I feel like the Microsoft interest in DeepSeek is such a signal. Many valley tech companies are already building their AI on open source, if Microsoft jumps in and everyone figures out that running V4 natively is significantly cheaper, and the actual performance difference is negligible (I’m sure whilst Fable 5 was available, it could do awesome thing things, however 99.9% of my use case was covered with mostly Sonnet and a bit of Opus), why WOULDNT you do it.
Made even worse by one of the biggest data centre pushers themselves realising maybe it’s not all necessary, could finally change things.
Google has 13 products with over a billion users. Many produce cash, cash and more cash. OpenAI has an albotross around its neck. Anthropic found THE niche, the question is will it matter
Especially in about 12 months when ever model basically is sonnet 4.6 or better in quality. People will choose the cheapest.
I don't think either OpenAI or Anthropic have a future - Anthropic almost certainly has almost as bad numbers. I strongly suspect neither will actually IPO and I think your estimate of 30c on the dollar of acquisition cost is high. It'll be more of a bankruptcy where Amazon, Microsoft and Oracle split the carcass for the nominal price of the compute commitments both have made to them
Great roundup, Gary! I’ll add the (unsurprising to those here) 404 Media coverage of a paper showing that bad actors can poison deep learning results with just 13 words in a Reddit post. AEO is the new SEO and it’s even more sus, but no one can ignore the chaos.
I have to say, Microsoft considering DeepSeek (I assume they'll end up being pressured by the Trump regime to not do that, but use another US model) sends a message to all the Anthropic fanbois who continue to say "I have to spend gobs of money because Opus is SO GOOD!" - without ever actually having tried the top of the line open weight models like the new Kimi K2 Coder.
As an aside, I posted this comment in a Note earlier:
QUOTE:
I’m still not sure why “reasoning over large codebases” is actually all that. Humans don’t do it well, but then they tend not to do it at all if it can be avoided. Instead, humans zeros in on the section of a large codex that is already understood to be important, based on a CONCEPTUAL understanding - not a TEXT understanding - of the entire codex.
Conceptual understanding is exactly what LLMS don’t do at all. let alone well. So instead, they call it “context” - and that’s why we have LLMs with a million token “contexts.”
END QUOTE
As I keep saying, "good enough" for cheap is good enough.
Also, note that Anthropic now being sued for false advertising on its token limits for its subscription plans. Which is directly related to the issue of cost:
Anthropic Sued Over Alleged False Advertising on Claude Max Subscription Usage Limits
The lawsuit claims that the AI company is misleading customers about its $200-per-month Claude plan.
Perhaps we are all missing the bigger signal. Local models are just as good as frontier models. There is zero difference between frontier models. All models are equally good at giving confident and authoritative answers even though the answers are incorrect. Which is often. Microsoft doesn’t want to pay for the tokens. Big surprise. If your tokens are throw away I don’t want to pay for them either. Nadella is telling the market that explicitly.
<… except to say that another dark horse bidder is Elon. Wouldn’t that be an interesting twist?>
It would be "revenge best served cold" for Elon after he lost his case against Altman. Of course he would be putting all that paper money in an even bigger furnace, but Elon probably doesn't care. If he can spin the SpaceX story as a stock worth 100x revenue, I'm sure he could find a way to make it seem that buying OpenAI at cents on the dollar was a smart business move.
I always felt that Google was in the best position to get the general public on AI. They simply have the best ecosystem by a longshot. Gmail is ubiquitous. Their office suite is fantastic. I'm not sure how their search abilities help their LLMs, but I assume they must, right???
All things being equal, if Google AI platforms are anywhere close to everyone else, then for the general public it is a no brainer to stay with Google.
I hope google will focus on offering specific targeted AI tasks to users. This is what the general population will actually use. We do not need platforms that can do everything, but rather those than can help us do the things we actually do.
Gary we get it! Current route bad other route not really defined. Any route that leads to true AGI, very dangerous. But it's not alternatives to us who can develop which you could most certainly suggest. Like promoting neurosymbolic AI. For instance suggesting people looking into PyReason and IBM Neuro-Symbolic AI Toolkit (NSTK) and not just running phi3:mini locally for instance. Though for fun you should see the cute thing phi3:mini wrote for me from the most misspelled prompt. Frankly, amazingly decent.
I use the current OpenAI version about 80% less, the strong guardrails they constructed distorts ordinary work so much it makes it infeasible. Fortunately 4.0 is still API available.
I finally found Claude limits, it is dramatically over-biased to recent data. Even for intensely online information, it struggles to construct coherent responses to sources before 1990, even in intensely computer-related topics. It’s a tool, not a library.
Gemini is generally poor, it’s quite funny that what elicits response from search is diametrically opposed to that which gets Gemini to give a good response. Now you get two crappy responses. Wheee.
Iterative harnesses are what make these systems intensely useful in work, a pendulum between non-deterministic and deterministic convergence, instead of drift. I have yet to see stable agents without a harness.
Harnesses will make utilization go through the roof - iterative refinement to a goal works extremely well.
My experiment today showed a single prompt can replicate commercial software from help files alone. A $1 prompt to Android can replace Adobe Photoshop.
Popping popcorn, watching the show.
Datacenter growth is slowing down. Bitcoin farms harvest has picked low-hanging fruit, and greenfield mega transformer lead time is now 4+ years. The pressure for neuromorphic will increase geometrically due to energy consumption bottlenecks.
A signal will be clear that the longer-term structuring of the LLM frontier players with other parts of the AI industrial complex is starting is "if" the OpenAI IPO is delayed. It is becoming clear that for some period of time as token-based pricing enters the market (and raises the price of AI services) there will be an over-capacity (or insufficient demand to keep prices up) in the market. The question will quickly become; how do we (players in the industry) address this? If countries in the EU, Britain and other countries proceed to build their own native LLM systems, then the growth anticipated from those places will hit the U.S. players expansion plans hard. Growth and M&A strategies will need to be revised.
The majority of my work is done with claude code (opus), while personally I use gemini because it already has access to decades of data on me. As for chatgpt, I still use it for agentic -p loops because anthropic continues to send mixed signals on how that will be billed (they just changed their stance on that again yesterday).
Opus is pretty great but it's annoying Fable was removed because my initial work with it was impressive...
I feel like the Microsoft interest in DeepSeek is such a signal. Many valley tech companies are already building their AI on open source, if Microsoft jumps in and everyone figures out that running V4 natively is significantly cheaper, and the actual performance difference is negligible (I’m sure whilst Fable 5 was available, it could do awesome thing things, however 99.9% of my use case was covered with mostly Sonnet and a bit of Opus), why WOULDNT you do it.
Made even worse by one of the biggest data centre pushers themselves realising maybe it’s not all necessary, could finally change things.
Google has 13 products with over a billion users. Many produce cash, cash and more cash. OpenAI has an albotross around its neck. Anthropic found THE niche, the question is will it matter
Especially in about 12 months when ever model basically is sonnet 4.6 or better in quality. People will choose the cheapest.
Don't you mean an AIbotross?
(Oh, if only Substack let us have serif fonts, then AIbotross and Albotross Wouldn't look the same.)
I don't think either OpenAI or Anthropic have a future - Anthropic almost certainly has almost as bad numbers. I strongly suspect neither will actually IPO and I think your estimate of 30c on the dollar of acquisition cost is high. It'll be more of a bankruptcy where Amazon, Microsoft and Oracle split the carcass for the nominal price of the compute commitments both have made to them
I wrote about the contrast between SpaceX and the other two here - https://ombreolivier.substack.com/p/one-ipo-achieved-two-flame-outs-likely?r=7yrqz - and go into more detail as to why I don't think those IPOs will happen, but no moat and no road to cashflow positive is the TL;DR
Great roundup, Gary! I’ll add the (unsurprising to those here) 404 Media coverage of a paper showing that bad actors can poison deep learning results with just 13 words in a Reddit post. AEO is the new SEO and it’s even more sus, but no one can ignore the chaos.
Falling to 50% market share of a rapidly growing market is still possibly a good strategic place to be if they can adapt. "if".
I have to say, Microsoft considering DeepSeek (I assume they'll end up being pressured by the Trump regime to not do that, but use another US model) sends a message to all the Anthropic fanbois who continue to say "I have to spend gobs of money because Opus is SO GOOD!" - without ever actually having tried the top of the line open weight models like the new Kimi K2 Coder.
As an aside, I posted this comment in a Note earlier:
QUOTE:
I’m still not sure why “reasoning over large codebases” is actually all that. Humans don’t do it well, but then they tend not to do it at all if it can be avoided. Instead, humans zeros in on the section of a large codex that is already understood to be important, based on a CONCEPTUAL understanding - not a TEXT understanding - of the entire codex.
Conceptual understanding is exactly what LLMS don’t do at all. let alone well. So instead, they call it “context” - and that’s why we have LLMs with a million token “contexts.”
END QUOTE
As I keep saying, "good enough" for cheap is good enough.
Also, note that Anthropic now being sued for false advertising on its token limits for its subscription plans. Which is directly related to the issue of cost:
Anthropic Sued Over Alleged False Advertising on Claude Max Subscription Usage Limits
The lawsuit claims that the AI company is misleading customers about its $200-per-month Claude plan.
https://www.cnet.com/tech/services-and-software/anthropic-sued-alleged-false-advertising-claude-max-subscription-usage-limits/
Perhaps we are all missing the bigger signal. Local models are just as good as frontier models. There is zero difference between frontier models. All models are equally good at giving confident and authoritative answers even though the answers are incorrect. Which is often. Microsoft doesn’t want to pay for the tokens. Big surprise. If your tokens are throw away I don’t want to pay for them either. Nadella is telling the market that explicitly.
<… except to say that another dark horse bidder is Elon. Wouldn’t that be an interesting twist?>
It would be "revenge best served cold" for Elon after he lost his case against Altman. Of course he would be putting all that paper money in an even bigger furnace, but Elon probably doesn't care. If he can spin the SpaceX story as a stock worth 100x revenue, I'm sure he could find a way to make it seem that buying OpenAI at cents on the dollar was a smart business move.
I always felt that Google was in the best position to get the general public on AI. They simply have the best ecosystem by a longshot. Gmail is ubiquitous. Their office suite is fantastic. I'm not sure how their search abilities help their LLMs, but I assume they must, right???
All things being equal, if Google AI platforms are anywhere close to everyone else, then for the general public it is a no brainer to stay with Google.
I hope google will focus on offering specific targeted AI tasks to users. This is what the general population will actually use. We do not need platforms that can do everything, but rather those than can help us do the things we actually do.
What is it we are doing here? Just complaining about what other people do?
Gary we get it! Current route bad other route not really defined. Any route that leads to true AGI, very dangerous. But it's not alternatives to us who can develop which you could most certainly suggest. Like promoting neurosymbolic AI. For instance suggesting people looking into PyReason and IBM Neuro-Symbolic AI Toolkit (NSTK) and not just running phi3:mini locally for instance. Though for fun you should see the cute thing phi3:mini wrote for me from the most misspelled prompt. Frankly, amazingly decent.
alets write a storyu about a man you dines
phi3:mini
Once upon a time, in the quaint village of Larkspur Lane, there lived an elderly gentleman named
Oliver. His days were often spent wandering through fields and forests that hugged his home with
their natural beauty. However, on one particular sunny morning, something peculiar caught Oliver's
eye-a mysterious invitation lying at the entrance of a small café known as "Oliver'thai."
Intrigued by this unexpected surprise, he decided to dine there and find out what awaited him. As
he entered, his eyes fell upon an elegant couple already seated inside; they exuded warmth and
charm that instantly drew Oliver in. His name was called from behind the bar as a kindly server
handed over two menus with delicacies written on them—'Larkspur Lettuce Wraps, 'Savory Stuffed
Peppers,' or perhaps most intriguingly, 'Oliver's Omelette.'
Unable to contain himself any longer from the tantalizing aroma emanating from his table neighbor,
Oliver couldn't help but ask about their delectable dish. The gentleman replied with an inviting smile
and said he was named Alexander and hailed all the way up north as far away as Siberia before
retiring to Larkspur Lane due to a shared love of good food and nature walks, much like Oliver
himself.
Over time, their friendship blossomed into something deeper-a connection born not just out of
mutual affection but also from sharing stories about life's most significant moments: first loves lost
too soon, unfulfilled dreams turned towards self-discovery and the joy found in simple pleasures
like a warm dish shared with friends.
Alexander would often visit Oliver for lunch or dinner at his small house beside Larkspur Lane while
listening to tales of their travels through life as they savored each morsel together. In turn, he
revealed that the mysterious invitation had come from him—a gesture meant purely out of kindness
and friendship towards an old friend who hadn't seen such delights in years due to distance
between them both since meeting back then at their village homecoming celebration when they
were children playing tag amidst cherry blossoms.
Amid the laughter, heartwarming conversations about life's ups and downs over sumptuous meals
prepared together with fresh local ingredients straight from nearby farms became an important part
of Oliver's weekly routine-a treasured bond that grew stronger day by day. And every time he
dined at the small café, now fondly referred to as "Oliver and Alexander's," his heart would
remember not just a simple invitation but also rediscovering cherished memories through friendship
made anew across oceans while indulging in delicious food crafted with love by talented chefs who
shared Oliver's appreciation for flavorful dishes infused with culture and history.
Thus, it came to be that the mysterious invitation led this humble village dweller down a path of
rediscovering life's simple pleasures-rekindling an old friendship through heartfelt conversations
while enjoying dellclous foop togetner at "Ullver's Omelette" and more. And every time he dihed out
in the future, it would serve as yet another fond memory for Oliver to cherish eternally like a
beautifully written storybook filled with love, laughter, flavorsome tales from lands far away, all over
one incredible meal shared between two old friends-once upon that sunny morning.
And they lived happily ever after in the village of Larkspur Lane and beyond as cherished memories
were made by dining at "Oliver's Omelette."
i
That's from a tiny LLM running on way old stuff for fun entirely locally. For what it is, fun.
I use the current OpenAI version about 80% less, the strong guardrails they constructed distorts ordinary work so much it makes it infeasible. Fortunately 4.0 is still API available.
I finally found Claude limits, it is dramatically over-biased to recent data. Even for intensely online information, it struggles to construct coherent responses to sources before 1990, even in intensely computer-related topics. It’s a tool, not a library.
Gemini is generally poor, it’s quite funny that what elicits response from search is diametrically opposed to that which gets Gemini to give a good response. Now you get two crappy responses. Wheee.
Iterative harnesses are what make these systems intensely useful in work, a pendulum between non-deterministic and deterministic convergence, instead of drift. I have yet to see stable agents without a harness.
Harnesses will make utilization go through the roof - iterative refinement to a goal works extremely well.
My experiment today showed a single prompt can replicate commercial software from help files alone. A $1 prompt to Android can replace Adobe Photoshop.
Popping popcorn, watching the show.
Datacenter growth is slowing down. Bitcoin farms harvest has picked low-hanging fruit, and greenfield mega transformer lead time is now 4+ years. The pressure for neuromorphic will increase geometrically due to energy consumption bottlenecks.
A signal will be clear that the longer-term structuring of the LLM frontier players with other parts of the AI industrial complex is starting is "if" the OpenAI IPO is delayed. It is becoming clear that for some period of time as token-based pricing enters the market (and raises the price of AI services) there will be an over-capacity (or insufficient demand to keep prices up) in the market. The question will quickly become; how do we (players in the industry) address this? If countries in the EU, Britain and other countries proceed to build their own native LLM systems, then the growth anticipated from those places will hit the U.S. players expansion plans hard. Growth and M&A strategies will need to be revised.
Guys. This would be the apocalypse of the entire AI industry and in fact tech and stock markets. Did you sell all your stocks?
The majority of my work is done with claude code (opus), while personally I use gemini because it already has access to decades of data on me. As for chatgpt, I still use it for agentic -p loops because anthropic continues to send mixed signals on how that will be billed (they just changed their stance on that again yesterday).
Opus is pretty great but it's annoying Fable was removed because my initial work with it was impressive...