. The publication said OpenAI “disputed the accuracy of the information” but claimed to have confirmed it with four industry executives. Significantly, the OpenAI pitch reportedly offered not only cash remuneration, but also other benefits to cooperating publishers.    

As of early June 2024, other large generative AI companies have not entered into website licensing agreements with publishers.

Content companies surfing an AI tsunami

On the content creation side of the equation, major publishers are attempting to avoid a repeat of their disastrous experience in the early days of the web while smaller websites are fearful the impact on them could be even greater.

As the web began to take business from traditional publishers, their leadership scrambled to find a new economic model. Ultimately, that model came to rely on websites, even though website advertising offered them pennies on their traditional ad dollars. Now, even those assets are under attack by the AI juggernaut. The content companies are in a new race to develop an alternative economic model before their reliance on web search is cannibalized.

The OpenAI Preferred Publisher Program seems to be an attempt to meet the needs of both parties.

The first step in the program is direct compensation. To Barry Diller, for instance, the fact his publications will get “direct compensation for our content” means there is “no connection” between his apocalyptic warning 14 months ago and his new deal with OpenAI.

Reportedly, the cash compensation OpenAI is offering has two components: “guaranteed value” and “variable value.” Guaranteed value is compensation for access to the publisher’s information archive. Variable value is payment based on usage of the site’s information.

Presumably, those signing with OpenAI see it as only the first such agreement. “It is in my interest to find agreements with everyone,” Le Monde CEO Louis Dreyfus explained.   

But the issue of AI search is greater than simply cash. Atlantic CEO Nicolas Thompson described the challenge: “We believe that people searching with AI models will be one of the fundamental ways that people navigate to the web in the future.” Thus, the second component in OpenAI’s proposal to publishers appears to be promotion of publisher websites within the AI-generated content. Reportedly, when certain publisher content is utilized, there will be hyperlinks and hover links to the websites themselves, in addition to clickable buttons to the publisher.

Finally, the proposal reportedly offers publishers the opportunity to reshape their business using generative AI technology. Such tools include access to OpenAI content for the publishers’ use, as well as the use of OpenAI for writing stories and creating new publishing content.

Back to the future?

Whether other generative AI and traditional content companies embrace this kind of cooperation model remains to be seen. Without a doubt, however, the initiative by both parties will have its effects.

One such effect was identified in a Le Monde editorial explaining their licensing agreement with OpenAI. Such an agreement, they argued, “will make it more difficult for other AI platforms to evade or refuse to participate.” This, in turn, could have an impact on the copyright litigation, if not copyright law.

We have seen new technology-generated copyright issues resolved in this way before.2 Finding a credible solution that works for both sides is imperative. The promise of AI is an almost boundless expansion of information and the knowledge it creates. At the same time, AI cannot be a continued degradation of the free flow of ideas and journalism that is essential for democracy to function.

Newton’s Law in the AI age

In 1686 Sir Isaac Newton posited his three laws of motion. The third of these holds that for every action there is an equal and opposite reaction. Newton described the consequence of physical activity; generative AI is raising the same consequential response for informational activity.

The threat of generative AI has pushed into the provision of information and the economics of information companies. We know the precipitating force, the consequential effects on the creation of content and free flow of information remain a work in progress.