2026-07-12 18:32:10
“With so much hype around the tech, your no-nonsense unbiased assesment is essential.” — RB, a paying member
Roberto Serrano, a professor at Brown, suspected his economics class was relying on ChatGPT to do their exams. He made the final paper a closed-book exam, and scores for 56 of his 59 students collapsed (by as much as 100%). Kudos to the two students who seem to work unaided.
This is a problem of incentives. The students appear to value the high score rather than mastering the subject. Perhaps because the value of a degree is increasingly less about intellectual excellence and more about job-market signalling.
The percentage of Americans who read for pleasure on any given day fell to 16% by 2023, down from 28% in 2004. Literacy, not a natural human state, is a learned skill that needs practice. Americans aren’t practising – postliteracy loves short-form video, after all. These data are from before the rise of generative AI.
Reading in America is a different problem. The data, even pre-AI, is terrible and doesn’t support the notion that post-literacy arrived after ChatGPT. Rather, the trend has been ongoing for at least two decades.
When access to intelligence is uncapped, AI could divide people based on our willingness to think and engage with what is difficult. David Brooks makes the case :
What really matters, therefore, is not brainpower but the willingness to run the mental marathons that produce high-quality results. […] The crucial task before us is to cultivate people’s desire to seek out cognitive complexity. How do we train people to see their life as a hero’s journey in which they take on difficult missions that they may fail at and that will certainly involve pain and suffering?
Reading long-form, constructed arguments that have been closely fought through by an author forces the reader to engage with the material far more deeply than a stream of summaries does. (These may give the illusion of thinking, but that isn’t the case.) I’ve been working on my second book over the past few months, and yes, R Mini Arnold, my agent, has been an extraordinarily helpful research associate. The latest AI models, Fable and GPT 5.6, can be prompted to produce outstanding (almost) research, but you really need to know what to ask and how to ask for it. In my case, that’s meant building up my mental map the old-fashioned way. Which means I need to sit quietly, read original material, consider it critically and handwrite my notes.
Elsewhere:
LinkedIn is awash with AI-generated posts; Substack is less so. I have built a Chrome extension that hides AI-generated content. It makes X more manageable to browse.
The University of Chicago Law School is piloting device-free first-year core classes and requires students to learn to use AI effectively.
“Fable is better than me at my job, but Fable alone would be a mediocre investor,” says one VC.
No, GPU demand is not softening. Silicon Data’s one-year H100 contract index bottomed near $1.70/hour last October and has since rebounded ~38% to $2.35. Spot prices are up 10% this year.
The SpaceX S1 offers some clues about how future demand might shape up as it discloses the infra deals the firm has signed: a three-year tenor with unusually permissive 90-day cancellation terms.
2026-07-06 21:44:31
Hi all,
Here’s our short Monday roundup of data signals across AI, energy and markets:
A GPU wave is ahead of us. More than 95% of the Grace-Blackwell GPUs have not yet been deployed, even though the chip has been shipping since December 2024.1 (h/t )
Freelance hunting. Fable 5 now completes 16% of real freelance projects at a quality just as good as that of human professionals, according to the Remote Labor Index. This is about double the previous best2.
David vs Goliath. A 35-billion-parameter model, trained differently3, now matches 1-trillion-parameter models on some long-horizon benchmarks.
Chinese pharma boost. Chinese biotech companies’ licensing deals are up 87% in the first five months of the year compared to the same period last year.
Wet-lab assistance. In a near-autonomous loop, GPT-5.4 helped Molecule.one’s lab run 10,080 reactions and increased the average yield in the Chan-Lam process4 by around 50%.
Holding swap. Central banks worldwide now hold more gold than US Treasuries – this is the first time since 1996.
Wealth concentration. Nearly half of all income earned in the US belongs to the top 10% of earners, the highest since WWII.
Fewer babies, richer adults? Across countries, a one-percentage-point lower birth rate in 1950 was associated with almost 27% higher GDP per working-age adult over 1970-2020, with no change to aggregate GDP.
Extreme heat. French utility EDF took almost 10% of its nuclear fleet offline (some 6.2 gigawatts) as river temperatures breached the threshold for cooling.
Thanks for reading!
Deployed describes chips that have been installed and are in active use. The state of announced chips, whether delivered or not, is not broken out.
The Remote Labor Index aims to measure how well AI agents can complete real freelance projects, spanning video, audio, 3D/CAD design, data analysis, and more, with human evaluators as judges of performance.
Using a new horizon-scaling method for AI agents. Horizon scaling refers to supervising agents on long‑context, multi‑step trajectories and training the entire decision process, not only the end answers.
This process is important in forming carbon-nitrogen bonds needed in many medicines.
2026-07-05 16:21:58
“Always an excellent perspective on emerging systems and their impact across the human landscape.” — Neill K., a paying subscriber
Our friends at Ramp and Revelio Labs released fresh data on AI jobs impact, based on more than 21,000 US firms. They find that heavy adopters grow headcount faster, not slower. These firms increased employment by about 10% over two years after adopting AI. Entry-level roles grew even faster, at 12%.
[H]igh-intensity AI firms are selecting different kinds of candidates. In this case, we believe they are selecting for a new set of skills, specifically, people who know how to use AI and use it well. Entry-level workers, especially recent graduates and college students, are a natural place to look.
We’ve written before that the widely accepted narrative that AI replaces jobs is too simple – this still holds. The opposite claim, that there’s nothing to worry about, is simplistic as well. Labor markets are complex but we can make some assumptions about what’s going on:
First, complementarity. If AI makes workers more productive, firms may want more workers because the return on each additional hire rises.
Second, supervision. As AI-generated work increases, firms may need more people to manage, review and quality-control that output.
Third, demand expansion. If the cost per task falls, more tasks become viable. Latent demand becomes actual demand. (Exactly what has happened with computing since the 1970s.)
Some firms are now learning that they fired people too quickly, mistaking task automation for human obsolescence. As we argued, the initial gains from AI show up in individual productivity, but the harder prize comes when firms redesign entire workflows and decision-making loops around it. There’s no evidence so far that humans aren’t needed in this redesign.
See also:
Medicine is trying to protect against “never skilling,” the risk that trainees rely on AI so much that they never develop clinical judgment.
Goldman Sachs economist Joseph Briggs expects AI adoption to temporarily displace about 9% of the US workforce over a 10-year transition.
US chip controls have driven China to treat open-source as resilience infrastructure, a new paper argues. Following each major US export control event since 2022, forking of LLM repos on GitHub jumped among China-linked developers but barely moved among US developers – 0.143 additional forks per repository-week for China vs 0.012 for the US, an 11x gap:
When uncertainty around upstream inputs rises, developers appear to increase engagement with open, locally runnable model infrastructure… [This is] a broader shift toward distributed innovation ecosystems that can expand participation, accelerate diffusion, and increase resilience under geopolitical and technological constraints.
Qwen and DeepSeek spread into research and commercial work globally almost as quickly as the best US models. But when authors examined US patents, the use of Chinese-origin models was rarely disclosed.
Another new paper suggests that Chinese innovation is becoming more self-reliant. The share of science produced in China that underlies domestic patents has grown from 1% in 2000 to 26% in 2025. China still builds on research done elsewhere, but domestic research is growing.
See also:
Notes from our 10-day visit to major AI labs in China.
One company you likely never heard of controls a large share of the optical transceivers that make every major AI data center work.
Wars are depleting global tungsten stocks. China mines 80% of it, and the West is scrambling to reopen its mines.
2026-06-29 20:42:25
Hi all,
Here’s our Monday roundup of data signals across AI, energy and markets.
Enjoy!
First, a chart from our inaugural state of the AI economy report. AI quarterly revenues are now exceeding the quarterly AI capex depreciation expense, but have not yet covered cumulative historic depreciation — let alone the additional headroom needed for a healthy margin.
China’s young guns. On average, Chinese AI labs hire talent with just 1.6 years of experience vs. 5.5 years for comparable roles in the US. (h/t )
The weight-loss dividend. Previously not-employed women who start GLP-1 treatment are about 27 percentage points more likely to be working after 18 months.
A new giant in town. SK Hynix’s market value passed Samsung Electronics’ for the first time.
2026-06-28 10:14:28
Hi,
Om Malik died on Wednesday. He was one of tech’s truest voices, as a journalist, a founder, an investor, a questioner and a photographer. He understood, before most, that technology is a human endeavor, not just an engineering one. Over the past 15 years, I’d seek him out regularly on my trips to the Bay Area.
He will be greatly missed.
Azeem
We published The State of the AI Economy report this week. It’s the first research report we know of to dissect the demand side of the AI economy, which is critical for understanding where AI is heading.
I’ve been in this research for months, and after all that time, one chart still stands out to me. It’s the chart that shows the break in the 50-year compute growth trend.
So, what’s so special about it… I’ve been tracking the global stock of compute for the past five years. This has involved building a model of the total number of computers of all types (mainframes, minicomputers, PCs, laptops, servers, phones, IoT devices) in the world and making reasonable, bounded estimates of compute horsepower. Up until 2023, the trend line was pretty clear: roughly 66% compounded growth in global compute stock, even across major platform shifts, from minicomputers to PCs and from PCs to phones.
Trends with a five-decade history have momentum, so to break this trend requires something special. The last time this happened was back in the mid-1990s, as businesses around the world inched past Solow’s paradox1, then accelerated by a growing consumerism of computing via Windows 95 and the Internet. Technology helped; the Intel Pentium had substantially better floating-point capabilities than earlier x86 chips. It arrived on the scene in 1993 – out of reach for my student budget.
The trend line reverts back to the mean around 2006. Dennard scaling2 broke: chip makers had to move to multi-core architectures, which don’t give a smooth scaling of FLOP capacity.
At the same time, the PC market had matured, and the development of mobile computing exploded in volume, but mobile processors are optimized for their physical constraints, rather than processing power. Cloud computing has centralized a lot of operations; it also emphasizes efficiency over raw power, and the shift from owned-and-operated servers meant higher compute utilization over a (relatively) lower base than otherwise.
Which brings us to the shift starting in 2020 as AI accelerators begin to make their mark.
Today’s AI runs on compute – floating point operations (FLOP) – as its central input. And we’re bringing more FLOP-factories online. Will this trend also revert to the long-term mean, just as the PC wave did? My guess is that this current pace would be sustained for a few years, perhaps even a decade or more, before reverting back to the long-term trend line.
See also:
Last week, we wrote that AI-native firms are flatter and have fewer managers than non-AI-natives. While this is true, for the sake of precision, we should say fewer managers of humans. With AI, every frontier employee becomes a manager of agentic teams.
2026-06-25 19:18:49
The generative AI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.
These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack. We are releasing this research today in our first The State of the AI Economy report.
The supply side of the AI market is well-understood. The picks and shovel suppliers, the computer chips, the memory, the power transformers, the cooling, all of the components of AI data centers are largely public companies. We get a sense of what is being spent on the buildout through their disclosures, sales and forward order books.
But understanding the demand side is much harder. And this is what we’ve spent the last few months tackling. We built a proprietary AI economy model that looks at total AI spend, whether enterprise or consumer, to answer the hardest questions of the AI wave:
How big is the market really?
Are the revenues growing?
How far do the revenues go to cover the investment expense?
What happens to the economics in the future as token prices fall and the quality of those tokens improves?
Before we get into the report, we think it’s important to break down how we did this work.
One of our central design choices was not to count the same thing twice. We report the dollar spent by an end customer. So if you spend one dollar with Anthropic for Claude and Anthropic spends 50 cents with Amazon to serve it, we track both figures internally, but we will report it as our de-duplicated number: one dollar. This avoids double-counting the value that flows through the supply chain.
This isn’t straightforward. While it’s easy to count the supply side, the demand side is trickier to entangle. Much of the revenue flowing into AI comes from privately held companies such as OpenAI, Anthropic, Cursor, ElevenLabs, and hundreds of others. They don’t legally need to disclose anything.
The remainder flows to the big hyperscalers that serve these models: Amazon, Google, and Microsoft. While they are public, they don’t consistently disclose their AI segment revenues.
To shed light on this, we examine public statements from hyperscalers and neoclouds, their suppliers, and their customers, using only high-confidence, detailed facts to inform our modeling. We also look at well-reported leaks and self-reports, to which we assign a confidence score.
The result is an item-by-item financial model for the largest contributing companies and business units. Each model is effectively a deconstructed financial plan, a P&L, balance sheet, and cash flow, and these are triangulated against other external sources and internal consistency checks. This makes our numbers auditable. We can identify which data point, with which confidence weighting, contributed to any given estimate.
We don’t include internal AI uplift, which is how much recommendation systems have improved, increasing ad revenue at Meta or Google. We do have models for those, but we’re not reporting them here.
Nor do we consider efficiency savings that the bigger tech companies might realize with their internal tools. We’re not tracking that yet.
We don’t include professional services and systems integration. When a Fortune 500 company spends or invests in AI, only a portion of that spend will go to an AI company. It won’t represent the full extent of their commitment, because a large part of it will be paying professional services to support the implementation.
We have got models for revenues in China, but this v1 of our report doesn’t include Chinese data yet.
Over the past 12 months, the AI ecosystem generated $110 billion in revenue when you remove double-counting. The growth rate is healthy. Annualizing the most recent month’s revenues indicates a $175 billion revenue run rate.
These revenues are growing faster than previous IT-oriented waves, roughly three times more rapidly than the mobile or Internet waves.
While many companies have moved beyond occasional pilots, they are still in the early stages of scaling and deepening. In conversations Azeem has had with senior execs across a range of industries in Europe and the US (from industrials to insurance, from finance to pharma), the consistent message is that they intend to invest more heavily in AI in the coming years. Companies are also becoming more vocal about the impact of AI on earnings calls, with the caveat that half of the surveyed CEOs believe their jobs depend on getting AI right.
The next question we wanted to track is whether AI revenues can cover the capital investment that’s required to build the infrastructure. Our model separates AI-oriented CapEx from ordinary CapEx across the major hyperscalers and neoclouds, the specialist AI cloud providers. This adjustment is important because hyperscalers were already spending around $120 billion annually1 on CapEx before ChatGPT.
We capture the additional investment in AI infrastructure, then depreciate compute assets over 6 years and other infrastructure over 14 years. Our modeling shows that revenues attributable to hyperscalers just about clear the depreciation expense.
Six years is defensible. That longer useful life reflects two things. One, demand still exceeds available AI compute; and two, operators are getting better at managing GPU fleets. Both help. The second alone is enough to justify a longer economic life.
We also examine how market size changes as token prices fall. The elasticity of demand shows that lower prices are met with increased spending. We estimate that across providers, every 10% price cut leads to 12-18% more tokens in use, so the total spend still rises.
We suggest that although a token is a useful billing metric, it is still not the unit of value we need to measure the economic value of intelligence circulating through the sector. Quality‑adjusted output tokens give us a better “intelligence quotient” for the AI economy by combining how many tokens are produced, how many of them are actually user‑visible outputs, and how capable the underlying models are.
The report also covers:
What AI demand has done to the US power industry and how power efficiency is changing,
What is happening to token costs, and how consumption-based billing may expand the market,
Four scenarios for how fast AI demand could grow under different price and capability trajectories.
This is v1, and we’d love your constructive feedback on what to improve and how you can help. Email us at aieconomy [at] exponentialview.co
Google, Microsoft and Amazon (whose spending included logistics investments). This number excludes Meta.
