2026-07-19 10:37:10
“You inspire me to think more exponentially. — Robin D., a paying subscriber
Moonshot AI’s new open model, Kimi K3, might be more of a shock to the US than the original DeepSeek model. As a model, it is very good.
I’m wary about benchmarks because benchmarks aren’t the real world. But the emerging consensus appears to be better than Claude Opus 4.8 and, in some cases, on par with Claude’s Fable and OpenAI’s GPT 5.6. The real question, of course, is on which dimensions does K3 beat the frontier labs, and for which workloads are those dimensions important?
Price-wise, it is expensive for an open-weight model. According to Artificial Analysis, it’s about the same price as GPT 5.6 Sol but about 24x more expensive than DeepSeek V4 Pro. Indeed, on a per-token basis, it is only half the price of OpenAI’s GPT 5.6 Sol, far from the usual price advantages of Chinese models.
For the AI economy as a whole, for companies around the world, for governments that aren’t rich, this is probably a net positive. The inference margins that OpenAI and Anthropic enjoy are significant, and they can maintain them because they have the very best models. But it’s pressure, not displacement. Enterprises don’t buy on price alone. They value security, support and possibly the fancy professional services on offer. And the harnesses OpenAI and Anthropic have built remain a differentiator.
As we argued in the State of the AI Economy, token demand is elastic. Falling prices drive demand, and that demand drives infrastructure usage. Hyperscalers and neoclouds will serve these higher-end open models, further fueling demand for compute and everything around it. This pushes more of the revenue pool towards the compute layer and away from the model layer margin. This strengthens rather than weakens the infrastructure payback case—and, of course, the chip and memory suppliers that sit below them. A tempering note: cheaper intelligence still waits for monthly management meetings and a slow-moving approval process.
Your AI pilot worked, but now comes the hard part – deploying AI agents across a company that wasn’t designed for autonomous software.
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Okta’s AI Identity Readiness assessment will score the security of your AI agents and show exactly what you need to fix to go to production with peace of mind.
Run the 5-minute assessment to benchmark your organization.
Lazard’s latest energy cost report shows that the levelized cost of solar photovoltaic electricity has risen in the US on a year-on-year basis. Back in 2021, this was $38 per MWh; in 2026, it’s $69. The price of gas generation has also risen from $60 to $90.
You’d be right to point out that we’ve long argued that because solar panels – one of the key cost elements for solar power generation – are on such strong learning curves, the price will keep trending down.
To make sense of this, I looked at the evolution of solar electricity costs in thirteen markets between 2020 and 2025 using data from IRENA, the International Renewable Energy Agency. The headline story is that, yes, PV modules remain on an aggressive learning curve, with unit costs dropping as production increases. Overall systems costs continue to trend downward, but the levelized cost for delivering electricity has risen slightly since 2023.
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In June, solar supplied a record 25% of the EU’s electricity, more than nuclear, gas, wind and hydro.
Households in three Australian states are getting three hours of free power every day during peak solar hours.
Britain’s first experiments with copyright began in the decades after William Caxton introduced printing in the 1470s. The Crown licensed a single company to police what went to print, and in return, its booksellers had an exclusive right to copy the texts with no end date. This license manufactured scarcity for over a century.
In 1695, Parliament refused to renew the Licensing Act, which enabled the booksellers’ monopoly. That 15-year interregnum brought an explosion of ideas: London gained 70 political periodicals (from one) and print culture spread to the provinces and American colonies. Then, in 1710, Parliament enacted the Statute of Anne: the world’s first copyright law. It gave exclusive rights for a fixed term, just 14 years, renewable once, and then the work went into the public domain. The law’s title was “An Act for the Encouragement of Learning.” Enough scarcity to incentivize creation and freedom after that so that knowledge would compound.
Twenty-first-century economics agrees. Joel Mokyr took the 2025 Nobel Prize for showing how useful knowledge becomes self-generating. Scientific understanding enables new technologies. The problems encountered in applying those technologies stimulate further science. And a society open to new ideas allows each advance to become the foundation for the next. Knowledge does not simply compound; it helps produce more knowledge
In a world of AI, this compounding will be doubly true. And will set up greater tension between content industries, who, like Britain’s booksellers in the 17th century, will want to protect their old business models, and the potential to drive open-source AI models and a raft of complementary startups. Brian Williamson argues that the EU’s Anne-style settlement- its text-and-data mining exception is key to the EU staying at the AI frontier.1 It allows AI to learn from lawfully accessible material:
Machines as well as humans should be free to learn; what matters in terms of protecting creators is whether outputs, not inputs, duplicate existing work.
For Europe, woefully behind in semiconductors, compute infrastructure and foundation models, yielding to copyright lobbies would further weaken its relative position in AI.
AI impact on jobs: Junior roles are being “seniorized,” and employers are more open to humanities graduates.
Russian soldiers’ average survival time after reaching the front is 20-30 minutes.
💸 Prediction markets are starting to bet on AI compute costs.
estimates that China will have a Mythos-like model in February 2027. His full analysis is worth reading.
Claude’s personality changes depending on the model and the language you use with it.
💪🏼 There’s now a promising candidate for the first vaccine to prevent pancreatic cancer.
Apple is testing PrismML’s tech to run big AI models directly on iPhones.
Good short essay by : “We tend to conflate power-seeking AI and superintelligent AI.”
😷 How Palantir embedded itself in the UK state, an investigation: “Despite having no real history of working with health data, Palantir began positioning itself as the go-to expert and Global Counsel started hiring Westminster insiders who had contacts in healthcare.”
Thanks for reading!
Caveat: It is an independent report, but it’s paid for by Google. However, I think the argument is salient enough to present to you.
2026-07-13 19:46:30
Hi all,
Here’s our short Monday roundup of signals to kick off your week.
Another scaling law? ByteDance researchers found that newer AI models learn on the job1 about twice as fast as models from just three months earlier.
Execs talk down job cuts. The share of CEOs expecting significant headcount cuts from AI fell from 46% in January 2025 to just 20% in May 2026.2
AI’s audience splits. ChatGPT’s user share fell below 50% for the first time in March.3
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.
