Scott Alexander, on the Astral Codex Ten blog, makes a strong argument for AI getting better and better at forecasting – and now regularly competing with (and sometimes outperforming) human superforecasters. The idea is simple and logical: Given that AIs have been trained on vast amounts of data, with the right harness, they should be able to make predictions with calculated confidence levels.

Ironically, we’ve spent the last couple of years making AI refuse to have opinions, and now we’re building a certification layer specifically so it can – which also means that the question isn’t whether AI forecasters are accurate; it’s whether we’ve thought at all about who decides which opinions get to count as “calibrated” versus “biased.”

But the AI prediction markets of the future should be far superior to the human markets of the present. The biggest barrier to current markets is liquidity - there isn’t enough money riding on most questions to convince top superforecasters to drop their jobs at Google or the NSA in order to think hard about them and bet on them. But AI forecasters bring the cost of forecasting labor down to near-zero, so we can have hundreds of different AI agents betting on each question and be pretty sure its error has been driven down to the theoretical minimum. This, in turn, means we can vastly expand the number of questions, including (finally!) allowing randos to submit their own questions (probably with AI assistance in proposing un-rules-lawyerable resolution criteria). […] This, then, is my prediction for the AI superforecaster future: for basic questions, your off-the-shelf AI chatbot will be able to offer opinionated probabilities superior to those of any human. For more controversial or bias-laden questions, a new era of prediction markets will smooth over differences in brand and model and efficiently aggregate all AIs’ opinions.

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Silicon Valley legend Kevin Kelly’s latest think piece is about our relationship with omnipresent AI assistants. In Kelly’s view – and remember, Kelly was a pioneer of the quantified self movement – we will soon all be running around with AI companions, which will become an extension of ourselves – hence the term “exoself.” In Kelly’s view, we will be making choices between four types of exoself relationships: Twin/Clone, Tutor/Guardian, Counselor/Assistant, and Hero/Friend.

So far, so dystopian (good or bad – you decide). What Kelly seems to miss, though, is the fact that these exoselves will be operated by commercial entities. And these entities have a history of embarking on infamous “enshittification” journeys (case in point: Meta now charges for essential features of its AR glasses). And as such, the relationship we have with our AI might not be something the user chooses, but platforms – not users – will engineer which of the four types you get, tuned to engagement or subscription revenue rather than your interests.

In which case, Kelly’s little admission at the very end of his piece might be the least of our worries (emphasis mine):

This second self will demand a new kind of relationship, one we haven’t had before — and its immense benefits will arrive bundled with immense problems. Every ailment that afflicts our born self will likely show up in the exoself too, plus novel ones we haven’t seen yet. Learning to use an exoself wisely will be one of the major lessons of a life lived this way. It will take years before society works out anything like best practices — we’re still working on those for social media. There will be multiple models and personality types to choose from. And there will be heart-wrenching stories of people losing their exoself — the worst case being simply that the platform went out of business.

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By now, fearmongering about AI has become the norm. From OpenAI and Anthropic warning the public about the insane danger their latest frontier models pose, to AI hypers and doomers taking positions on either end of the extreme to take the headlines. It’s all rather annoying and tiresome. And just when you think you have seen it all, along comes this (emphasis mine):

“AI is a global technology with global benefits, global harms, and a consistent tendency for new capabilities to eventually proliferate," Stephen Casper, a computer scientist at MIT who spoke at a major AI conference in Beijing this month, told Wired. “One thing that almost everyone in AI can agree on right now is that AI doesn’t need a Chernobyl moment,” he added. Casper didn’t elaborate further on this analogy, but by invoking the infamous nuclear disaster, it’s clear that the fear isn’t just over the catastrophe itself.

Yeah, we don’t need a Chernobyl moment. Also – whatever that means. Jeez!

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Say what you want about the leaders of AI shops Anthropic and OpenAI, criticize their truly dumb game of “our models are so good and powerful, they need to be locked up” fearmongering marketing play, but what happens now, with a national government deciding nilly-willy who gets access to AI, is weird, wrong, and plain dangerous.

The Trump administration is requiring both Anthropic and OpenAI to get approval for each new customer of their most powerful AI technology.

Assume (and that is still, to a degree, an assumption), for a moment, that the difference between frontier models and the rest of the bunch does matter in terms of economic impact (firms with access to frontier models have an actual competitive advantage), and you realize how messed up it is that the US government is shifting the playing field in very real ways – not just on a firm-by-firm basis, but for nation-states and whole regions. But then, of course, all this might not matter all that much as open-source models such as GLM-5.2 have become (allegedly) as good as (or better than) Mythos-class models. Maybe the genie is out of the bottle…

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Last summer, Stanford professor Erik Brynjolfsson published a comprehensive (albeit early) study on the impact of AI on the job market. Crunching a large data set made available through payroll provider ADP, he found that the impact of AI on jobs is real and measurable. Now he is back with more, and more current, data. And it doesn’t look any prettier.

For workers ages 22 to 25, employment in highly AI-exposed occupations is now shrinking at 3.8% per year and the early-career decline sharpened after year one — 2.8% decrease to April 2024, growing to a more than 4% decline per year since. The average decline on a month-to-month basis averages about −0.3% but Brynjolfsson notes that trend is noisy, compared to the year-over-year deceleration.

Link above is to the full study – it’s worth digging into the data to get the full picture.

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Much (much!) has been written about the pros and cons of remote work. Companies left and right have been requiring people to come back to the office – or embrace the remote/hybrid model. Studies have been done to show that people are happier when they can work from home; others show that remote work has a measurable impact on creativity and innovation across the firm. But, until now, little has been written (let alone studied) about the impact of remote work on mental health. A recently published study in Science looked at a large swath of data from studies done on remote work (note that this is US-only data) and came to a sobering conclusion (emphasis mine):

After the pandemic, workers in remote-capable jobs spent more time working alone and avoided social activities with their friends, remaining more isolated both during and after work. This pattern was most pronounced among remote workers living alone: They spent entire days without human contact and their mental distress, use of mental healthcare, and antidepressants increased acutely.

It’s not a pretty picture and warrants further study, as well as consideration on behalf of employers (outside of the ROI debate).

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When Jeffrey and I were at Singularity University, we used to comment on the fact that the average plumber in Silicon Valley made about as much (and sometimes more) money than the average software engineer. The same was (and is) true for many other trades. Plus – try to get a plumber to come to your house if you live in the Bay Area and you’ll experience first hand how hard it is to even find one. Nvidia’s CEO Jensen Huang just made the same point:

“If you’re an electrician, you’re a plumber, a carpenter—we’re going to need hundreds of thousands of them to build all of these factories,” Huang told Channel 4 News in the U.K. in late 2025. “The skilled craft segment of every economy is going to see a boom. You’ve going to have to be doubling and doubling and doubling every single year.”

Aside from him potentially being right – it’s kinda sad to think we live in a world where the value of human labor is to keep the machines running. Brave New World indeed.

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I do disagree with the multi-level marketing analogy (as MLMs live and die by their pyramid scheme nature of recruiting new people into the system), but Matthew Hughes does have a point when it comes to the notion that the “vibe-code yourself to millions” message of too many of the GenAI startups is predatory, sad, and dangerous. It makes me wonder how we will remember this moment in time.

And I am concerned that the fear I’ve described is being exploited by companies like Replit and Cursor (which is also doing the exact same influencer marketing schtick, albeit not as aggressively as Replit), who are touting their services as a way for people to escape the precariousness of this current moment.

Hughes’s best point is that vibe-coding is worse than Herbalife because at least Herbalife tells you the price, whereas vibe-coding tools notoriously burn through tokens with little price transparency. Which is also why it isn’t Herbalife.

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The newest student AI cheating tools don’t just write the essay for you – they slow-type it into Google Docs, typos and corrections and all, to slip past the detectors, while the human does nothing of the sort. We’ve now built software whose entire job is to fake the effort of learning, because effort is the thing we grade.

To me, this is the whole story; not the cheating per se. Cheating in school is as old as school itself (heck, I am guilty as charged here). The truly interesting question is why a student who’s just signed up for years of student loan debt would so cheerfully skip the part they’re actually paying for. The answer might be that they’re not being lazy (or dumb), they’re being rational. Bryan Caplan made the case years ago in The Case Against Education: most of what a degree buys you isn’t the knowledge, it’s the signal. If the credential is the product and the learning is optional, then handing the work to a bot that types its own typos is exactly what a smart customer does. Yes, this is the calculator panic all over again – except the calculator never pretended to be you doing the arithmetic. This one does. Which leaves one uncomfortable question for every school grading the take-home essay: what did we think we were measuring all along?

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The prevailing narrative at the moment is that AI is coming hard for software engineering jobs (from mass layoffs to recent graduates not being able to find a job and everything in between). Turns out, the data doesn’t actually support this narrative. Arvind Narayanan and Sayash Kapoor explored this topic in a thoughtful post on their “AI as normal technology” Substack. The reason is what Narayanan and Kapoor call the “decide-execute-deliver sandwich” – of which AI compresses the “execute” part but doesn’t budge on the other two.

Across 100,000 developers on GitHub, the researchers found that AI agents led to an eight-fold increase in the number of lines of code written, consistent with the idea that AI almost completely compresses the Execute layer of the sandwich. But this led to only 30% more releases, strongly suggesting that human bottlenecks (the Decide and Deliver layers) remain in place.

In my eyes, this has pretty far-reaching implications for other professions – as the two authors also point out:

In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned.

Highly recommended reading.

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This is pretty funny – Tim Ferriss (of “The 4-Hour Workweek” fame) writes a long post about “Has AI Already Killed How-To Nonfiction? Sales Trends, My Personal Data, and What It Might Mean for the Future.” It has some interesting insights and data points, and you might want to read it. But… it is also, likely, written (or co-written) by AI… ;)

Some telltales (unless Tim has adapted his writing style to sound like AI now):

My head has been spinning after getting a spreadsheet roughly a week ago.

But, let’s be honest: one quarter doesn’t make a trend. So let’s zoom out and look at my full catalog over a few years.

And my personal favorite:

Let that sink in for a minute.

The problem (for me at least) isn’t that Tim is (or isn’t – who knows, maybe his writing just sounds like this) using AI to write his blog posts – it’s that, due to the fact that I must have read the “Let that sink in for a minute.” line a million times by now (as it’s a staple of AI-generated slop), I am just so much less engaged with his article. Which is a shame – as it does make a good point (or so I believe, as I couldn’t get myself to do more than skim it…).

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On her blog, German pharmaceutical database specialist Ava outlines the creative uses her company found for AI – it makes you cringe, smirk, and realize that this exact thing might be going on in many companies the world over.

We have recurring house-wide meetings where groups are asked to show off their LLM projects. They register them, try them out for a couple months, and then come back presenting their results. I have attended all of these meetings so far, and there was not a single one that actually worked out. All projects ended with the conclusion that this isn’t workable, that this isn’t saving time, or that it over-complicates things. Hundreds of people, different teams, people enthusiastic about AI, all kinds of projects, and there wasn’t a single success.

For one, it was shown that you can ask the bot how it feels today. That wasn’t presented as a joke, or being sarcastic; no, it was shown very seriously, I guess under the guise of how cool and futuristic and human it is. […] Next up was the great use case of downloading the cafeteria menu (which is a 1 page nicely designed Excel sheet, like a timetable, showing the different options for each day) from the intranet, giving it to ChatGPT, and asking it what’s for lunch on Wednesday.

Read the whole thing. It’s gold. And then, maybe, compare it to your reality in the company you work for… ;)

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Classroom teachers in the US are becoming increasingly worried about the impact of AI on their students – specifically their critical thinking skills (which, ironically, are the very skills that are most needed in an age of AI).

Christa Corricelli, a special education teacher at Saugus Middle/High School outside Boston, says AI could be a valuable technology for learning, but too often students are using it as an answer machine – not a tool to bolster their thinking. “I think students who aren’t already intrinsically self-motivated to be critical thinkers, like that top 1% of the class … I think people who are not already that personality type, we’re going to see those critical thinking skills atrophy over time,” Corricelli says.

We likely won’t know how the use of AI, both on the teacher and student side, will impact our children’s mental and social development, nor what this will mean for the world at large – but it is surely something we might want to try to get right before it’s too late.

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The use of generative AI in genetic research, so far, has been held back by the complicated nature of turning DNA sequences into their physical forms – simply stated: The machines can dream up new DNA sequences much faster than we are able to assemble (and then test) them. This is about to change:

The technique, called Sidewinder, can assemble dozens of genetic sequences simultaneously in a single test tube, producing just one incorrect junction for every 10 million assembly events – a level of precision that far surpasses conventional methods, which misfire roughly once every 10 to 30 joins. Sidewinder also draws on cheap raw materials that have until now been too difficult to use reliably.

and:

In a demonstration of how squarely Sidewinder targets this bottleneck, the team behind the technique, led by Caltech synthetic biologist Kaihang Wang, harnessed the power of Evo 2 to redesign a 12,500-letter DNA sequence of the E. coli genome in silico and then used Sidewinder to build it from scratch – with no errors. Sequences of that length can encode entire biochemical pathways, laying the groundwork for engineered microbes that manufacture drugs, biofuels, or specialty chemicals, and eventually to the assembly of vast DNA constructs approaching complete artificial genomes.

Now, just as a thought experiment, consider what this could mean for the hotly-debated issue with the capability of frontier models to be use to assist with biological warfare. With great powers comes great responsibility…

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A new study from MIT’s Media Lab shows the correlation of tool use (in three groups: ChatGPT, Google Search, and no tool use) with brain activity. With the caveat that this is a small study, the results are not pretty:

EEG analysis presented robust evidence that LLM, Search Engine and Brain-only groups had significantly different neural connectivity patterns, reflecting divergent cognitive strategies. Brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling.

As the study concludes: “We demonstrate the pressing matter of exploring a possible decrease in learning skills.” Ouch.

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Add this to your AI datacenter bingo card: not only are GPUs and RAM in short supply, energy is a major limiting factor, and water consumption is a massive concern; we also don’t have enough electricians to build out the infrastructure.

That said, the Lake Mariner site is about one hour away from the Buffalo Bills’ stadium, which delivers another benefit. The completion of a massive refurb at the Bills has freed up hundreds of electrical contractors. And it is trades, specifically electricians, which are the biggest bottle neck for datacenter projects, said Farrell.

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Scale Labs just updated their Remote Labor Index (RLI) – a measure of how well AI agents are actually able to do work in the real world (“Evaluating the capability of AI agents to perform real-world, economically valuable remote work”). The tl;dr:

Absolute Automation is Near Zero: Current agents perform near the floor. At the time this leaderboard was launched, the highest-performing agent (Manus) achieved a 2.5% automation rate, with other models performing worse. This indicates systems fail to complete the vast majority of projects to a professional, client-ready standard.

The upshot? They are getting better.

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You have seen the headlines – young professionals increasingly have a hard time finding jobs and AI is to blame. The argument is easy to follow and certainly makes sense at first blush. A newly published paper begs to differ – what if it’s not AI (which is going through its own identity crisis at the moment, trying to prove its ROI), but remote work? The argument goes like this:

Early-career workers require more supervision than experienced hires, and build important skills, knowledge and social capital by observing and working alongside senior colleagues. Working from home adds friction to these processes, making entry-level workers more costly to bring on board in terms of time and resources and slowing their prospects for promotion. As such, the rise of remote work has worsened the trade-off for hiring entry-level workers, while leaving the calculus for senior hires unchanged.

If this proves to be true, you can expect a double-whammy hitting young professionals – as AI surely will have an additional effect on their job prospects.

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It is one thing to see the price of your state-of-the-art smartphone go up; it’s a whole different thing to see whole swaths of the population in low-income countries being priced out of the market completely. One of the more hopeful developments in tech over the last twenty years was the massive democratization of Internet access through the availability of cheap smartphones. Travel to any low-income country and you will see masses of people being able to access the Internet using sub-$100 smartphones. The AI boom led to a steep increase in the price for RAM chips, which in turn led to an equally steep increase in the price of (low-end) smartphones – leaving many people without the ability to purchase a smartphone and hence not being able to access the Internet.

So the trend of the last few decades, of consumer electronics getting better and cheaper every year, faces a sharp reversal: the poor world is now entering a smartphone crisis.

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The great Benedict Evans dropped his newest slide deck – this time on the state of affairs in AI. And when Ben talks, we listen. Skip the infrastructure cost slides and go straight to his insights on the commoditization of AI models (and hence why OpenAI & Co.’s moat might not be as deep as they make you believe), the challenge of AI for BPO-heavy countries like the Philippines, and the fun 1980s-era automation ads.

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The seemingly (for good reason) never-ending debate about AI job losses got another entry with the release of the latest report from the US Bureau of Labor Statistics (BLS). The jobs you would expect to be at the highest risk of being replaced by AI are (at least looking at the raw data) being replaced: Customer service jobs dropped by 130,180 jobs in the latest report.

On Friday, in an ~annual data dump from BLS~, it emerged that a depression in these “artificial intelligence related occupations” really does appear to be happening. This category was down by 0.2% from May of 2024 to May of 2025, a tiny drop, but one made more notable by employment in general trending up 0.8% in the same time period.

Meanwhile, those affected by AI-related job losses are sometimes relegated to using their (human) skills to clean up the AI mess. Oh boy…

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A recent YouGov poll found that the average American is pretty pessimistic about the prospects of AI.

Most Americans (71%) feel that the pace of AI development is moving too fast. […] Most Americans are skeptical that everyone will benefit economically from AI. Nearly two-thirds (64%) of Americans say that it is slightly or very unlikely that AI will create economic gains that benefit everyone.

Not a good showing for a technology which is supposed to be the savior of humanity (or at least business). It makes you wonder how much of that perception is due to the hype and fearmongering by the fine folks who built AI. It surely can’t help if, for example, the CEO of Perplexity runs around and tells everyone that AI will replace them, right?

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There can be no envy for CEOs trying to stay on top of the AI speed train these days. First they used AI to justify their layoffs – it just sounds so much better if you fire people due to “AI-related efficiency gains” (even better if you do so “anticipating” said gains). And now we have CEOs bragging about how much of the company’s code is AI-written. As if that means anything?! Both perspectives are navel-gazing at its finest…

Move over app downloads and EBITDA – the hot metric for CEOs is now AI productivity. In interviews and on quarterly earnings calls, CEOs are flaunting stats on how much code AI agents are generating. The trend began with AI companies like Anthropic, Meta, and Google, which have been grilled about their AI investments, and has continued with other companies eager to position themselves as AI-forward. From fintech to streaming, agentic AI adoption is the new status symbol among executives.

When will we see CEOs talking about how they are focusing on solving their customers’ problems again? It would make for a refreshing change…

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We talked about a similar idea here on the Briefing before – we called it the “bifurcation of intelligence”: a world in which some companies deploy Copilot and call it a day, while others are rethinking their business models in an age of AI agents (and the rest of it). Robert Glaser digs deeper into this idea:

But the interesting AI work does not wait for the next community meeting. It appears inside a code review, a sales proposal, a research task, a product prototype, a production incident, a test strategy, a compliance question. Or when someone figures out that for a certain class of product components, they can set up something close to a dark factory: write the intent, let the agent run a very loose loop, apply enough backpressure to keep it on track, evaluate the outcome against strong scenarios, refine the intent, and repeatedly get high-quality results. By the time the story is cleaned up enough to become a best-practice slide, the important learning has often lost its teeth. What made it useful was the friction: the missing context, the test that failed, the weird API behavior, the moment where the agent sprawled into nonsense and someone had to pull it back.

And to stay in the theme of my new book OUTLEARN:

The next advantage is learning velocity. Who finds the real patterns faster? Who moves discoveries from individuals to teams to organizational capabilities? Who builds backpressure into agentic loops, so agents can’t sprawl? Who distributes useful agent capabilities without turning them into monolithic enterprise agents that fit nobody? Who finally uses agentic engineering to make agile real, instead of just slapping AI onto the old ceremonies?

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If you do not read anything else this week, do yourself a favor and read this article by Nilay Patel on “Software Brain.” It’s a thoughtful piece about the disconnect between what the makers of AI think they are building, and what many of us experiences.

The entire human experience cannot be captured in a database. That’s the limit of software brain. That’s why people hate AI. It flattens them.

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