2025-12-12 00:42:23
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I recently set out my macro view on the next 24 months of AI. The response was strong and many of you wrote in with questions. In this episode, I build on that analysis and answer your questions.
Some highlights:
(03:36) The biggest AI constraint right now
(10:43) Why mid-2026 is a crucial turning point
(18:41) The market’s reaction to OpenAI’s code red
(20:51) The best strategy for middling powers?
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2025-12-08 23:36:24
Hi all,
Here’s your Monday round-up of data driving conversations this week in less than 250 words.
Let’s go!
Agentic AI ↑ Orchestration systems (like Poetiq) significantly boost frontier-model performance on ARC-AGI-21.
VC screening ↑ LLMs cut venture capital deal screening time from 2 hours to ~13 seconds.
Underestimating competitors ↑ 93% of companies misjudge how quickly rivals are adopting AI and robotics.
Sourcing from China ↓ A third of the members of the European Chamber in China2 are looking to shift sourcing away from China due to tight export controls.
2025-12-07 10:59:49
I haven’t read anyone as thorough as you on the AI bubble and related topics. – Miguel O., a paying member
Hi all,
In this edition:
Superhuman persuasion. New data shows that AI is more effective than TV ads at changing minds, even when it lies.
The chip sanctions failed. How “stacking” old tech is neutralizing the US blockade (and validating my 2020 thesis).
Solving the unsolved. Terence Tao calls AI “routine” and autonomous agents are finally cracking the backlog of neglected science.
The end of Hollywood. Financialization, not AI, may be the root cause.
In my latest video, I break down how I think about the overlapping technology S-curves that are driving the market upheaval:
There are more exponentials in this AI wave doing their work than just ChatGPT and large language models. These new technologies make new things possible: we start to do things we didn’t do before, either because they weren’t possible or because they were too expensive.
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OpenRouter, an aggregator routing traffic across 300+ AI models for five million developers, released an analysis of 100 trillion tokens of usage. Their data offers a unique, cross-platform look at the market. It’s worth a deep read, but for now I’d highlight two directions:
First, the “glass slipper” effect – retention is driven by “first-to-solve,” not “first-to-market.” When a model is the first to unlock a specific, high-friction workload (like a complex reasoning chain), the cohort that adopts it shows effectively zero churn, even when cheaper or faster competitors emerge. This confirms my long-held view: customers don’t buy benchmarks; they buy solutions. Once a model fits the problem, like a glass slipper, switching costs become irrelevant.
Second, the shift to agentic inference is undeniable. In less than 12 months, reasoning-optimised models have surged from negligible to over 50% of all token volume. Consequently, average prompt lengths have quadrupled to over 6,000 tokens, while completion lengths have tripled. The insight here is that users aren’t becoming more verbose; the systems are. We are seeing the mechanical footprint of agentic loops iterating in the background.
China’s drive for semiconductor independence is accelerating faster than predicted. The recent Shanghai IPO of Moore Threads, a leading AI chipmaker, surged 425% on debut, signaling voracious domestic capital support for “China’s Nvidia” alternatives. This aligns with a bold forecast from Bernstein, that China is on track to produce more AI chips than it consumes by 2026, effectively neutralizing the intended chokehold of US export controls.
2025-12-06 01:22:57
In today’s session I reflected on why AI’s bottleneck is no longer the models but the systems expected to absorb them.
I followed with a Q&A that touched on
OpenAI’s competitive position
Where value will accrue in the stack
The role of energy and grid limits
The impact of cybersecurity risks
Enjoy!
Azeem
2025-12-05 21:37:25
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Yesterday, I unpacked the commercial reality behind Gemini’s release and OpenAI’s “Code Red.” Recent moves look defensive and could narrow OpenAI’s route to a $100 billion in revenues.
But past the immediate competition, we are seeing overlapping S-curves of technology rewriting the rules of our economy. In this video, I step back from the leaderboard to look at the transition from scale to reasoning, the “TAM fallacy” that is blinding investors, and the emergence of entirely new behaviours.
Skip to the best part:
(00:09) How ChatGPT became synonymous with AI
(11:46) The iPhone calculation that breaks everything
(16:38) The challenge of evaluating new markets
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2025-12-04 23:10:10
In February 2023, when ChatGPT had just hit 100 million users and launched its $20 premium tier, I ran the numbers on revenue potential. My napkin math pointed to monthly revenues of at least $60 million by year-end. Readers thought I was optimistic. OpenAI closed 2023 at $1.6 billion in annualized revenue and then tripled it the following year.
A month ago, I asked whether they could reach $100 billion in full year revenues by 2027. The math showed a plausible route: layered subscription momentum, enterprise API growth, international expansion, advertising and agentic systems. I concluded:
Is it possible? The mathematics says yes – barely, but yes.
Sam Altman’s declaration of a “code red” makes that ‘barely possible’ even more unlikely.
OpenAI remains the fastest-growing technology company in history, with revenues approaching $13 billion this year and 800 million weekly active users. But now Google, Anthropic and DeepSeek are pressuring OpenAI to choose between defending its core product and pursuing new revenue streams.
Altman seems to have chosen defense. That decision makes sense for product integrity. It also suggests the firm will delay several revenue streams underpinning our $100 billion scenario.
In our previous work, we showed you a scenario. The latest news changes our assumptions – here is a live update.
The Information reported that Altman declared a ‘code red,’ telling staff: “We are at a critical time for ChatGPT.” Google’s AI resurgence, he warned, could bring ‘temporary economic headwinds.’
Altman redirected engineering toward five priorities: personalization, image generation, model behavior, speed, and reliability. Advertising, AI agents and Pulse – automatic-feed experiment – now take a lower priority. The concern is that running ads while users doubt ChatGPT’s edge would push them toward good enough rivals (see my analysis of how Google came to own “good enough”). And “good enough” may understate the threat – on several benchmarks, Google and Anthropic’s models already outperform.
Gemini 3 Pro and Claude 4.5 have started to eat more and more into my AI conversations. ChatGPT is still dominant, but less than it was just three weeks ago. ChatGPT does retrain those queries where years of conversational history have made its context indispensable, a narrowing niche built on accumulated exchanges I can’t yet abandon.
I genuinely admired ChatGPT Pulse; its premise was exactly what I needed: telling ChatGPT my tracking priorities, receiving nightly intelligence briefs tailored to those domains, with offers to drill deeper. Yet execution stumbled on fundamentals and it didn’t hold me. After several weeks, the experience became monotonous and small friction points were a dealbreaker. But its fatal flaw was that Pulse has no sense of time. It recycled stale findings day after day, like a news feed for amnesiacs. I no longer open it regularly.
Meanwhile, our API and programmatic workflows rarely relied on OpenAI’s offering anyway. Gemini 2.5 Flash handles bulk processing. Claude’s API powers judgment calls: editing passes, analysis and code, although Gemini’s 3 Pro model is coming increasingly handy.
I doubt I’m an outlier. Last quarter’s numbers show my shift mirrors a broader migration across three competitive fronts:
Google has woken up. ChatGPT’s web traffic declined 6% in late November, correlating directly with the release of Gemini 3 Pro and competing models. Google’s Gemini grew from 350 million to 650 million monthly active users between March and October, with daily requests tripling quarter-over-quarter. Gemini added 100 million users in four months, then 200 million in three. A large part of this may be due to the memetic temptations of images generated by Nano Banana. But Nano Banana’s virality hasn’t exploded to the same scale as OpenAI’s “ghiblification” craze, yet Google’s spike feels more sustainable. Those user numbers will keep growing. But it’s not just Google…
Open-source alternatives are eating into OpenAI’s cost advantage. DeepSeek’s V3.2 matches GPT-5 performance on some benchmarks at a tenth of the cost, and parity at ~6x lower cost is no longer unusual, as we wrote in EV#551.
Focused competitors, best seen as Anthropic. Claude Opus 4.5 now leads in coding, agentic workflows and computer use. For enterprise buyers, those are the capabilities that write contracts.
Facing stiffer competition on both enterprise and consumer sides, OpenAI has made a trade-off to delay expansion into ads and agentic systems to protect the core products under siege.
Last month, I projected five revenue streams that could combine to reach $100 billion by 2027. This month’s code red announcement forces us to reassess each one:
Remove or halve the ad and agent revenue, and the math shifts. Our earlier scenario totaled $100 billion across five streams. Without ads and agents firing on schedule, what does that ceiling fall to? Perhaps $55-60 billion by 2027.
That remains extraordinary growth by any historical standard. It is not, however, $100 billion.
Of course, there is an astonishing paucity of information in this market right now. Is the “code red” just an internal kick in the pants to an already overworked AI team? Is the decline in ChatGPT’s usage actually linked to the launch of Gemini?
Or are we just seeing a typical seasonality? ChatGPT’s traffic has traditionally dipped around Black Friday, according to Coatue, an investor in both Anthropic and OpenAI.
If history is any guide, usage could pick up again in two to three weeks, and we’ll update our models once more.
OpenAI has rallied before – after Claude 3, after Gemini 1.5’s million-token context window, after DeepSeek’s efficiency leap. Each time, it answered. But now all three forces are surging at once.
OpenAI is already mounting its response. Chief research officer Mark Chen hinted at a model codenamed Garlic:
We have models internally that perform at the level of Gemini 3, and we’re pretty confident that we will release them soon and we can release successor models that are even better.
The upshot, he explained, was that OpenAI can now pack the same level of knowledge into a smaller model that previously required a much larger one. Smaller means faster to train, cheaper to serve and quicker to iterate, exactly the advantages you need when the incumbent is cutting prices and moving the goalposts.
But it would take a lot to undercut Google. The incumbent has woken up and is pulling everything into its gravity well. Its vertical integration allows it to better control inference and training costs; its deep balance sheet is fed by the $300 billion cash spigot that is its ad business. The search giant could cut prices longer than most rivals can bear.
When escaping an object as massive as Google, you need to find an angle, one that really distinguishes you from the competition, that is perhaps orthogonal to their gravitational field. Again, I go into detail on this in my analysis of Google.
I’m wondering whether OpenAI’s broad approach still makes sense – or if it needs a sharper differentiation from Google.
There are some signals emerging: the firm recently signed a deal with LSEG, a financial information group that includes the London Stock Exchange and Refinitiv’s market data business. This puts professional-level financial data directly into the ChatGPT workflow, particularly for LSEG’s existing customers. Deals with airlines like Emirates and Virgin Australia might presage deeper integration of ChatGPT into both inward-facing operations and, ultimately, the consumer-passenger experience. These tactics might yield the right kind of differentiation that would deepen user engagement and, with it, greater opportunities to monetize.
Will this be enough to turn off the “code red” and put the company in a shouting chance of the $100 billion scenario for 2027?
We’ll have a clear answer soon enough. I await Garlic with bad (bated) breath.
