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jayeltontoro · 3 months ago

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"America is exceptional in that it represents the one place where there are lots of wealthy people who are totally defenseless. We're an all-you-can-eat buffet for the privacy-annihilating voyeurs of Silicon Valley."

And I guess that points pretty clearly at the aims of DOGE and melon skunk in looting the federal tax database of every US citizen's data. It's worth more than all the gold in fort Knox that it would seem the first felon (FFOTUS ?) and his gang is targeting next...

Two weak spots in Big Tech economics

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in AUSTIN on Mar 10. I'm also appearing at SXSW and at many events around town, for Creative Commons, Fediverse House, and EFF-Austin. More tour dates here.

Big Tech's astonishing scale is matched only by its farcical valuations – price-to-earnings ratios that consistently dwarf the capitalization of traditional hard-goods businesses. For example, Amazon's profit-to-earnings ratio is 37.65; Target's is only 13.34. That means that investors value every dollar Amazon brings in at three times the value they place on a dollar spent at Target.

The fact that Big Tech stocks trade at such a premium isn't merely of interest to tech investors, or even to the personal wealth managers who handle the assets of tech executives whose personal portfolios are full of their employers' stock options.

The high valuations of tech stocks don't just reflect an advantage over bricks and mortar firms – they are the advantage. If you're Target and you're hoping to hire someone who's just interviewed at Amazon, you have to beat Amazon's total compensation offer. But when Amazon makes that offer, they can pay some – maybe even most – of the offer in stock, rather than in cash.

This is a huge advantage! After all, to get dollars, both Amazon and Target have to convince you to spend money in their stores (or, in Amazon's case, with its cloud, or as a Prime sub, etc etc). Both Amazon and Target get their dollars from entities outside of the firm's four walls, and the dollars only come in when they convince someone else to do business with them.

But stock comes from inside the firm. Amazon makes new Amazon shares by typing zeroes into a spreadsheet. They don't have to convince you to buy anything in order to issue that new stock. That is their call, and their call alone.

Amazon can buy lots of things with stock – not just the labor of in-demand technical workers who command six-figure salaries. They can even buy whole companies using stock. So if Amazon and Target are bidding against one another for an anticompetitive acquisition of a key supplier or competitor, Amazon can beat Target's bid without having to spend the dollars its shareholders would like them to divert to dividends, stock buybacks, etc.

In other words, a company with a fantastic profit/earning ratio has its own money-printer that produces currency that can be used to buy labor and even acquire companies.

But why do investors value tech stocks so highly? In part, it's just circular reasoning: a company with a high stock price can beat its competitors because it has a high stock price, so I should buy its stock, which will drive up its stock price even further.

But there's more to this than self-fulfilling prophecy. The high price of tech stocks reflects the market's belief that these companies will continue to grow. If you think a company will be ten times bigger in two years, and it's only priced at three times as much as mature rivals that have stopped growing altogether, then that 300% stock premium is a bargain, because the company will have 1,000% growth in just a couple years. Tech companies have proven themselves, time and again, to be capable of posting incredible growth – think of how quickly Google went from a niche competitor to established search engines to the dominant player, with a 90% market share.

That kind of growth is enough to make anyone giddy, but it eventually runs up against the law of large numbers: doubling a small number is easy, doubling a large number is much, much harder. A search engine that's used by 90% of the world can't double its users – there just aren't enough people to sign up. They'd need to breed several billion new humans, raise them to maturity, and then convince them to be Google users.

And here's the thing: the flipside of the huge profits that can be reaped by investors who buy stocks at a premium in anticipation of growth is the certainty that you will be wiped out if you're still holding the stock when the growth halts. When Amazon stops growing, its PE ratio should fall to something like Target's, which means that its stock should decline by two thirds on that day.

Which is why Big Tech investors tend to be twitchy, hair-trigger types, easily stampeded into mass selloffs. That's what happened in 2022, when Facebook admitted to investors that it had grown more slowly than it had projected, and investors staged the largest stock selloff in history (to that point – hi, Nvidia!), wiping a quarter-trillion dollars off Meta's valuation in a day:

https://www.forbes.com/sites/sergeiklebnikov/2022/02/03/stocks-plunge-after-facebooks-massive-sell-off-nasdaq-falls-37/

As Stein's Law has it: "anything that can't go on forever eventually stops." Growth stocks have to stop growing, eventually, and when they do, you'd better beat everyone else to the fire exit, or you're going to get crushed in the stampede.

Which is why tech companies are so obsessed with both actual growth, and stories about growth. Facebook spent tens of billions on bribes to telcos around the world, demanding that they charge extra to access non-Facebook websites and apps, in a bid to sign up "the next billion users":

https://www.eff.org/deeplinks/2019/02/countries-zero-rating-have-more-expensive-wireless-broadband-countries-without-it

That wasn't just about some ideological commitment to growth – it was about the real, material advantages that a growing company has, namely, that it can substitute the stock it creates for free by typing zeroes into a spreadsheet for money that it can only get by convincing you to give your money to it.

"Facebook Zero" (as this bribery program was called) was about actual growth: finding people who weren't Facebook users and turning them into Facebook users, preferably forever (thanks to Facebook's suite of lock-in tactics that make it a digital roach motel that users check into but don't check out of):

https://www.eff.org/deeplinks/2021/08/facebooks-secret-war-switching-costs

But plenty of the things that Big Tech gets up to are about the narrative of growth. That's why Big Tech has pumped every tech bubble of this stupid decade: metaverse, cryptocurrency, AI. These technologies have each been at the forefront of Big Tech marketing and investor communications, but not solely because they represented a market opportunity. Rather, they represented a more-or-less plausible explanation for how these companies that were on the wrong side of the law of large numbers could continue to double in size, without breeding billions of new customers to sign up for their services.

The tell – as always – comes in the way that these companies refute their critics. When critics point out that Facebook spent $1.2 billion on a metaverse product that only has 32 users:

https://futurism.com/the-byte/metaverse-decentraland-report-active-users

Or that practically no one buys anything with cryptocurrency:

https://www.mollywhite.net/annotations/latecomers-guide-to-crypto/

Not even when the government gives them free crypto and passes a law forcing merchants to accept crypto:

https://bitcoinblog.de/2024/09/02/weak-bitcoin-adoption-in-el-salvador-disappoints-the-president/

Or that hardly anyone uses AI, and what uses it does have are often low-value:

https://www.wheresyoured.at/oai-business/

The "narrative entrepreneurs" behind the claims of infinite growth from these technologies all have the same response: "That's what they said about the web, and yet it grew really fast! People who lacked the vision to understand the web's potential missed out. Buy [crypto|metaverse|AI] or have fun being poor!"

It's true – there were a lot of people who were blithely dismissive of the web, and they were wrong. But the fact that the web's skeptics were wrong doesn't mean that skepticism itself is foolish. People were also skeptical of Qibi, Beanie Babies, and the Segway – all of which were predicted to continue to increase in value forever and become permanently installed as significant facts in the economy. The fact that lots of people think something is stupid is not a reliable indicator that it is actually great.

So it's not just that capitalism adopts "the ideology of a tumor" in insisting that infinite growth is possible. The value in corporate claims to eternal growth is not aesthetic, it is material. If the market believes a company will grow, then that company gets to print its own money, which lets it outcompete mature rivals, which lets it grow some more.

But! When the company runs out of growth potential, the process runs in reverse. Not only do executives – whose portfolios are stuffed full of their own company's shares – stand to lose most of their net worth overnight, but once a company's stock starts to decline, it can expect to see an exodus of the key personnel who are compensated in now-worthless stock. That means that once a company hits a bad bump in the road that sets it off course, it needs to worry about losing all the skilled employees who can get it back on the road.

So growth is important, not for its own sake, but for how it affects the cost basis of companies, and thus determines their competitive outlook. But not all growth is created equal.

Remember when Facebook pissed away billions in a bid to capture "the next billion users"? Those users – people from poor countries in the global south – were not as valuable to Facebook as its US customers. The news that sparked a $250 billion, one-day selloff of Facebook shares wasn't merely about anemic growth – it was specifically about anemic growth in the USA.

American customers are worth more than other users to Big Tech – that's true even of users from other populous countries, and of users from other wealthy countries. Norway is rich as hell, but each Norwegian Facebook user is worth pennies on the kroner compared to American users. And there are brazilians of people in South America, but they're worth even less per capita than Norwegians are. Even the whole EU, with its 500m+ relatively wealthy consumers, is only worth a fraction of the US market.

Why is the American market so prized by Big Tech? Because it the only country in the world at the center of a Venn diagram with three overlapping circles. America is the only country in the world that is:

a) populous;

b) wealthy; and

c) totally lacking in legal privacy protections.

The US Congress last updated American consumer privacy law in 1988, when the Video Privacy Protection Act was passed to protect Americans from the high-tech threat of…video store clerks leaking your rental history to the newspapers. Despite the bewildering, obvious, serious privacy risks that have emerged since Die Hard was in theaters, Congress has done nothing to extend Americans' consumer privacy rights.

There are other rich countries where privacy law sucks, but they are small countries with few people. There are extremely populous poor countries with shitty privacy laws, but they're poor. Tech has to steal the private data of dozens of those people to make as much money as they can get from selling the data of just one American. And there are other rich, populous countries – like Germany, say – but those countries actually defend the privacy of the people who live there, and so the revenue tech gets from each of those users is even lower than the RPU for the undefended poor people of the global south.

America is exceptional in that it represents the one place where there are lots of wealthy people who are totally defenseless. We're an all-you-can-eat buffet for the privacy-annihilating voyeurs of Silicon Valley.

These are the two dirty secrets of Big Tech's economics. These companies are reliant on the fragile narrative of infinite growth, and that narrative isn't merely about global growth, but it is particularly and especially about growth in the USA.

Tech's power comes from an implausible story of discovering an endless stream of Americans to sign up and screw over. That story is extremely load-bearing – so much so that by the instant at which the first crack appears, collapse is only moments away. And boy, are there cracks:

https://www.wheresyoured.at/power-cut/

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/2025/03/06/privacy-last/#exceptionally-american

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

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

#big tech #corruption #data theft #privacy #in a society where your welfare is dependent on the integrity of your personal data #violating that data is violating the person and should be treated just as seriously

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jonah-miles-smith · 9 months ago

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Unlocking Business Potential Through Data & Analytics: A Comprehensive Guide

In today's data-driven world, leveraging Data Analysis is essential for businesses to remain competitive. With the rise of Big Data, organizations have unprecedented access to vast amounts of information, but effectively harnessing this data requires advanced techniques and tools. This is where Data Science comes into play, utilizing sophisticated methods to extract actionable insights.

One of the core components of Business Intelligence (BI) is the ability to make informed decisions based on data. Machine Learning algorithms, a subset of AI, can predict future trends and behaviours by analysing historical data. Combining Data Visualization with these predictions allows businesses to present complex data in a more understandable and actionable format.

Predictive Analytics is particularly valuable for forecasting future outcomes based on current data trends. It involves analyzing patterns and using statistical techniques to predict future events, which is crucial for strategic planning and decision-making. Data Mining and Data Management also play significant roles here, as they help in uncovering patterns and ensuring data is organized and accessible.

Investing in Analytics Tools can streamline the process of analysing and interpreting data. From Data Warehousing solutions that store and manage large volumes of data-to-Data Analytics Software that provides advanced analytical capabilities, the right tools can make a significant difference in efficiency and accuracy.

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A solid understanding of Statistical Analysis and Data Modelling is crucial for interpreting data accurately. These techniques help in making sense of the data and ensuring that insights are reliable and actionable. Data Governance ensures that data is used ethically and complies with relevant regulations, while Real-Time Analytics provides immediate insights that can influence quick decision-making.

Data Integration is another important aspect, as it involves combining data from various sources to create a unified view. Effective Data Reporting practices are essential for communicating insights to stakeholders clearly and effectively.

In summary, the synergy between Data Analysis, Business Intelligence, and cutting-edge technologies like Machine Learning and Artificial Intelligence can unlock significant value for businesses. By investing in the right Analytics Tools and adhering to best practices in Data Management and Governance, organizations can harness the full potential of their data and drive strategic growth.

#Data & Analytics #Data Engineering #Data Integration #Data Management #Big Data #Data Reporting #Predictive Analytics #data analytic providers

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handleerz · 9 months ago

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#data #data integrity #datascience #data analytics #big data #data center #data business

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kiaktuell · 10 months ago

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Risikokapital fließt vermehrt in aufstrebende KI-Startups für Gesundheitslösungen

In den letzten Jahren hat sich der Gesundheitssektor zunehmend in ein attraktives Ziel für Risikokapitalinvestitionen verwandelt, insbesondere im Bereich der Künstlichen Intelligenz (KI). Mit der rasanten Entwicklung neuer Technologien und Lösungen, die darauf abzielen, die Effizienz und Qualität der Gesundheitsversorgung zu verbessern, haben Investoren begonnen, verstärkt in aufstrebende…

#Algorithmen #Big Data #Datenanalyse #Geschäftsmodell #Gesundheitswesen #Integration #Intelligenz #KI #Künstliche Intelligenz #Medizin #Optimierung #Partnerschaften #Risikokapital #SEM #Telemedizin #Überwachung

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jcmarchi · 3 months ago

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The ethics of AI and how they affect you - AI News

New Post has been published on https://thedigitalinsider.com/the-ethics-of-ai-and-how-they-affect-you-ai-news/

The ethics of AI and how they affect you - AI News

Having worked with AI since 2018, I’m watching its slow but steady pick-up alongside the unstructured bandwagon-jumping with considerable interest. Now that the initial fear has subsided somewhat about a robotic takeover, discussion about the ethics that will surround the integration of AI into everyday business structures has taken its place.

A whole new range of roles will be required to handle ethics, governance and compliance, all of which are going to gain enormous value and importance to organisations.

Probably the most essential of these will be an AI Ethics Specialist, who will be required to ensure Agentic AI systems meet ethical standards like fairness and transparency. This role will involve using specialised tools and frameworks to address ethical concerns efficiently and avoid potential legal or reputational risks. Human oversight to ensure transparency and responsible ethics is essential to maintain the delicate balance between data driven decisions, intelligence and intuition.

In addition, roles like Agentic AI Workflow Designer, AI Interaction and Integration Designer will ensure AI integrates seamlessly across ecosystems and prioritises transparency, ethical considerations, and adaptability. An AI Overseer will also be required, to monitor the entire Agentic stack of agents and arbiters, the decision-making elements of AI.

For anyone embarking on the integration of AI into their organisation and wanting to ensure the technology is introduced and maintained responsibly, I can recommend consulting the United Nations’ principles. These 10 principles were created by the United Nations in 2022, in response to the ethical challenges raised by the increasing preponderance of AI.

So what are these ten principles, and how can we use them as a framework?

First, do no harm

As befits technology with an autonomous element, the first principle focuses on the deployment of AI systems in ways that will avoid any negative impact on social, cultural, economic, natural or political environments. An AI lifecycle should be designed to respect and protect human rights and freedoms. Systems should be monitored to ensure that that situation is maintained and no long-term damage is being done.

Avoid AI for AI’s sake

Ensure that the use of AI is justified, appropriate and not excessive. There is a distinct temptation to become over-zealous in the application of this exciting technology and it needs to be balanced against human needs and aims and should never be used at the expense of human dignity.

Safety and security

Safety and security risks should be identified, addressed and mitigated

throughout the life cycle of the AI system and on an on-going basis. Exactly the same robust health and safety frameworks should be applied to AI as to any other area of the business.

Equality

Similarly, AI should be deployed with the aim of ensuring the equal and just distribution of the benefits, risks and cost, and to prevent bias, deception, discrimination and stigma of any kind.

Sustainability

AI should be aimed at promoting environmental, economic and social sustainability. Continual assessment should be made to address negative impacts, including any on the generations to come.

Data privacy, data protection and data governance

Adequate data protection frameworks and data governance mechanisms should be established or enhanced to ensure that the privacy and rights of individuals are maintained in line with legal guidelines around data integrity and personal data protection. No AI system should impinge on the privacy of another human being.

Human oversight

Human oversight should be guaranteed to ensure that the outcomes of using AI are fair and just. Human-centric design practises should be employed and capacity to be given for a human to step in at any stage and make a decision on how and when AI should be used, and to over-ride any decision made by AI. Rather dramatically but entirely reasonably, the UN suggests any decision affecting life or death should not be left to AI.

Transparency and Explainability

This, to my mind, forms part of the guidelines around equality. Everyone using AI should fully understand the systems they are using, the decision-making processes used by the system and its ramifications. Individuals should be told when a decision regarding their rights, freedoms or benefits has been made by artificial intelligence, and most importantly, the explanation should be made in a way that makes it comprehensible.

Responsibility and Accountability

This is the whistleblower principle, that covers audit and due diligence as well as protection for whistleblowers to make sure that someone is responsible and accountable for the decisions made by, and use of, AI. Governance should be put in place around the ethical and legal responsibility of humans for any AI-based decisions. Any of these decisions that cause harm should be investigated and action taken.

Inclusivity and participation

Just as in any other area of business, when designing, deploying and using artificial intelligence systems, an inclusive, interdisciplinary and participatory approach should be taken, which also includes gender equality. Stakeholders and any communities that are affected should be informed and consulted and informed of any benefits and potential risks.

Building your AI integration around these central pillars should help you feel reassured that your entry into AI integration is built on an ethical and solid foundation.

Photo by Immo Wegmann on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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sbscglobal · 10 months ago

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Welcome to the digital era, where data reigns as the new currency.

In modern information technology, the term “Big Data” has surged to the forefront, embodying the exponential growth and availability of data in today’s digital age. This influx of data encompasses vast volumes, generated at unprecedented speeds and with diverse varieties, presenting both challenges and opportunities across industries worldwide.

To unlock the true potential of big data, businesses need to address several critical areas like #BigDataCollection and #DataIntegration, #DataStorage and Management, #DataAnalysis and #DataAnalytics, #DataPrivacy and #DataSecurity, Innovation and Product Development, Operational Efficiency and Cost Optimization. Here at SBSC we recognize the transformative power of #bigdata and empower businesses to unlock its potential through a comprehensive suite of services: #DataStrategy and #Consultation: SBSC’s Tailored advisory services help businesses define their Big Data goals, develop a roadmap, and align data initiatives with strategic objectives.

#DataArchitecture and #DataIntegration: We Design and implementation of scalable, robust data architectures that support data ingestion, storage, and integration from diverse sources. #DataWarehousing and Management: SBSC provides Solutions for setting up data warehouses or data lakes, including management of structured and unstructured data, ensuring accessibility and security. Data Analytics and Business Intelligence: Advanced analytics capabilities leveraging machine learning, AI algorithms, and statistical models to derive actionable insights and support decision-making.

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Contact SBSC to know the right services you need for your Business

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#Big Data Collection #big data #Cloud Services Consultation #Data Warehousing #Data Strategy #Data Storage #Data Security #Data Privacy #Data Integration #Data Architecture #Data Analysis

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