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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Friend of radical Martin Alderson is back with a deep analysis of why many conventional software systems are under attack by AI – exemplified by the example of Figma, the once-darling Adobe-killer (and failed acquisition target of said company). Anthropic launched their new Design tool, automating 80% of what Figma does – as part of their Claude app, no design skills required.

But the structural point is harder to wriggle out of. Figma has ~2,000 employees. Anthropic has ~2,500 total and I doubt Claude Design took more than a handful to build. Figma now needs to out-execute a competitor whose inference is ~free to them, whose marginal cost to ship is roughly zero, and who employs fewer people on the competing product than Figma has on a single pod. That’s a very hard position to pivot out of. This feels like a preview of where SaaS economics are heading. The companies that built big orgs on the assumption of steady seat expansion are going to find themselves competing with products built by tiny teams inside the frontier labs. Figma just happens to be the first big public name where one of their primary inference suppliers has started competing against them

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Voice cloning has become incredibly easy, good, and, in some cases, useful. Advanced AI models only need a few seconds of your voice to create a convincing clone – which also means that with those few seconds of your voice, I can create a convincing spoof of you. Which, in turn, means that when your voice data is stolen, you might be deep in the s#!%.

On April 4, 2026, the extortion group Lapsus$ posted Mercor on its leak site. The dump is reported at roughly four terabytes and bundles a payload that breach analysts have been warning about for two years: voice biometrics paired with the same person’s government-issued identity document. According to the leaked sample index, the archive covers more than 40,000 contractors who signed up to label data, record reading passages, and run through verification calls for AI training.

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This famous question, first posed by the Roman poet Juvenal in his Satires, dating back to the 1st century AD, is increasingly answered by: As long as you are feeding the machine, nobody… Meta, reportedly, is running surveillance software on work PCs of their employees:

Newswire Reuters reports that Meta management sent staff a memo informing them that they’ll soon run a new tool called “Model Capability Initiative” that will record their keystrokes, mouse movements, and even take occasional screenshots – all in the name of gathering data the social networking giant can use to build better AI models.

The staff doesn’t seem to be too happy about it: “Meta employees are up in arms over a mandatory program to train AI on their mouse movements and keystrokes.”

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With AI models becoming more and more powerful, the cost of inference (at least for frontier models) is staying about the same (or increases) and the models consuming vastly more tokens for a given task. This being said, Toby Ord did a fascinating analysis of the cost of running AI agents as a function of “cost of labour” – and found that agents sometimes cost much more than human labour (“How is the ‘hourly’ cost of AI agents changing over time?”). In sum:

• This provides moderate evidence that:
  • the costs to achieve the time horizons are growing exponentially,
  • even the hourly costs are rising exponentially,
  • the hourly costs for some models are now close to human costs.

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We have been talking about an individual’s ability to sequence their own genome at home, using lab-grade but DIY equipment, for a while now (it was one of the predictions floating around in the ether in the heyday of Singularity University – it was always “just around the corner”). Now it has (finally) happened – alas, not for the faint of heart.

So this week I sequenced my genome entirely at home. Literally on my kitchen table.

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If you have even half an ear to the ground when it comes to cybersecurity, you have heard stories about Anthropic’s newest model “Mythos” being held back as it is “too dangerous” – with the main fear being that it finds vulnerabilities in software with an unprecedented speed and accuracy. In fact, people are hacking all kinds of hard- and software using current state-of-the-art models such as GPT-5.4 or Opus for the last couple of months now. All of which turns cybersecurity into even more of a race between who can outspend whom, than it already is. In simple (AI economic) terms:

to harden a system we need to spend more tokens discovering exploits than attackers spend exploiting them [and] to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them.

If you are running a system which has any public exposure surface (e.g. a website, an API, or an app), you better take this seriously. I wouldn’t be surprised if we will see tons of new exploits being executed in the next few months and years.

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GenZ, supposedly the most AI-savvy generation entering the workforce right now, is not too thrilled about that whole AI thing.

Anger over AI is increasing among Gen Z at the same time excitement is fading. Nearly one-third of the survey’s respondents, 31%, said AI makes them feel angry, up 9 percentage points from last year. And just 22% said the technology makes them feel excited, down from 36% the prior year. 

Reconcile this with the growing pressure on entry-level jobs, as well as overall job losses due to AI, and you have a storm brewing.

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The linked article is a good and accessible summary of where we stand on AI’s energy footprint. The tl;dr is that AI’s current energy footprint is modest (comparable to streaming video). But demand is growing fast, reasoning models use 10–100x more energy than basic queries, and efficiency gains keep getting reinvested into more capability rather than saved. And what electricity powers the data centers is a much bigger question: Clean grid = net climate okay. Gas/coal grid = real problem.

Stop feeling guilty about prompts. Your Wh per query is not the lever that matters. You’ll do more climate good by eating one less steak, taking one fewer flight, or voting for better energy policy than by boycotting LLMs. What matters at the individual level is where you direct your attention. Demand the acceleration of the deployment of clean generation to meet data center demand; grid interconnections, nuclear licensing, transmission lines, and permitting reform are the bottleneck, not GPUs.

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It shouldn’t come as a surprise that by harvesting the massive data trove that is Reddit, one can find drug side effects that are underreported in clinical trials. Reminds us of a pharma client of ours who mentioned that they consider Apple a massive threat to their business – as the company has a humongous amount of data on healthy people, whereas pharma companies typically only have data on sick people.

Using artificial intelligence to scan more than 400,000 Reddit posts, researchers from the University of Pennsylvania documented numerous reports of possible GLP-1 side effects that may be underrecognized in clinical trials — including menstrual changes, fatigue, and temperature sensitivities.

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Shadow AI was the story for a while – workers sneaking ChatGPT past IT, doing in minutes what used to take hours, running an underground productivity movement from their personal accounts (or simply freeing up more time to watch TikTok). Management called it a governance problem. Workers called it getting the job done. It felt, in a strange way, like good news (just like the good old days when we all brought our personal Dropbox accounts to the workplace as we were sick and tired of 1980s SharePoint).

That era has quietly ended. A new global survey of 3,750 executives and employees across 14 countries finds that more than 54% of workers bypassed their company’s AI tools in the past 30 days and completed the work manually instead – and another 33% haven’t used AI at all. Eight in ten enterprise workers are avoiding the technology their employers are spending record sums to deploy. Shadow AI has become the AI no-show show.

Now the data tells a different story. The tool that workers once raced to adopt covertly has become, for a large and growing share of the workforce, the tool they’ve stopped using altogether. Not because it doesn’t work. Because they’re afraid of what happens when it works too well.

The piece also surfaces a huge trust gap: only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives – a 52-point chasm. Executives and employees are, as the report puts it, describing fundamentally different companies. The fear of obsolescence – FOBO, fear of becoming obsolete – has apparently crossed the threshold from anxiety into active avoidance. Which is, if you think about it, a perfectly rational response to a completely irrational situation.

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For twenty years, the world operated on a simple principle: buy standard software, don’t build. The logic made sense, as building was insanely expensive, risky, and slow. The result was highly standardized systems (well hello, SAP!) which we had to stretch well beyond what they were designed for, patch the gaps with middleware, hire consultants to integrate the integrators, and call the whole messy pile “transformation.”

This long piece by EY’s Colm Sparks-Austin makes the case that the economics have fundamentally flipped. AI and modern dev tools have made engineering capacity abundant. The constraint is no longer “can we build this?” It’s “do we know what to build and why?” Colm’s argument is sharp – treat the core (ERP, system of record) as the skeleton: rigid, compliance-bearing, changed rarely. And treat the edge – the customer-facing layer, the last mile – as tissue: built to regenerate when the market shifts.

Standardization is no longer a safety net. It is a ceiling.

The piece is long, but worth your time – especially if you work with or inside large enterprises still debating whether to “buy or build.” That debate is over.

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