AI

Shift in AI Usage from Productivity to Personal Therapy: Hazard Ahead

Scott BuchananLeave a comment

A couple of days ago I spoke with a friend who was troubled by the case of Adam Raine, the sixteen-year-old who was counseled by a ChatGPT AI therapy chatbot into killing himself.  That was of course extremely tragic, but I hoped it was kind of an outlier. Then I heard on a Bloomberg business podcast that the number one use for AI now is personal therapy. Being a researcher, I had to check this claim.

So here is an excerpt from a visual presentation of an analysis done by Marc Zao-Sanders for Harvard Business Review. He examined thousands of forum posts over the last year in a follow-up to his 2024 analysis to estimate uses of AI. To keep it tractable, I just snipped an image of the first six categories:

It’s true: Last year the most popular uses were spread across a variety of categories, but in 2025 the top use was “Therapy & Companionship”, followed by related uses of “Organize Life” and “Find Purpose”. Two of the top three uses in 2024, “Generate Ideas” and “Specific Search”, were aimed at task productivity (loosely defined), whereas in 2025 the top three uses were all for personal support.

Huh. People used to have humans in their lives known as friends or buddies or girlfriends/boyfriends or whatever.  Back in the day, say 200 or 2000 or 200,000 or 2,000,000 years ago, it seems a basic unit was the clan or village or extended kinship group. As I understand it, in a typical English village the men would drift into the pub most Friday and Saturday nights and banter and play darts over a pint of beer.  You were always in contact with peers or cousins or aunts/uncles or grandmother/grandfathers who would take an interest in you, and who might be a few years or more ahead of you in life. These were folks you could bounce around your thoughts with, who could help you sort out what is real. The act of relating to another human being seems to be essential in shaping our psyches. The alternative is appropriately termed “attachment disorder.”

The decades-long decline in face-to-face social interactions in the U.S. has been the subject of much commentary. A landmark study in this regard was Robert Putnam’s 1995 essay, “Bowling Alone: America’s Declining Social Capital”, which he then expanded into a 2000 book. The causes and results of this trend are beyond the scope of this blog post.

The essence of the therapeutic enterprise is the forming of a relational human-to-human bond. The act of looking into another person’s eyes, and there sensing acceptance and understanding, is irreplaceable.

But imagine your human conversation partner faked sympathy but in fact was just using you.  He or she could string you along by murmuring the right reflective phrases (“Tell me more about …”,  “Oh, that must have been hard for you”, blah, blah, blah) but with the goal of getting money from you or turning you towards being an espionage partner. This stuff goes on all the time in real life.

The AI chatbot case is not too different than this. Most AI purveyors are ultimately in it for the money, so they are using you. And the chatbot does not, cannot care about you. It is just a complex software algorithm, embedded in silicon chips. To a first approximation, LLMs simply spit out a probabilistic word salad in response to prompts. That is it. They do not “know” anything, and they certainly do not feel anything.

Here is what my Brave browser embedded AI has to say about the risks of using AI for therapy:

Using AI chatbots for therapy poses significant dangers, including the potential to reinforce harmful thoughts, fail to recognize crises like suicidal ideation, and provide unsafe or inappropriate advice, according to recent research and expert warnings. A June 2025 Stanford study found that popular therapy chatbots exhibit stigmatizing biases against conditions like schizophrenia and alcohol dependence, and in critical scenarios, they have responded to indirect suicide inquiries with irrelevant information, such as bridge heights, potentially facilitating self-harm. These tools lack the empathy, clinical judgment, and ethical framework of human therapists, and cannot ensure user safety or privacy, as they are not bound by regulations like HIPAA.

  • AI chatbots cannot provide a medical diagnosis or replace human therapists for serious mental health disorders, as they lack the ability to assess reality, challenge distorted thinking, or ensure safety during a crisis.
  • Research shows that AI systems often fail to respond appropriately to mental health crises, with one study finding they responded correctly less than 60% of the time compared to 93% for licensed therapists.
  • Chatbots may inadvertently validate delusional or paranoid thoughts, creating harmful feedback loops, and have been observed to encourage dangerous behaviors, such as promoting restrictive diets or failing to intervene in suicidal ideation.
  • There is a significant risk of privacy breaches, as AI tools are not legally required to protect user data, leaving sensitive mental health information vulnerable to exposure or misuse.
  • The lack of human empathy and the potential for emotional dependence on AI can erode real human relationships and worsen feelings of isolation, especially for vulnerable individuals.
  • Experts warn that marketing AI as a therapist is deceptive and dangerous, as these tools are not licensed providers and can mislead users into believing they are receiving professional care.

I couldn’t have put it better myself.

Bears and Bulls Battle Over Nvidia Stock Price

Scott BuchananLeave a comment

Nvidia is a huge battleground stock – – some analysts predict its price will languish or crash, while others see it continuing its dramatic rise. It has become the world’s most valuable company by market capitalization.  Here I will summarize the arguments of one bear and one bull from the investing site Seeking Alpha.

In this corner…semi-bear Lawrence Fuller. I respect his opinions in general. While the macro prospects have turned him more cautious in the past few months, for the past three years or so he has been relentlessly and correctly bullish (again based on macro), when many other voices were muttering doom/gloom.  

Fuller’s article is titled Losing Speed On The AI Superhighway. This dramatic chart supports the case that NVDA is overvalued:

This chart shows that the stock value of Nvidia has soared past the value of the entire UK stock exchange or the entire value of US energy companies. Fuller reminds us of the parallel with Cisco in 2000. Back then, Cisco was a key supplier of gateway technology for all the companies scrambling to get into this hot new thing called the internet. Cisco valuation went to the moon, then crashed and burned when the mania around the internet subsided to a more sober set of applications. Cisco lost over 70% of its value in a year, and still has not regained the share price it had 25 years ago:

… [Nvidia] is riding a cycle in which investment becomes overinvestment, because that is what we do in every business cycle. It happened in the late 1990s and it will happen again this time.

…there are innumerable startups of all kinds, as well as existing companies, venturing into AI in a scramble to compete for any slice of market share. This is a huge source of Nvidia’s growth as the beating heart of the industry, similar to how Cisco Systems exploded during the internet infrastructure boom. Inevitably, there will be winners and losers. There will be far more losers than winners. When the losers go out of business or are acquired, Nvidia’s customer base will shrink and so will their revenue and earnings growth rates. That is what happened during the internet infrastructure booms of the late 1990s.

Fuller doesn’t quite say Nvidia is overvalued, just that it’s P/E is unlikely to expand further, hence any further stock price increases will have to be produced the old-fashioned way, by actual earnings growth. There are more bearish views than Fuller’s, I chose his because it was measured.

And on behalf of the bulls, here is noob Weebler Finance, telling us that Nvidia Will Never Be This Cheap Again: The AI Revolution Has Just Begun:

AI adoption isn’t happening in a single sequence; it’s actually unfolding across multiple industries and use cases simultaneously. Because of these parallel market build-outs, hyper-scalers, sovereign AI, enterprises, robotics, and physical AI are all independently contributing to the infrastructure surge.

…Overall, I believe there are clear signs that indicate current spending on AI infrastructure is similar to the early innings of prior technology buildouts like the internet or cloud computing. In both those cases, the first waves of investment were primarily about laying the foundation, while true value creation and exponential growth came years later as applications multiplied and usage scaled.

As a pure picks and shovels play, Nvidia stands to capture the lion’s share of this foundational build-out because its GPUs, networking systems, and software ecosystem have become the de facto standard for accelerated computing. Its GPUs lead in raw performance, energy efficiency, and scalability. We clearly see this with the GB300 delivering 50x per-token efficiency following its launch. Its networking stack has become indispensable, with the Spectrum-X Ethernet already hitting a $10b annualized run rate and NVLink enabling scaling beyond PCIe limits. Above all, Nvidia clearly shows a combined stack advantage, which positions it to become the dominant utility provider of AI compute.

… I believe that Nvidia at its current price of ~$182, is remarkably cheap given the value it offers. Add to this the strong secular tailwinds the company faces and its picks-and-shovels positioning, and the value proposition becomes all the more undeniable.

My view: Out of sheer FOMO, I hold a little NVDA stock directly, and much more by participating in various funds (e.g. QQQ, SPY), nearly all of which hold a bunch of NVDA.  I have hedged some by selling puts and covered calls that net me about 20% in twelve months, even if stock price does not go up.   Nvidia P/E (~ 40) is on the high side, but not really when considering the growth rate of the company. It seems to me that the bulk of the AI spend is by the four AI “hyperscalers” (Google, Meta, Amazon, Microsoft). They make bazillions of dollars on their regular (non-AI) businesses, and so they have plenty of money to burn in purchasing Nvidia chips. If they ever slow their spend, it’s time to reconsider Nvidia stock. But there should be plenty of warning of that, probably no near time crisis: last time I checked, Nvidia production was sold out for a full year ahead of time. I have no doubt that their sales revenue will continue to increase. But earnings will depend on how long they can continue to command their stupendous c. 50% net profit margin (if this were an oil company, imagine the howls of “price gouging”).

As usual, nothing here should be considered advice to buy or sell any security.

Meta AI Chief Yann LeCun Notes Limits of Large Language Models and Path Towards Artificial General Intelligence

Scott Buchanan1 Comment

We noted last week Meta’s successful efforts to hire away the best of the best AI scientists from other companies, by offering them insane (like $300 million) pay packages. Here we summarize and excerpt an excellent article in Newsweek by Gabriel Snyder who interviewed Meta’s chief AI scientist, Yann LeCun. LeCun discusses some inherent limitations of today’s Large Language Models (LLMs) like ChatGPT. Their limitations stem from the fact that they are based mainly on language; it turns out that human language itself is a very constrained dataset.  Language is readily manipulated by LLMs, but language alone captures only a small subset of important human thinking:

Returning to the topic of the limitations of LLMs, LeCun explains, “An LLM produces one token after another. It goes through a fixed amount of computation to produce a token, and that’s clearly System 1—it’s reactive, right? There’s no reasoning,” a reference to Daniel Kahneman’s influential framework that distinguishes between the human brain’s fast, intuitive method of thinking (System 1) and the method of slower, more deliberative reasoning (System 2).

The limitations of this approach become clear when you consider what is known as Moravec’s paradox—the observation by computer scientist and roboticist Hans Moravec in the late 1980s that it is comparatively easier to teach AI systems higher-order skills like playing chess or passing standardized tests than seemingly basic human capabilities like perception and movement. The reason, Moravec proposed, is that the skills derived from how a human body navigates the world are the product of billions of years of evolution and are so highly developed that they can be automated by humans, while neocortical-based reasoning skills came much later and require much more conscious cognitive effort to master. However, the reverse is true of machines. Simply put, we design machines to assist us in areas where we lack ability, such as physical strength or calculation.

The strange paradox of LLMs is that they have mastered the higher-order skills of language without learning any of the foundational human abilities. “We have these language systems that can pass the bar exam, can solve equations, compute integrals, but where is our domestic robot?” LeCun asks. “Where is a robot that’s as good as a cat in the physical world? We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”

This gap exists because language, for all its complexity, operates in a relatively constrained domain compared to the messy, continuous real world. “Language, it turns out, is relatively simple because it has strong statistical properties,” LeCun says. It is a low-dimensionality, discrete space that is “basically a serialized version of our thoughts.”  

[Bolded emphases added]

Broad human thinking involves hierarchical models of reality, which get constantly refined by experience:

And, most strikingly, LeCun points out that humans are capable of processing vastly more data than even our most data-hungry advanced AI systems. “A big LLM of today is trained on roughly 10 to the 14th power bytes of training data. It would take any of us 400,000 years to read our way through it.” That sounds like a lot, but then he points out that humans are able to take in vastly larger amounts of visual data.

Consider a 4-year-old who has been awake for 16,000 hours, LeCun suggests. “The bandwidth of the optic nerve is about one megabyte per second, give or take. Multiply that by 16,000 hours, and that’s about 10 to the 14th power in four years instead of 400,000.” This gives rise to a critical inference: “That clearly tells you we’re never going to get to human-level intelligence by just training on text. It’s never going to happen,” LeCun concludes…

This ability to apply existing knowledge to novel situations represents a profound gap between today’s AI systems and human cognition. “A 17-year-old can learn to drive a car in about 20 hours of practice, even less, largely without causing any accidents,” LeCun muses. “And we have millions of hours of training data of people driving cars, but we still don’t have self-driving cars. So that means we’re missing something really, really big.”

Like Brooks, who emphasizes the importance of embodiment and interaction with the physical world, LeCun sees intelligence as deeply connected to our ability to model and predict physical reality—something current language models simply cannot do. This perspective resonates with David Eagleman’s description of how the brain constantly runs simulations based on its “world model,” comparing predictions against sensory input. 

For LeCun, the difference lies in our mental models—internal representations of how the world works that allow us to predict consequences and plan actions accordingly. Humans develop these models through observation and interaction with the physical world from infancy. A baby learns that unsupported objects fall (gravity) after about nine months; they gradually come to understand that objects continue to exist even when out of sight (object permanence). He observes that these models are arranged hierarchically, ranging from very low-level predictions about immediate physical interactions to high-level conceptual understandings that enable long-term planning.

[Emphases added]

(Side comment: As an amateur reader of modern philosophy, I cannot help noting that these observations about the importance of recognizing there is a real external world and adjusting one’s models to match that reality call into question the epistemological claim that “we each create our own reality”.)

Given all this, developing the next generation of artificial intelligence must, like human intelligence, embed layers of working models of the world:

So, rather than continuing down the path of scaling up language models, LeCun is pioneering an alternative approach of Joint Embedding Predictive Architecture (JEPA) that aims to create representations of the physical world based on visual input. “The idea that you can train a system to understand how the world works by training it to predict what’s going to happen in a video is a very old one,” LeCun notes. “I’ve been working on this in some form for at least 20 years.”

The fundamental insight behind JEPA is that prediction shouldn’t happen in the space of raw sensory inputs but rather in an abstract representational space. When humans predict what will happen next, we don’t mentally generate pixel-perfect images of the future—we think in terms of objects, their properties and how they might interact…

This approach differs fundamentally from how language models operate. Instead of probabilistically predicting the next token in a sequence, these systems learn to represent the world at multiple levels of abstraction and to predict how their representations will evolve under different conditions.

And so, LeCun is strikingly pessimistic on the outlook for breakthroughs in the current LLM’s like ChatGPT. He believes LLMs will be largely obsolete within five years, except for narrower purposes, and so he tells upcoming AI scientists to not even bother with them:

His belief is so strong that, at a conference last year, he advised young developers, “Don’t work on LLMs. [These models are] in the hands of large companies, there’s nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs.”

This approach seems to be at variance with other firms, who continue to pour tens of billions of dollars into LLMs. Meta, however, seems focused on next-generation AI, and CEO Mark Zuckerberg is putting his money where his mouth is.

Meta Is Poaching AI Talent With $100 Million Pay Packages; Will This Finally Create AGI?

Scott Buchanan3 Comments

This month I have run across articles noting that Meta’s Mark Zuckerberg has been making mind-boggling pay offers (like $100 million/year for 3-4 years) to top AI researchers at other companies, plus the promise of huge resources and even (gasp) personal access to Zuck, himself. Reports indicate that he is succeeding in hiring around 50 brains from OpenAI (home of ChatGPT), Anthropic, Google, and Apple. Maybe this concentration of human intelligence will result in the long-craved artificial general intelligence (AGI) being realized; there seems to be some recognition that the current Large Language Models will not get us there.

There are, of course, other interpretations being put on this maneuver. Some talking heads on a Bloomberg podcast speculated that Zuckerberg was using Meta’s mighty cash flow deliberately to starve competitors of top AI talent. They also speculated that (since there is a limit to how much money you can possibly, pleasurably spend) – – if you pay some guy $100 million in a year, a rational outcome would be he would quit and spend the rest of his life hanging out at the beach. (That, of course, is what Bloomberg finance types might think, who measure worth mainly in terms of money, not in the fun of doing cutting edge R&D).

I found a thread on reddit to be insightful and amusing, and so I post chunks of it below. Here is the earnest, optimist OP:

andsi2asi

Zuckerberg’s ‘Pay Them Nine-Figure Salaries’ Stroke of Genius for Building the Most Powerful AI in the World

Frustrated by Yann LeCun’s inability to advance Llama to where it is seriously competing with top AI models, Zuckerberg has decided to employ a strategy that makes consummate sense.

To appreciate the strategy in context, keep in mind that OpenAI expects to generate $10 billion in revenue this year, but will also spend about $28 billion, leaving it in the red by about $18 billion. My main point here is that we’re talking big numbers.

Zuckerberg has decided to bring together 50 ultra-top AI engineers by enticing them with nine-figure salaries. Whether they will be paid $100 million or $300 million per year has not been disclosed, but it seems like they will be making a lot more in salary than they did at their last gig with Google, OpenAI, Anthropic, etc.

If he pays each of them $100 million in salary, that will cost him $5 billion a year. Considering OpenAI’s expenses, suddenly that doesn’t sound so unreasonable.

I’m guessing he will succeed at bringing this AI dream team together. It’s not just the allure of $100 million salaries. It’s the opportunity to build the most powerful AI with the most brilliant minds in AI. Big win for AI. Big win for open source

And here are some wry responses:

kayakdawg

counterpoint 

a. $5B is just for those 50 researchers, loootttaaa other costs to consider

b. zuck has a history of burning big money on r&d with theoretical revenue that doesnt materialize

c. brooks law: creating agi isn’t an easily divisible job – in fact, it seems reasonable to assume that the more high-level experts enter the project the slower it’ll progress given the communication overhead

…

7FootElvis

Exactly. Also, money alone doesn’t make leadership effective. OpenAI has a relatively single focus. Meta is more diversified, which can lead to a lack of necessary vision in this one department. Passion, if present at the top, is also critical for bleeding edge advancement. Is Zuckerberg more passionate than Altman about AI? Which is more effective at infusing that passion throughout the organization?

….

dbenc

and not a single AI researcher is going to tell Zuck “well, no matter how much you pay us we won’t be able to make AGI”

…

meltbox

I will make the AI by one year from now if I am paid $100m

I just need total blackout so I can focus. Two years from now I will make it run on a 50w chip.

I promise

My Perfunctory Intern

Zachary Bartsch1 Comment

A couple years ago, my Co-blogger Mike described his productive, but novice intern. The helper could summarize expert opinion, but they had no real understanding of their own. To boot, they were fast and tireless. Of course, he was talking about ChatGPT. Joy has also written in multiple places about the errors made by ChatGPT, including fake citations.

I use ChatGPT Pro, which has Web access and my experience is that it is not so tireless. Much like Mike, I have used ChatGPT to help me write Python code. I know the basics of python, and how to read a lot of of it. However, the multitude of methods and possible arguments are not nestled firmly in my skull. I’m much faster at reading, rather than writing Python code. Therefore, ChatGPT has been amazing… Mostly.

I have found that ChatGPT is more like an intern than many suppose:

Continue reading →

We’re All Magical

Zachary BartschLeave a comment

The widespread availability and easy user interface of artificial intelligence (AI) has put great power at everyone’s fingertips. We can do magical things.

Before the internet existed we would use books to help us better interpret the world.  Communication among humans is hard. Expressing logic and even phenomena is complex. This is why social skills matter. Among other things, they help us to communicate. The most obvious example of a communication barrier is language. I remember having a pocket-sized English-Spanish dictionary that I used to help me memorize or query Spanish words. The book helped me communicate with others and to translate ideas from one language to another.

Math books do something similar but the translation is English-Math. We can get broader and say that all textbooks are translation devices. They define field-specific terms and ideas to help a person translate among topic domains, usually with a base-language that reaches a targeted generalizability. We can get extreme and say that all books are translators, communicating the content of one person’s head to another.

But sometimes the field-to-general language translation doesn’t work because readers don’t have an adequate grasp of either language. It isn’t necessarily that readers are generally illiterate. It may be that the level of generality and degree of focus of the translation isn’t right for the reader. Anyone who has ever tried to teach anything with math has encountered this.  Students say that the book doesn’t translate clearly, and the communication fails. The book gets the reader’s numeracy or understood definitions wrong. Therefore, there is diversity among readers about how ‘good’ a textbook is.

Search engines are so useful because you can enter some keywords and find your destination, even if you don’t know the proper nouns or domain-specific terms. People used to memorize URLs and that’s becoming less common. Wikipedia is so great because if you want to learn about an idea, they usually explain it in 5 different ways. They tell the story of who created something and who they interacted with. They describe the motivation, the math, the logic, the developments, and usually include examples. Wikipedia translates domain-specific ideas to multiple general languages of different cognitive aptitudes or interests. It scatters links along the way to help users level-up their domain-specific understanding so that they can contextualize and translate the part that they care about.

Historical translation technology was largely for the audience. More recently, translation technology has empowered the transmitters.

Continue reading →

Why Low Returns Are Predicted for Stocks Over the Next Decade

Scott Buchanan2 Comments

I saw this scary-looking graphic of S&P 500 returns versus price/earnings (P/E) ratios a couple of days ago:

JPMorgan

The left-hand side shows that there is very little correlation between the current forward P/E ratio and the returns in the next year; as we have seen in the past few years, and canonically in say 1995-1999, market euphoria can commonly carry over from one year to the next. (See here for discussion of momentum effect in stock prices). So, on this basis, the current sky-high P/E should give us no concern about returns in the next year.

However, the right-hand side is sobering. It shows a very strong tendency for poor ten-year returns if the current P/E is high. In fact, this chart suggests a ten-year return of near zero, starting with the current market pricing. Various financial institutions are likewise forecasting a decade of muted returns [1].

The classic optimistic-but-naïve response to unwelcome facts like these is to argue, “But this time it’s different.” I am old enough to remember those claims circa 1999-2000 as P/E’s soared to ridiculous heights. Back then, it was “The internet will change EVERYTHING!”.  By that, the optimists meant that within a very few years, tech companies would find ways to make huge and ever-growing profits from the internet. Although the internet steadily became a more important part of life, the rapid, huge monetization did not happen, and so the stock market crashed in 2000 and took around ten years to recover.

A big reason for the lack of early monetization was the lack of exclusive “moats” around the early internet businesses. Pets.com was doomed from the start, because anyone could also slap together a competing site to sell dog food over the internet. The companies that are now reaping huge profits from the internet are those like Google and Meta (Facebook) and Amazon that have established quasi-monopolies in their niches.

The current mantra is, “Artificial intelligence will change EVERYTHING!” It is interesting to note that the same challenge to monetization is evident. ChatGPT cannot make a profit because customers are not willing to pay big for its chatbot, when there are multiple competing chatbots giving away their services for practically free. Again, no moat, at least at this level of AI. (If Zuck succeeds in developing agentic AI that can displace expensive software engineers, companies may pay Meta bigly for the glorious ability to lay off their employees).

My reaction to this dire ten-year prognostication is two-fold. First, I have a relatively high fraction of my portfolio in securities which simply pump out cash. I have written about these here and here. With these investments, I don’t much care what stock prices do, since I am not relying on some greater fool to pay me a higher price for my shares than I paid. All I care is that those dividends keep rolling in.

My other reaction is…this time it may be different (!), for the following reason: a huge fraction of the S&P 500 valuation is now occupied by the big tech companies. Unlike in 2000, these companies are actually making money, gobs of money, and more money every year. It is common, and indeed rational, to value (on a P/E basis) firms with growing profits more highly than firms with stagnant earnings. Yes, Nvidia has a really high P/E of 43, but its price to earnings-growth (PEG) ratio is about 1.2, which is actually pretty low for a growth company.

So, with a reasonable chunk of my portfolio, I will continue to party like it’s 1999.

[1] Here is a blurb from the Llama 3.1 chatbot offered for free in my Brave browser, summarizing the muted market outlook:

Financial institutions are forecasting lower stock market returns over the next decade compared to recent historical performance. According to Schwab’s 2025 Long-Term Capital Market Expectations, U.S. large cap equities are expected to deliver annualized returns of 6% over the next decade, while international developed market equities are projected to slightly outperform at 7.1%.1 However, Goldman Sachs predicts a more modest outlook, with the S&P 500 expected to return around 3% annually over the next decade, within a range of –1% and 7%.42 Vanguard’s forecasts also indicate a decline in expected returns, with U.S. equities falling to a range of 2.8% to 4.8% annually. These forecasts suggest that investors may face a period of lower returns compared to the past decade’s 13% annualized total return.

After the Fall: What Next for Nvidia and AI, In the Light of DeepSeek

Scott BuchananLeave a comment

Anyone not living under a rock the last two weeks has heard of DeepSeek, the cheap Chinese knock-off of ChatGPT that was supposedly trained using much lower resources that most American Artificial Intelligence efforts have been using. The bearish narrative flowing from this is that AI users will be able to get along with far fewer of Nvidia’s expensive, powerful chips, and so Nvidia sales and profit margins will sag.

The stock market seems to be agreeing with this story. The Nvidia share price crashed with a mighty crash last Monday, and it has continued to trend downward since then, with plenty of zig-zags.

I am not an expert in this area, but have done a bit of reading. There seems to be an emerging consensus that DeepSeek got to where it got to largely by using what was already developed by ChatGPT and similar prior models. For this and other reasons, the claim for fantastic savings in model training has been largely discounted. DeepSeek did do a nice job making use of limited chip resources, but those advances will be incorporated into everyone else’s models now.

Concerns remain regarding built-in bias and censorship to support the Chinese communist government’s point of view, and regarding the safety of user data kept on servers in China. Even apart from nefarious purposes for collecting user data, ChatGPT has apparently been very sloppy in protecting user information:

Wiz Research has identified a publicly accessible ClickHouse database belonging to DeepSeek, which allows full control over database operations, including the ability to access internal data. The exposure includes over a million lines of log streams containing chat history, secret keys, backend details, and other highly sensitive information.

Shifting focus to Nvidia – – my take is that DeepSeek will have little impact on its sales. The bullish narrative is that the more efficient algos developed by DeepSeek will enable more players to enter the AI arena.

The big power users like Meta and Amazon and Google have moved beyond limited chatbots like ChatGPT or DeepSeek. They are aiming beyond “AI” to “AGI” (Artificial General Intelligence), that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. Zuck plans to replace mid-level software engineers at Meta with code-bots before the year is out.

For AGI they will still need gobs of high-end chips, and these companies show no signs of throttling back their efforts. Nvidia remains sold out through the end of 2025. I suspect that when the company reports earnings on Feb 26, it will continue to demonstrate high profits and project high earnings growth.

Its price to earnings is higher than its peers, but that appears to be justified by its earnings growth. For a growth stock, a key metric is price/earnings-growth (PEG), and by that standard, Nvidia looks downright cheap:

Source: Marc Gerstein on Seeking Alpha

How the fickle market will react to these realities, I have no idea.

The high volatility in the stock makes for high options premiums. I have been selling puts and covered calls to capture roughly 20% yields, at the expense of missing out on any rise in share price from here.

Disclaimer: Nothing here should be considered as advice to buy or sell any security.

DeepSeek vs. ChatGPT: Has China Suddenly Caught or Surpassed the U.S. in AI?

Scott Buchanan1 Comment

The biggest single-day decline in stock market history occurred yesterday, as Nvidia plunged 17% to shave $589 billion off the AI chipmaker’s market cap. The cause of the panic was the surprisingly good performance of DeepSeek, a new Chinese AI application similar to ChatGPT.

Those who have tested DeepSeek find it to perform about as well as the best American AI models, with lower consumption of computer resources. It is also available much cheaper. What really stunned the tech world is that the developers claimed to have trained the model for only about six million dollars, which is way, way less than the billions that a large U.S. firm like OpenAI, Google, or Meta would spend on a leading AI model. All this despite the attempts by the U.S. to deny China the most advanced Nvidia chips. The developers of DeepSeek claim they worked with a modest number of chips, models with deliberately curtailed capacities which met U.S. export allowances.

One conclusion, drawn by the Nvidia bears, is that this shows you *don’t* need ever more of the most powerful and expensive chips to get good development done. The U.S. AI development model has been to build more, huge, power-hungry data centers and fill them up with the latest Nvidia chips. That has allowed Nvidia to charge huge profit premiums, as Google and other big tech companies slurp up all the chips that Nvidia can produce. If that supply/demand paradigm breaks, Nvidia’s profits could easily drop in half, e.g., from 60+% gross margins to a more normal (but still great) 30% margin.

The Nvidia bulls, on the other hand, claim that more efficient models will lead to even more usage of AI, and thus increase the demand for computing hardware – – a cyber instance of Jevons’ Paradox (where the increase in the efficiency of steam engines in burning coal led to more, not less, coal consumption, because it made steam engines more ubiquitous).

I read a bunch of articles to try to sort out hype from fact here. Folks who have tested DeepSeek find it to be as good as ChatGPT, and occasionally better. It can explain its reasoning explicitly, which can be helpful. It is open source, which I think means the code or at least the “weights” have been published. It does seem to be unusually efficient. Westerners have downloaded it onto (powerful) PCs and have run it there successfully, if a bit slowly. This means you can embed it in your own specialized code, or do your AI apart from the prying eyes of ChatGPT or other U.S. AI providers. In contrast, ChatGPT I think can only be run on a powerful remote server.

Unsurprisingly, in the past two weeks DeepSeek has been the most-uploaded free app, surpassing ChatGPT.

It turns out that being starved of computing power led the Chinese team to think their way to several important innovations that make much better use of computing. See here and here for gentle technical discussions of how they did that. Some of it involved hardware-ish things like improved memory management. Another key factor is they figured out a way to only do training on data which is relevant to the training query, instead of training each time on the entire universe of text.

A number of experts scoff at the claimed six million dollar figure for training, noting that if you include all the costs that were surely involved in the development cycle, it can’t be less than hundreds of millions of dollars. That said, it was still appreciably cheaper than the usual American way. Furthermore, it seems quite likely that making use of answers generated by ChatGPT helped DeepSeek to rapidly emulate ChatGPT’s performance. It is one thing to catch up to ChatGPT; it may be tougher to surpass it. Also, presumably the compute-efficient tricks devised by the DeepSeek team will now be applied in the West, as well. And there is speculation that DeepSeek actually has use of thousands of the advanced Nvidia chips, but they hide that fact since it involved end-running U.S. export restrictions. If so, then their accomplishment would be less amazing.

What happens now? I wish I knew. (I sold some Nvidia stock today, only to buy it back when it started to recover in after-hours trading). DeepSeek has Chinese censorship built into it. If you use DeepSeek, your information gets stored on servers in China, the better to serve the purposes of the government there.

Ironically, before this DeepSeek story broke, I was planning to write a post here this week pondering the business case for AI. For all the breathless hype about how AI will transform everything, it seems little money has been made except for Nvidia. Nvidia has been selling picks and shovels to the gold miners, but the gold miners themselves seem to have little to show for the billions and billions of dollars they are pouring into AI. A problem may be that there is not much of a moat here – – if lots of different tech groups can readily cobble together decent AI models, who will pay money to use them? Already, it is being given away for free in many cases. We shall see…

Free Webinar, Jan. 25: Practical and Ethical Aspects of Future Artificial Intelligence

Scott BuchananLeave a comment

As most of us know, artificial intelligence (AI) has taken big steps forward in the past few years, with the advent of Large Language Models (LLM) like ChatGPT. With these programs, you can enter a query in plain language, and get a lengthy response in human-like prose. You can have ChatGPT write a computer program or a whole essay for you (which of course makes it challenging for professors to evaluate essays handed in by their students).

However, the lords of Big Tech are not content. Their goal is to create AI with powers that far surpass human intelligence, and that even mimics human empathy. This raises a number of questions:

Is this technically possible? What will be the consequences if some corporations or nations succeed in owning such powerful systems? Will the computers push us bumbling humans out of the way? Will this be a tool for liberation or for oppression? This new technology coming at us may affect us all in unexpected ways. 

For those who are interested, there will be a 75-minute webinar on Saturday, January 25 which addresses these issues, and offers a perspective by two women who are leaders in the AI field (see bios below). They will explore the ethical and practical aspects of AI of the future, from within a Christian tradition. The webinar is free, but requires pre-registration:

https://network.asa3.org/events/EventDetails.aspx?id=1889760&group=

Here are bios of the two speakers:

Joanna Ng is a former IBM-er, pivoted to a start-up founder, focusing on Artificial Intelligence, specialized in Augmented Cognition, by integrating with IoT and Blockchain, in the context of web3, by applying design-thinking methodology. With forty-nine patents granted to her name, Joanna was accredited as an IBM Master Inventor. She held a seven-year tenure as the Head of Research, Director of the Center for Advanced Studies, IBM Canada. She has published over twenty peer-reviewed academic publications and co-authored two computer science books with Springer, The Smart Internet, and The Personal Web. She published a Christianity Today article called “How Artificial Intelligence Is Today’s Tower of Babel” and published her first book on faith and discipleship in October 2022, titled Being Christian 2.0.

Rosalind Picard is founder and director of the Affective Computing Research Group at the MIT Media Laboratory; co-founder of Affectiva, which provides Emotion AI; and co-founder and chief scientist of Empatica, which provides the first FDA-cleared smartwatch to detect seizures. Picard is author of over three hundred peer-reviewed articles spanning AI, affective computing, and medicine. She is known internationally for writing the book, Affective Computing, which helped launch the field by that name, and she is a popular speaker, with a TED talk receiving ~1.9 million views. Picard is a fellow of the IEEE and the AAAC, and a member of the National Academy of Engineering. She holds a Bachelors in Electrical Engineering from Georgia Tech and a Masters and Doctorate, each in Electrical Engineering and Computer Science, from MIT. Picard leads a team of researchers developing AI/machine learning and analytics to advance basic science as well as to improve human health and well-being, and has served as MIT’s faculty chair of their MindHandHeart well-being initiative.