It’s a good question – and also, almost always, aimed at the wrong target. The problem is hardly ever the test; the problem is the thing you’re trying to test.

Here’s the problem: Assumptions arrive too big. Someone sits across from me, full of conviction, and says something that sounds perfectly reasonable – “a user will pay $40 a month for this.” Innocuous. One clean sentence. Except that one sentence is hiding a whole stack of separate assumptions, each one smuggled in as fact: That the user has the problem. That they know they have it. That they’d pay to solve it. That they’d pay that much. That they’d pay it to you. That they’d pay it now. Six conjectures wearing the costume of one.

And the test inherits the whole bundle. You build something to validate “users will pay $40 a month,” it comes back murky, and you’ve learned little – because you couldn’t isolate a single variable. A convoluted claim makes a convoluted test, and a convoluted test answers nothing.

Now, to be fair to ourselves: most of life runs on conjecture, and that’s completely fine. We couldn’t function otherwise. You can’t go back to first principles every time you cross a street, select a movie to watch, or pick a restaurant – the world is too complex and the day too short. We run on inherited belief, and mostly it serves us well. But when it comes to experiments – building a business, building a product, betting real time and money – you do have to go back to first principles of what you actually know to be true, and build up from there.

Which is where my single favorite question comes in, the one I ask sitting across the table from someone: “Do you know this to be true, or do you just assume it?”

It’s astonishing how often “I know” turns out to be “I assume.” You poke once, twice, sometimes more often, and the certainty dissolves. It was folklore. A best practice someone read in a deck. An industry standard nobody’s checked since 2014. What worked at the last company. “Everyone knows” this.

If this feels familiar, it should – it’s the Five Whys, the recursive interrogation Sakichi Toyoda built into the Toyota Production System. Same idea, different flavor. Not five whys here, but the same question asked again and again – do we know this to be true? – until you hit one of two floors: actual evidence, or an honest admission that you don’t know.

Once you’ve separated what you know from what you merely assume, the assumptions are out in the open. Now you can shrink them. The method is just this: atomize. Break the bundle down until each piece is a single falsifiable claim – one sentence, one variable, one thing that could be shown wrong. Take “users will pay $40 a month” and walk it down to its atoms – they have this problem; they recognize it as a problem; they’d pay to fix it; $40 is a price they’d accept; they’d buy it from us specifically; they’d buy it today. Suddenly the original question – “how do we test this in the cheapest, quickest way?” – answers itself.

Here’s the loop, repeatable on any claim you’ve got. Write the assumption as one falsifiable sentence – and if it needs an “and,” it’s not one assumption, it’s two, so split it. Ask “do we know this, or do we assume it?” – recursively – until you hit evidence or admit ignorance. For each assumption still standing, ask the real question: what’s the cheapest, quickest test that could prove this wrong? Note that – prove it wrong, not right. A test that can only confirm you teaches you nothing; you want the one that could kill the idea cheaply. Then, before you run anything, write down what result would change your mind. And run the smallest version first. Then the next. That’s it. That’s the discipline. And yes, it is the essence of the scientific method. But we tend to not do this in business.

So, the next time you catch yourself saying “I know” – do you? Or do you still assume?

@Pascal

The other day, at the amazing EMBRACE Festival in Berlin, I had a conversation with a participant after my session, “The Turbulence Lab: Turning Disruption into Your Unfair Advantage,” about the main culprit in failed experiments. Of course there are many – but one of the most undervalued ones (from my perspective) is the fact that all too often we run our experiments on assumptions which bundle just too many variables into one. Experiments become too big, too complex, and too intertwined to yield meaningful results. Instead, next time you are thinking about testing a hypothesis, try to break it down into its most atomic unit and figure out the quickest, cheapest way to test it. You’ll find that by doing so you will not only get better results, but ultimately also move much faster.

And now, this…

Headlines from the Future

The Canary in the Coal Mine. 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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AI Slop Is Coming Even for the Best of Us. 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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Using Ozempic to Prevent Violence? This reads like a bad joke from a dystopian novel – but scientists in the Netherlands found a (so far unconfirmed) link between GLP-1 drugs and the suppression of violent tendencies.

Scientists at Rutgers University examined nationally representative survey data that compared former and current GLP-1 users. In people currently taking GLP-1s, they found, the link between being impulsive and being more prone to violence was noticeably weaker. Though the team’s findings are far from certain proof that GLP-1s can reduce violent behavior, they do warrant follow-up research, the authors say. […] To do so, the team turned to data collected last summer from a nationally representative survey of 7,521 U.S. adults. They specifically looked at 821 people who reported ever having taken a GLP-1, including 597 people currently on one. People were asked questions about their alcohol use and level of impulsivity, such as whether they would enjoy being in a high-speed chase or a fistfight. They were also asked (with a guarantee of confidentiality) if they had taken part in various violent crimes sometime in the past year. Sure enough, the researchers noticed a sizeable difference between people taking a GLP-1 and people who used GLP-1s in the past but are no longer taking them.

If history is any indicator, you can already see the overeager politicians who are going to mandate widespread GLP-1 use for anyone “on the spectrum” of turning violent…

What We Are Reading

Three Ways to Think About AI and Jobs AI won’t kill all jobs equally. Here’s a smart framework for figuring out how safe yours really is. @Jane

Allbirds Names New CEO and Changes Name Again Not an April Fool’s joke: After making the pivot from a shoe company to an AI company, Allbirds once again rebrands and changes its name. @Mafe

Why Your Best Ideas Aren’t Original The history of “multiple discovery” further supports the argument that a breakthrough idea may really depend on someone first framing a sufficiently precise question or problem to be solved. @Jeffrey

When Employees Are Drowning in Change I’ve heard a common thread in leadership discussions lately: the challenge isn’t identifying what needs to change or how, it’s that managing the volume of change people are being asked to absorb has reached a tipping point. @Kacee

How Not to Use “AI” (Workshop) A wonderful antidote to the “AI is inevitable” narrative and the resulting “you better get on the bandwagon” movement. This one is from a professor of (of all things) “AI, Language Diversity, and Communication Technologies.” @Pascal

Down the Rabbit Hole

😱 You seriously can’t make this up: Meta CTO reports employee morale is near historic lows, prompting leadership to propose boosting workplace snack budgets.

🪓 You might have heard me tell the tale of the two lumberjacks. It’s the little extra story in the GYSHIDO book. And now you can chop some wood right in your browser. Quite frankly – as utterly useless as it is – it’s wonderful to see people make useless things just for the heck of it.

🙂 Did you know that new emojis are being proposed and then accepted (or not) by a committee? Here is a wonderful pile of rejected proposals. My favorite? The “Angry Pile of Poo.”

☕ If you know me, you know that I am a coffee fanatic. Researchers in Australia found a completely new way to make the magic brew – by using ultrasonic sound waves.

🧑‍🚀 Follow along the amazing journey of Artemis II on this wonderful photo and sound timeline.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

This week, while spending a delightful day with our friends from EMBRACE at their annual HR-Tech festival in Berlin and speaking to many, many HR people, it was fascinating to hear and see how much their work has been impacted by AI (not surprisingly, of course). When you receive hundreds of AI-generated resumes and cover letters for your AI-generated job ad, all of which you run through your AI-powered applicant tracking system, the biggest challenge seems to be neither the sheer volume of applications nor the spotting of AI-generated content, but rather the fact that AI equalized all applicants to virtually the same level. Now everybody is great – and the actually good people are lost in the shuffle. “AI, the great equalizer” seems to cut both ways…

And now, this…

Headlines from the Future

GenAI Generates Genomes. 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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AI Is Coming for Your Kids Brains. Maybe. 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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A (funny) AI Reality Check. 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… ;)

What We Are Reading

A Disease of Deforestation: How Ebola Is Linked to the Smartphone in Your Pocket Your smartphone’s supply chain runs through the Congo’s rainforest, and the mining that feeds it may be driving the next Ebola epidemic. @Jane

Fifa Faces Empty Seats as 180,000 World Cup Tickets Hit Resale Market FIFA’s aggressive variable pricing strategy is backfiring; more than 180,000 tickets are still available, and resellers will likely be losing money. Not to mention, there are legal probes into the strategy alleging false scarcity to increase prices. @Mafe

Your Search Results Are Getting Sloptimized The moment it became clear that AI search would become a thing, gaming AI search would also surely become a thing. And here we are. @Jeffrey

BUILD Vs BUY: The Decision Framework for AI in Accounting Firms Our friends at CPA.com released a framework that, while it’s meant for accounting firms, is super helpful for organizations of all industries when considering how to approach the new capabilities that vibe coding has presented. @Kacee

Vinyl Succumbs to Loudness War: More Than Just Collateral Damage! First vinyl came back, then it lost to the relentless pushing of loudness in pretty much all recent music recordings – which should remind you that, yes, music did sound better in the past (and not just because it was analog). @Pascal

Down the Rabbit Hole

🍔 McDonald’s is using AI to make their drive-thrus more efficient, people are not having it: McDonald’s introduces AI drive-thru system, sparking customer backlash.

🤦 Episode 12 in the series of “CEOs doing dumb things with AI”. CEO says there will be no raises because he spent all the money on AI: “We will fund this AI investment by reallocating the budget from 2026 annual salary adjustments.”

💼 The AI-triggered (or blamed) layoffs keep coming: Tech industry loses 123,000 jobs this year – AI is the most cited reason for layoffs.

🏢 Meanwhile: Half of Americans fear AI could put someone in their household out of work, Reuters/Ipsos poll finds. and ‘More harmful than helpful’: young people sour on AI

🪖 If you don’t pay for it, you ARE the product: Scans by Dutch Pokémon Go players may have helped U.S. develop military drone technology.

⚽ You say football, I say cybercrime: Cybercriminals create 19,000 FIFA-themed domains ahead of 2026 World Cup.

↗ Dive into the deep end: Access our complete collection of 2,800+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

While I generally regard the upward trend of interest as a good and encouraging response to a context of persistent uncertainty amid the slow-motion collapse of old systems and ways of doing/thinking, I also think now might be a perfect time to talk about what it looks like when foresight goes wrong.

I see three typical failure modes.

(1) Sometimes, the foresight journey is just a mission into the future searching for justifications of the present strategic direction. Futures-thinking facilitator/researcher Thomas D’hooge aptly describes this as “foresight as anticipatory legitimation,” where one version of the future is emphasized – and it just happens to be one that strongly favors preferred present choices as highly beneficial. This is foresight practiced with a heavy confirmation bias. The foresight is closed and compromised, so the insight will be as well.

(2) Even a more open process can still lead to a closed and limiting result when the foresight collapses into something treated like a forecast. A new strategic direction emerges around a future that comes to be treated as an inevitability. The old “official future” is cast down only to elevate a new official future in its place. This is futures-thinking in the service of big bet prediction. But as the old aphorism has it: Prediction is hard, especially about the future.

There’s a reason so many foresight reports include an explicit disclaimer that they’re NOT predictions. Foresight isn’t meant to be a one-shot, one-way journey from uncertainty to certainty. Rather, foresight is most effective when practiced as a dynamic linkage between the present and the possible, where the view from the future affords us a new perspective on present decisions and actions – and those actions enable us to develop increasing clarity about how to navigate and shape an unfolding future.

Now about the action part…

(3) Foresight that never connects to action is just an interesting exercise, and it’s one that will quickly lose buy-in. In a strategic setting, our capacity to envision and explore futures is only valuable to the extent that we can use it to inform and influence decision making in the present. I’ve always loved Bob Johansen’s simple formulation of a Foresight-Insight-Action loop. If we’re not closing the loop, we’re not delivering value.

The big question then (and one we often hear from clients when we’re leading foresight trainings at Radical) is how to get to “correct” actions without collapsing the foresight work into a forecast where we’re still just predicting a winner at the end of the day. Part of the answer involves keeping multiple futures meaningfully in play (rather than focusing on one that feels the most “likely”) to identify actions that look strategically valuable across a range of possible futures. These actions are the so-called No Regrets Moves. Make them with confidence.

The other part is to view the actions as a set of interrelated bets that don’t all need to hit to be worth making. The actions don’t all need to be long-term “correct,” but they should make sense together in a smart, evolving portfolio of experimentation that’s driving learning as the Foresight-Insight-Action loop rolls action-based learnings back into the foresight process. The actions are experimental pings in the direction of futures of interest, enabling us to discover a strategic path forward in a space where there is no roadmap.

To repurpose one of my favorite Pascal-isms from his new book: You’re not predicting the winning path here. You’re discovering the winning path.

And so it is with foresight and futures done well more generally: We’re not predicting the future here. We’re discovering the future. Practice accordingly.

@Jeffrey

I am currently on the road for a series of client sessions – as part of this, I had the chance to sit in on a full day of AI sessions taught by some of the leading experts in the field at a top university – all geared toward a business audience. What stood out to me was less the specific content, but rather the fact that the sessions were all very, very similar. Here you have three experts talking about AI – and they all say the same thing. This might be indicative of the fact that we all still know very little about how AI truly will play out in the real world – hence we all make the same claims, use the same examples, and come to the same conclusions…

And now, this…

Headlines from the Future

Maybe Remote Work Is the Culprit for Young People’s Hiring Issues. 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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AI Agents (Still) Suck. 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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Your Brain on ChatGPT. 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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Energy? Water? RAM? GPUs? No, Electricians Are the AI Datacenter Bottleneck. 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.

What We Are Reading

‘An Equal and Habitable World Is Possible’: Academics Set out Sweeping Vision for Planetary Survival A rare piece of good news! A sweeping new report makes the case that a fairer, greener world is materially possible and lays out exactly how to get there. @Jane

Hackers Hijacked Instagram Accounts by Tricking Meta AI Support Chatbot into Granting Access Hackers exploited Meta’s AI support chatbot to hijack Instagram accounts by simply asking it to reset passwords without ever needing access to the victim’s email. @Mafe

AI Has Ruined the Job Market AI tools have rendered the old signals meaningless. In their place: a chaotic hall of mirrors where applicants and recruiters are both feeling lost. @Jeffrey

The New Ivies Over the weekend, my 17-year-old niece told me she’s never used AI because her school’s stance has basically been: “Don’t use it; it’s cheating.” That honestly alarmed me a bit, because the gap between how schools are treating AI and how employers expect people to work with it is growing fast. @Kacee

The Metaverse Fever Dream Phenomenal long-form exploration of the shitshow that is the “Metaverse.” If you ever asked yourself “what was THAT all about?” – here is your answer. @Pascal

Down the Rabbit Hole

😱 Another hard-hitting AI critique from Ed Zitron. Disagree with him, but do yourself the favor of reading it – it’s important to see all sides of the discussion: AI doesn’t have ROI.

🦹 AIs are getting pretty good at working with our day-to-day tools – which also means they are becoming powerful vectors to attack us: ChatGPT for Google Sheets exfiltrates workbooks.

🪖 First we had fitness app Strava leaking the secret locations of US airbases; now we have ad-tracking tech exposing the secret locations of US troops: The ad-tracking industry is exposing US soldiers on the battlefield.

🧑‍⚖️ This is a little bonkers and shows you how far AI has come (in certain domains): In a rigorous blind study, law professors overwhelmingly preferred AI-generated answers to student legal questions over answers written by fellow law professors – and flagged the AI answers as potentially misleading or harmful far less often.

💰 More and more companies are putting the brakes on their lavish AI (token) spending – Uber just instituted a (sensible) $1,500 monthly cap per tool. Simon Willison has a thoughtful back-of-the-envelope calculation of what that means in real terms – and what the implications are.

↗ Dive into the deep end: Access our complete collection of 2,800+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

Last week I had the chance to sit down with the amazing Gero Hesse, founder and CEO of Embrace, the German HR-Tech company, to talk about OUTLEARN. Talk about an industry which is being affected by AI – there isn’t a HR function in a big(ger) company anymore which isn’t running AI software to help with sifting through resumes, interviewing candidates, or help with the onboarding process. Here’s the interview.

And now, this…

P.S. Lessons in history – here is The Guardian, 19 years ago, about MySpace: “Will MySpace ever lose its monopoly?”

Headlines from the Future

Where Are the Returns on AI? Against the backdrop of the AI investment boom, we are starting to see the first signs of companies struggling to justify their AI investments (specifically, token spending). Recent headlines, such as Uber’s COO questioning the value they are getting out of their AI investments (“Uber burned through its entire 2026 AI budget in four months. Now its COO is questioning whether it’s worth it”), are starting to reverberate through industry after industry. The specific metric being questioned is the connection between token spend and features shipped (which is a fair point):

“That link is not there yet,” he said. “Maybe implicitly there’s more that is getting shipped, but it’s very hard to draw a line between one of those stats and ‘Okay now we’re actually producing like 25% more useful consumer features.’” […] “If you’re not actually able to draw a direct line to how [many] useful features and functionality you’re shipping to your users, that trade becomes harder to justify,” Macdonald said.

And it’s not just Uber – Nvidia (the company which is synonymous with AI) is facing similar existential questions:

These developments also suggest that the economics of replacing or augmenting human labor with AI may be more complicated than some early forecasts originally implied. That echoes what Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently said in an interview with Axios. “For my team, the cost of compute is far beyond the costs of the employees,” he said.

For us this points to a fascinating conundrum which we have been looking into (from a different angle) in our last Tuesday Briefing – while individual users often get massive value from AI through their $20 to $250/month subscriptions and their ability to freely explore the uneven boundaries of the technology, companies find themselves in a situation where they struggle to fit AI into “labor-shaped holes.”

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The RAM Crisis Latest Victim: Cheap Smartphones. 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.

What We Are Reading

Final Frontier for Meds? UK Startup Sends Drug-making into Space Cancer treatment just got a cosmic upgrade. Turns out the best pharmacy is 250 miles straight up. @Jane

Why the Vatican Invited Anthropic to the Pope’s AI Encyclical Presentation Pope Leo brought together the Vatican and Anthropic around a shared concern that AI systems risk being shaped purely by economic and competitive incentives rather than human values. @Mafe

The Great Flattening, Part 2: The Data Is Worse Than the Anecdotes It’s not just the LinkedIn posters: Companies and brands are converging on the same blandly smooth, maddeningly generic AI voice. @Jeffrey

Architects of Innovation: How Ics Power The Modern Tech Organization Insightful piece on how many orgs still operate like information and decision-making should flow vertically. Reality is, the people creating the most leverage are moving ideas horizontally across the business. @Kacee

The Cost Of Ever questioned what it will cost you over your lifetime to upgrade your perfectly fine iPhone every year? Or any of the myriad of other decisions we make every day? Here you go. It’s worse than you might think. @Pascal

Down the Rabbit Hole

🎓 After graduation speeches around the country have consistently missed the mark on AI, Steve Wozniak shows us how it’s done: Apple cofounder Steve Wozniak got cheers, not boos, after telling students they ‘all have AI – actual intelligence’

🧠 FOMO is real: Fear of missing out is linked to hypersensitive brain reactions to digital likes.

😱 That is a very poor choice of words: Bank boss sorry after describing workers as ‘lower value human capital’.

🤦 When search becomes a prompt: You can no longer Google the word ‘disregard.’

📹 Departing Meta staffer posts biting anti-AI video internally amid mass layoffs.

💸 Here’s the tracker: Is AI Profitable Yet?

💾 The RAM shortage is (very) real – and it shows: Memory has grown to nearly two-thirds of AI chip component costs.

💍 The headline here isn’t that there is a new, thinner Oura ring, but the fact that Oura (the health- and wellness-tracking company) has integrated GLP-1 (Ozempic) tracking into its app: They’ve finally made the Oura Ring smaller and lighter.

😋 Yum! There is now a small language model which takes food ingredients and creates recipes based on them. Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings.

👦 The terrifying rise of schoolboys making AI girlfriends.

🪫 This hits home – when we were at Singularity University, cold fusion was “ten years away.” Why Is Fusion Energy Always ’10 Years Away’?

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

The reason is what Ethan Mollick named the “Jagged Frontier.” The capability surface of LLMs is wildly uneven, and you cannot read its shape from the outside – the AI model nails the task you assumed was hard and fails miserably on the one you assumed was trivial, and the only way to find the edge is to walk up to it and test. So the target-state slide, for an AI-enabled product, is merely your best guess – not an actual plan based on knowledge and facts. You’re drawing a destination you’ve never actually seen.

And here’s the part leaders often trip over – and it sounds like a contradiction: even when you can’t draw the destination, your organization is still going to shape whatever you end up building. That’s Melvin Conway’s old observation (immortalized as “Conway’s Law”), the one software people quote – any group that builds a system ships a design that copies the group’s own communication structure. The seams in the product end up mapping the gaps in how people talk. The deliberate way to use that, popularized by Matthew Skelton and Manuel Pais in Team Topologies, is the inverse Conway maneuver: instead of letting your structure dictate the architecture by accident, you design the teams to produce the architecture you want and let Conway work for you. Want a modular product? Build modular teams with clean interfaces, and the product follows. It’s a genuinely powerful move – and it carries one quiet assumption that AI quietly demolishes. It assumes you can draw the architecture first.

Having thought about this for quite a while now, and having had the chance to discuss this with a group of leaders at a recent event in Hamburg, Germany, I believe AI shifts that assumption: In the AI era, you stop designing teams to match a target architecture, and you start designing them to match the discovery process itself. The org’s job is no longer to produce a system you’ve already drawn. It’s to find a system you can’t yet specify. And finding has a structure of its own – short loops, low latency between the person who discovers what works and the person who can ship it, tight coupling between experimentation and production. The inverse Conway maneuver doesn’t disappear; it changes what it’s aimed at. You’re still shaping the org to shape the outcome. But the outcome you’re optimizing for is the speed and quality of learning, not the fidelity of a blueprint.

A traditional product org can run discovery in one corner – a research team, a lab – and hand the findings down a chain to the people who build. With AI, that chain is where the value evaporates. By the time the insight clears three reviews and a quarterly planning cycle, the frontier has moved and the insight is stale. The thing you learned in March about what the model could do is a different thing by June. Learning that can’t be acted on quickly isn’t learning your organization actually has; it’s learning one person had.

For leaders in organizations, this means that we have to stop trying to write the target-state architecture before we’ve earned the right to – we simply don’t know it yet, and a confident wrong guess is worse than an honest blank. Go measure one number instead: how long does it take, in your company, for something one person discovers about what AI can really do to become how the company works? Weeks? Quarters? Never? Then go shorten that number, because it’s the only metric in this whole conversation that you fully control. Put the person who experiments and the person who ships on the same small team, in the same room, reporting to the same human. Give them a real sandbox and standing permission to use it – not a committee they have to petition. You’re not building the organization that executes the plan. You’re building the one that goes looking.

P.S. Curious about the best approaches on how to build a learning organization fit for an AI-driven future? My latest book “OUTLEARN – The Art of Learning Faster Than the World Can Change” might be useful.

@Pascal

This last week I have had the privilege of speaking to a roomful of executives in the Dominican Republic about the ideas we laid out in both Disrupt Disruption as well as OUTLEARN. A week later, I came across Ben Evans’s latest deck on the state of AI (see below) – flip to slide 76 in that deck and you see a graph showing the number of employees working in the IT-BPM (business process management) industry in the Philippines. You could draw a similar slide for the Dominican Republic. And as much as Meta laying off 8,000 catches the headlines, I think the much bigger effect of AI will be on the folks working in places like the Philippines on behalf of global businesses…

And now, this…

Headlines from the Future

AI-related Job Losses? It’s Complicated. 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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AI Eats the World. 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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Google eats AI. Or maybe it’s the other way around. After Google’s annual product fest, Google I/O, this week, the number of times AI was mentioned is hard to count. There is a humorous 52-second supercut of all the important announcements from the event (hint: it’s AI). But then there is also the beginning of the AI-fueled ad era (which, of course, had to come).

When researching a topic, consumers want to know exactly how a product suits their unique situation. In fact, 75% of people report making faster, more confident decisions using AI Mode in Search. That’s why we’re testing two new types of ads, built with Gemini, that offer relevant product details along with helpful guidance.

It will be interesting to see how this all plays out over the longer term – with a growing backlash against AI in general, it makes you wonder how enthralled people will be about ads being shoved into their AI-generated responses. And how much will you trust an AI-generated response, one which, to this day, is at risk of being hallucinated in the first place, to be accurate, when you know that there are commercial interests at play?

What We Are Reading

Elon Musk and the US Government Fought an AI Anti-discrimination Law. the Arguments Don’t Hold up. When the federal government joins a billionaire’s lawsuit to kill state AI protections, it sends every other state a clear message: don’t even try. @Jane

This Literary AI Scandal Changes Everything A literary prize is at the center of an AI scandal, but this time the accused authors aren’t owning up to anything, exposing a loophole that may be difficult to close in the future. @Mafe

How Agentic AI Supercharges Startups and Threatens Incumbents Feels like we’re underestimating how much advantage is shifting toward teams that can move, decide, and adapt quickly. @Kacee

Ask an Astronaut Ever wanted to ask an astronaut (an actual space-faring one) some questions? Such as: How do you shower in space? Here is a delightful opportunity to do so – and get some real answers. @Pascal

Down the Rabbit Hole

⏳ The ever-great Simon Willison on the state of AI within the last six months – so much change, so much progress: The last six months in LLMs in five minutes.

🖼️ Give people a real Monet, tell them that it’s AI-generated, and watch the comments flow… Yep, Monet is overrated. Apparently.

🙍‍♂️ The kids are not having it: The AI bots are coming and the young are booing, not applauding.

🤑 Ed Zitron’s latest take-down piece traces the money in AI – and it ain’t pretty: AI is too expensive.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

After our recent plug for Kevin Kelly and his perspective on uncertainty (and the fact that even uncertainty is now uncertain). This week Kevin is back with a thoughtful (and thought provoking) piece on his experience with AI (“The Emergent Self Loop”) – it’s worth a read. Disagree with it (I do, at least in part), but ponder over it a bit. As someone once said: AI is not artificial but alien intelligence.

And now, this…

Headlines from the Future

First It Was AI RIFs, Now It’s AI LOCs. 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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Turns out, the People Are Not so Hot on AI after All. 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?

What We Are Reading

Brazil Caught up in Craze for Whistling-only Whatsapp Groups Hundreds of thousands of Brazilians are currently in WhatsApp groups where the only permitted communication is… whistling. @Jane

The State of Answer Engine Optimization Answer engine search is only going to continue to grow; AI-sourced site visitors have higher purchasing intent and a higher conversion rate than those arriving on websites from other channels. This report highlights the state of AEO and what companies are doing to get in the game. @Mafe

Data Makes the World Go ‘Round Fern Halper surfaces the disconnect happening in transformation right now: organizations layer AI on top of foundations that were already fragmented, disconnected, or poorly governed. @Kacee

Admit It, That Protein Shake Is Basically Soylent From Soylent Green to Soylent to your modern-day protein shake… I remember well the craze of “optimize your life by skipping food and going straight to the nutrients.” Heck, we fed this stuff to people at Singularity University’s executive program… @Pascal

Down the Rabbit Hole

🧑‍🔧 Futher to the point above on CEOs touting their AI-generated code generation and American’s increasingly not agreeing with AI, workers are engaging in all kinds of weird behavior: Meta employees launch protest against mouse-tracking tech at US offices. Amazon employees are inflating AI usage to top leaderboards and impress managers.

🏥 Maybe it is not the best idea to use LLMs for your healthcare needs?! Google’s healthcare AI made up a body part – what happens when doctors don’t notice? And: Medical AI transcriber for Ontario doctors ‘hallucinated,’ generated errors.

📝 I recently found myself in a Zoom meeting with four AI notetakers but no people – of course, I trolled the notetakers by talking gibberish. But there is a bigger issue here: They are making lawyers very nervous.

🦹‍♂️ AI models becoming eerily good at cybersecurity also means AI models becoming superbly good at hacking your systems. This is not a hypothetical anymore – it’s happening in the wild: ‘It’s here’: Google issues dire warning after catching hackers using AI to break into computers

🎓 Want to learn how AI actually works? Here is a fantastic course explaining the inner workings of LLMs using math an 11-year-old can understand.

🕵️ Wondering what your webbrowser knows about you? More than you might think… Scarily more.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

They weren’t struggling to implement the agents. They were struggling to absorb the time.

In many cases, that newly available capacity didn’t translate into a rethinking of the work itself. Instead, it triggered a kind of organizational friction (yes, politics) because when time gets freed up at that scale, it begins to challenge deeply embedded assumptions about productivity, utilization, and value. In some firms, the instinct was to quietly refill the time with more of the same work. In others, it created internal tension because the system wasn’t designed to accommodate that level of slack. What surfaced wasn’t a technology gap, but a mindset and culture gap.

Contrast that with another firm I spoke with – similar agent deployed with similar time savings – where leadership refused to let the freed hours get reabsorbed into more returns. They made the trade explicit: every preparer owed a minimum of five hours a week to learning client advisory work, sitting in on CFO calls, and understanding what insights showed up in real conversations. Same efficiency gains from the AI implementation, but dramatically different approaches with their portfolio of time.

This is the broader shift that is just starting to come into focus. AI is not simply making work faster or more efficient – it is collapsing the time required to perform it. And that collapse is happening unevenly across functions, roles, and industries, which makes it harder to see as a single, coherent trend. But at the organizational level, the implication is consistent: the relationship between time and output is breaking down.

For decades, most operating models have been built on the assumption that more output requires more time applied to known processes. AI is disrupting that equation. When a meaningful portion of the day is no longer required for execution, the question is no longer how to optimize the work, but what the work should become. And this is where many organizations stall, because the default response is to treat freed-up time as excess capacity to be redeployed into the existing system. It feels rational, it preserves predictability, and it aligns with how performance has historically been measured. But it misses the larger opportunity.

The more useful way to think about this is through the lens of core and edge:

Core: the work that sustains the business as it exists today – repeatable, measurable, necessary.

Edge: the work that shapes what the business becomes next – exploratory, undefined, harder to quantify in the near term.

Historically, the core has consumed almost all the organizational oxygen, leaving the edge to a small subset operating on the periphery. What AI introduces is the chance to rebalance the equation – not by eliminating the core, but by collapsing the time it requires, and in the process opening up space to build for the future.

That space is where the real leverage sits, but it is not automatically captured. In fact, most organizations will default to reinvesting that time back into the core, driving incremental gains in efficiency or volume. There is nothing inherently wrong with that, but it is unlikely to create meaningful separation. The organizations that begin to differentiate will be the ones that deliberately redirect a portion of that freed time toward the edge – activities that expand capability, deepen customer understanding, rethink products/services, and build entirely new ways of creating value.

Of course, most organizations aren’t built for this. Performance systems, incentives, and management practices are all wired to optimize known processes. So when time is freed up, the system naturally pulls people back toward the core, because that is where success is defined and measured. This is why the friction shows up as “politics” or resistance.

The real gift of AI isn’t speed... it’s choice. Organizations are being given, perhaps for the first time at scale, the ability to decide what to do with time that was previously non-negotiable. That choice will shape not only how work gets done, but what kinds of capabilities are built and where differentiation emerges over time.

Most will respond by doing more work, quicker.

A smaller group will respond by doing different work altogether.

The gap between those two approaches is where the next wave of advantage will be created.

@Kacee

Silicon Valley legend Kevin Kelly recently wrote an excellent piece on “our certain uncertainties.” It is one of those rare pieces where I highlighted nearly every sentence. I highly recommend adding it to your weekend reading list – to whet your appetite, let me just give you a stitched-together quote:

So for the next 10-15 years we have perpetual, continuous, severe uncertainty. This is a burdensome weight because people hate uncertainty more than bad news. […] What we end up with is a poly-X, a multi-factored unknown, an uncertainty cascade, a pervasive lack of confidence about the future, in an era of ambiguity. […] The most effective response to this multi-layered persistent uncertainty is not to seek impossible stability, but to cultivate radical adaptability and radical optionality.

Read it. I wish I were as eloquent as Kevin – the ideas and concepts he shared are very much what we have been preaching for years as well (and I am sure will sound and look familiar).

And now, this…

Headlines from the Future

When Everyone Has AI and the Company Still Learns Nothing. 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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The Age-Gated Internet is Coming. In the (usually) well-meaning effort to keep minors from seeing stuff they shouldn’t, regulators around the world are pushing for age-gating the Internet. What started with sites which are obviously not for children, such as porn, is now being extended to social media and a bunch of other sites. There are a good number of reasons why this is a bad idea (and why it mostly doesn’t work anyway), but one of the more important ones is that it creates all kinds of privacy issues for all of us. Meta, not one to miss a beat, decided to take the bull by the horns:

Meta is unleashing AI that scans users’ bodies – from face shape to height – in an aggressive bid to root out underage accounts on Facebook and Instagram. The company announced Tuesday it was developing “advanced AI” that includes the use of visual analysis for detecting underage accounts. This new visual analysis technique will enable Meta’s AI to scan photos and videos for “visual clues” about a user’s age – including one’s height and bone structure.

Brave new world.

What We Are Reading

The Rise of Emotional Surveillance Burger King’s AI headset assistant is named Patty, and she’s judging whether you’re friendly enough. It is the scary future of work. @Jane

I’ve Covered Robots for Years. This One Is Different Instead of learning from videos of humans, these robots practice entirely in simulation, inventing their own solutions through trial and error, and they are scarily accurate. @Mafe

How to Remove the Wrong Kind of Friction (and Add the Right Kind) Plugging a very insightful, practical episode of a dear friend’s podcast here. Check out Lisa Kay Solomon’s conversation with Bob Sutton on friction as a design problem and its use and abuse in process, collaboration, and work. @Jeffrey

The Abundance Era We’ve spent the last decade overloading the skeleton (core systems) with things they were never meant to do. Advantage comes from treating the edge as tissue: something you can continuously rebuild, not protect. @Kacee

Appearing Productive in The Workplace An eloquent exploration of what happens when we remove “slowness” due to deliberate work from our outputs and instead focus on quantity as a measure of productivity. @Pascal

Down the Rabbit Hole

🤙 Action leads to reaction: “Telus uses ai to alter call-agent accents” – “AI ‘accent masking’ at overseas call centres sparks union backlash in Canada”

🚔 Turns out, self-driving cars commit traffic violations after all. And now, California gives them tickets.

👨‍💻 AI has the potential to make everyone a little smarter – but for the top 2% of workers, it might be a very different story: Ex-Meta manager says just 2% of engineers are winning the AI era.

🦹 If you are one of the people who have been writing in public (i.e., the Internet), you had better get used to the idea that your anonymity is gone: AI only needs 150 words to identify you. What does that mean for you?

🎬 How A.I. is transforming China’s entertainment industry.

🕴️ If in doubt, evoke Jevon’s Paradox and the world will be saved: Dario Amodei spent last year warning of an AI white-collar bloodbath. Now he’s changing the narrative.

📞 Nostalgic for your rotary phone of yesteryear? You can get it back! Here is the Rotary Un-Smartphone.

↗ Dive into the deep end: Access our complete collection of 2,800+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

A quick personal update before we dive in: OUTLEARN launched on Tuesday and – I’ll be honest – the response has been beyond what I expected. The book hit #1 New Release in Amazon’s Strategy & Competition category on launch day and is currently sitting at #2 on the bestseller list in that same category.

But what’s even more awesome is your feedback. One early reader wrote that he bought three copies on launch day to hand out to colleagues at a startup they’re building together. Another described it as a “fully actionable page-turner” and said the reframing of postmortems as harvest meetings would – his words – “turbocharge how you extract practicable learnings from any project outcome.”

That’s exactly what I was hoping this book would do. Not sit on a shelf. Get used on a Monday morning.

If you haven’t grabbed a copy yet: Get it here. It’s 150 pages, every chapter ends with something deployable, and it’s cheaper than lunch. And if you’ve already read it – an honest Amazon review in these first two weeks genuinely makes the difference between a book that reaches people and one that disappears.

And now, on to our usual programming…

Here is an interesting argument: “AI doesn’t really ’think.’ Rather, it remembers how we thought together. And we’re about to stop giving it anything worth remembering.” This is from a provocative article by Bright Simons. I might not fully buy into all aspects of his argument, but his essay is very well worth reading (and pondering over). Let me leave you with just one more quote from the article: “The result is a world in which individual productivity rises while the collective pace of human thought starts to fall.”

Read the thing. And then, read this…

Headlines from the Future

Why (Conventional) Software Truly Is under Attack. 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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Software Brain is Eating The World. 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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Who Watches the Watchmen? 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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4TB of Voice Samples Were Just Stolen from 40,000 AI Contractors. 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.

What We Are Reading

AI’s Next Frontier: People Skills AI models now beat humans on emotional intelligence tests, which only goes to prove that acing a test and understanding a feeling are completely different things. @Jane

Paypal Says AI Shopping Agents Are Creating an Invisible Storefront Economy Nearly all merchants are already seeing AI agent traffic, but fewer than 25% have the machine-readable catalogs, APIs, or the agent-compatible checkout systems needed to act in real time and convert on the spot. @Mafe

Scarce Assets: The Abundance-Driven Scarcity Supercycle Sticking with my theme from last week: Another angle on the idea (and value) of finding what remains scarce in markets where some things seem newly and wildly abundant. @Jeffrey

Chatgpt Traffic Analysis: Insights from 17 Months of Clickstream Data Most people assume AI search = more discovery, but this data suggests the opposite: that in fact distribution is compressing. If you’re not in the answer set, you’re effectively invisible. @Kacee

When the Internet Was a Place If you are fortunate enough to have experienced the Internet pre-Web 2.0, you know that it was a very, very different place. It’s high time to claim back some of those properties. @Pascal

Down the Rabbit Hole

🤔 Finally want to understand how LLMs actually work? Here is a wonderfully designed and easy-to-follow primer.

🤑 We mentioned it in the briefing last week. The era of heavily subsidized AI models might come to an end quicker than many of us thought or had hoped for. GitHub Copilot just announced that they will be moving solely to a usage-based billing model. Meanwhile, tokens get more expensive, and AI agents can now cost as much (and more) than human workers. No more free beer!

🏬 Someone set up a store and let an AI agent run it. Here is the story of how it’s going (hint: not great, but also not a complete disaster).

🧑‍🏫 A Catholic scholar argues that GenAI threatens authentic education by replacing the process of learning with the production of polished output, emphasizing the need for pedagogical redesign to restore the formation of thoughtful, responsible individuals: Repairing the ruins – Why AI can’t replace education.

💭 Your friendly public service announcement: A.I. should elevate your thinking, not replace it.

🕴️ Somehow I am genuinely surprised that this wasn’t the case before (and how this is news to begin with): Accenture to roll out Copilot to all 743,000 employees in boost for Microsoft

🙀 This story is making the rounds at the moment – an AI agent goes rogue, deletes a company’s entire production database, and then apologizes for it. The deeper cut is that it’s not just the AI agents fault, but the database system itself didn’t have any safeguards in place to prevent this from happening in the first place.

📰 The (sad) future of journalism – an OpenAI-linked news outlet appears to be entirely AI-generated. And the bigger picture: AI swarms could hijack democracy without anyone noticing.

🥳 XOXO, the Portland-based answer to SxSW, is no more. But its legacy lives on in the form of this wonderful (and wonderful-looking) XOXOFest website.

👻 Now we (finally) know: Spooky feelings in old houses may be caused by boiler sounds, study suggests.

↗ Dive into the deep end: Access our complete collection of 2,700+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

In a now-famous study, participants played a virtual ball-tossing game while inside the scanner. At first, the other players passed the ball to you. You felt included. Then, abruptly, they stopped. They passed it only to each other, ignoring you completely. You were excluded. You were failing socially.

The brain scans revealed something harsh: the dorsal anterior cingulate cortex – the same region that activates when you’re punched in the stomach – lit up as though you’d been physically struck. Your brain doesn’t differentiate between the emotional pain of rejection and the physical pain of being hurt. It processes both as the same thing. Evolution shaped you this way. Throughout most of human history, social rejection meant expulsion from the tribe, which meant death. Your brain built a rapid alarm system to prevent it.

Which brings us to the most dangerous entity in your boardroom: not the skeptical CFO or the micromanager, but the prehistoric saboteur living inside your own skull – the amygdala.

Imagine – you’re in a quarterly review. The CFO asks a pointed question about your project’s burn rate. Your mouth goes dry. Your mind, which was sharp five seconds ago, suddenly feels foggy and slow. You know the numbers – you rehearsed the numbers – but they’ve vanished. You mumble something defensive. The meeting moves on. Later, in the elevator, the answer floods back, obvious and clear.

What happened? A war between your ears. In one corner: the prefrontal cortex, the CEO of your brain – logic, creativity, long-term planning all happen here. In the other corner: the amygdala, buried deep in your temporal lobes. Ancient. Fast. Unsophisticated but devastatingly effective. It doesn’t write poetry or design software – it keeps you alive by scanning for threats. And here’s the tragedy: these two systems operate like a seesaw. When one goes up, the other comes down.

The CFO’s pointed question? To your amygdala, it was indistinguishable from a predator in the tall grass. So it initiated a hostile takeover – flooding your bloodstream with cortisol, diverting energy to your legs and fists, and literally cutting blood flow to your prefrontal cortex. The lights in the CEO’s office went dark. You weren’t stupid in that meeting. You were chemically lobotomized.

This is the biological saboteur. And it’s running your company’s innovation efforts into the ground.

I want to throw something every time I walk into an innovation lab and see a poster that says “Fail Fast, Fail Forward.” It’s a beautiful sentiment. It is also biologically impossible in most organizations. You can’t just decide to be comfortable with failure any more than you can decide not to pull your hand back from a hot stove. If the environment triggers the threat response, the lizard wins. Every time.

You can buy all the bean bag chairs and install all the ping-pong tables you want, but if your culture punishes mistakes – even subtly – the amygdala wins every time. Simply stated: In most organizations, every idea that challenges the status quo is initially perceived by someone as a mistake. If your culture punishes mistakes, it is also punishing the early signals of innovation.

Think about the standard Monday morning status meeting. A project manager has to report that an initiative didn’t work. They’re nervous. The room goes quiet. You, the leader, say “It’s okay, we learned something” – but even those words betray you. “It’s okay” is a verdict disguised as comfort. It concedes that something wrong happened and positions you as the authority granting forgiveness. And forgiveness implies the possibility of its absence next time. If your tone is tight, if you sigh, if there’s a micro-expression of disappointment, the room detects it. Every brain in the room just received a signal: Error = Fear and Pain.

The result? People stop offering creative solutions (prefrontal cortex) and start offering defensive explanations (lizard). They stop saying “I wonder why that happened?” and start saying “Well, marketing didn’t give us the right assets.” All learning stops, and self-preservation begins.

So what do you do? You can’t wait another million years for evolution to update the firmware. You have to hack the software you have today.

Here’s one starting point: change the words you use. Words like “failure,” “mistake,” and “error” are loaded – they are threat triggers. When you ask “Why did this fail?”, you are practically begging the amygdala to wake up. Instead, try framing every outcome as a “first attempt in learning.” When you frame an outcome as a “failure,” you’re issuing a verdict. When you frame it as a “first attempt,” you’re describing a process. It implies iteration. It implies you’re not done yet. The lizard stays asleep. The CEO stays online.

And here’s the Monday morning test: at the start of your next crisis meeting, try calling out the biology explicitly. “Everyone’s lizard brain is freaking out right now. That’s normal. Let’s take two minutes to breathe so we can get our prefrontal cortexes back online.” It sounds silly. It works.

This is from Part II of my new book OUTLEARN: The Art of Learning Faster Than the World Can Change, which is live today on Amazon – paperback and ebook.

The book goes much deeper into the neuroscience, the linguistic hacks, and the math of why some failures lead to breakthroughs and others lead to bankruptcy. If this essay landed for you, grab a copy – and if you’re feeling generous, an honest Amazon review in the first week helps enormously.

@Pascal

Remember the glorious days when Uber and Lyft were heavily subsidized by their venture capital sugar daddies and you couldn’t get over how cheap it is to get a ride? Yeah, those days are gone (and much can be said about the market-distorting effects of the VC-fueled subsidies). Well, it increasingly looks like the sweet days of $20/month all-you-can-prompt AI plans are also coming to an end – pretty much all the major AI companies are tweaking their pricing strategies, making tokens for their latest frontier models much more expensive, and generally trying to dig themselves out of the “for every dollar we make, we lose five”-hole. It doesn’t come as a surprise – but it will be interesting to see what it does to market demand.

And now, this…

Headlines from the Future

Putting the AI Investment into Perspective. As the saying goes – a picture is worth a thousand words.

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Are the Costs of AI Agents Also Rising Exponentially? 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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Cyber Security Is a Completely Different Game Now. 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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DIY Sequence Your Whole Genome. 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.

What We Are Reading

A New Kind of Hybrid Car Is About to Hit America’s Streets EREVs are the exciting new hybrid technology everyone should know about. Your car runs on electric power but quietly refuels its own battery with gas, so you never have to worry about being stranded! @Jane

TikTok Makes Americans Want Chinese EVs They Can’t Have Chinese car brands are nearly absent from US roads due to tariffs and regulations, but are building American consumer desire through social media while playing a long-term strategy. @Mafe

What Will Be Scarce? An economist goes deep on a relatively optimistic scenario for the future of human labor, finding durable value in what he calls the “relational sector,” where the value of the service is likely to be increasingly linked to the human providing it. @Jeffrey

The End of One-Size-Fits-All Enterprise Software Pascal and I have been writing about this lately, we’re moving from standardized systems to outcome-driven architectures that can conform to the business. @Kacee

Our newsroom AI policy As companies (and in this case, newsrooms) around the world grapple with what it means to operate in an AI-enabled/driven world, it will become more and more important for organizations to establish (and publish) clear guidelines and disclosures on their use of AI – here is a good example from the Ars Technica newsroom. @Pascal

Down the Rabbit Hole

✊ “We believe in human beings.” Union leaders are escalating their anti-AI rhetoric, portraying the industry’s leaders as profit-hungry “oligarchs” eager to replace humans.

☀️ Shine (not drill), baby shine: IEA – Solar overtakes all energy sources in a major global first.

✍️ Hacking the system: A college instructor turns to typewriters to curb AI-written work and teach life lessons.

☕ A wonderful lesson in taking something that worked (ordering coffee through a carefully designed app) and making it worse by using AI: Ordering with the Starbucks ChatGPT app was a true coffee nightmare.

🧑‍⚖️ Let AI be the judge: Cooperative negotiation is a solvable problem (or so says this company).

🔐 Turns out – your cybersecurity does, in fact, withstand the (possibly coming) wave of quantum computer-powered attacks (despite the attention-grabbing headlines): Quantum computers are not a threat to 128-bit symmetric keys.

🎠 Ed Zitron, one of the most outspoken critics of AI, is back (and it’s worth reading – even if you don’t agree with him): Four Horsemen of the AIpocalypse.

↗ Dive into the deep end: Access our complete collection of 2,700+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

The, quite possibly, craziest story to emerge this week from the ever-nutty world of AI hype is, of course, the rebrand/relaunch of sneaker company Allbirds as an AI company – resulting in a $127 million increase in stock market value. I don’t even comment on how absurd all of this is. You know something is up when even the most die-hard AI-boosting publications start calling BS… Anyway – time for your weekly dose of news and analysis…

And now, this…

Headlines from the Future

Better Drug Side Effects Monitoring through Reddit? 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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Let’s Talk About AI’s Energy Footprint (Again). 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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The GenZ AI Tide is Turning. 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.

What We Are Reading

The Air Is Full of DNA - Here’s What Scientists Are Using It for Genetic breadcrumbs in the air reveal ecosystem secrets, spot sneaky invaders, and even track humans! @Jane

My Quest to Solve Bitcoin’s Great Mystery A detailed read about one man’s journey to find out who’s behind Satoshi Nakamoto. @Mafe

How To Future-Proof Your Career In The Age Of AI If cognitive flexibility, taste, and good judgment become critical differentiators in a world of abundant intelligence, does the most valuable background begin to look a lot like a classical interdisciplinary, liberal arts education? @Jeffrey

Will AI Start ‘Going Rogue’? the Chorus of Warnings Is Getting Louder When the people building the tech warn about loss of control, it may be a signal worth paying attention to. @Kacee

The Peril of Laziness Lost Here is an interesting argument from the world of software development: Laziness (in coding) leads us to more elegant, better-performing, and cleaner code. With AI coding tools, laziness suddenly has stopped being a virtue – if nothing else, AI happily keeps churning… And with laziness becoming a lost art, software will become worse. I’d venture to say that this is what is happening in every area AI touches. @Pascal

Down the Rabbit Hole

🍸 Rejoice! It is now legal to distill your own alcohol in the United States: US appeals court declares 158-year-old home distilling ban unconstitutional.

☠️ Nothing to see here. OpenAI backs bill that would limit liability for AI-enabled mass deaths or financial disasters.

🧑‍⚕️ Surprised is no one: Scientists invented a fake disease. AI told people it was real.

🎲 Ever wanted to increase your chances of beating your niece at Connect Four? Here’s the mathematically best way to do it: WeakC4, or distilling an emergent object.

🇪🇺 European tech sovereignty is a thing. It will be interesting to see how this plays out in long run. Latest point in case: France ditch Windows for Linux to cut reliance on US tech.

🔌 Have we reached the tipping point? Seven countries now generate 100% of their electricity from renewable energy.

✈️ Desperate times call for desperate measures. Great at gaming? US air traffic control wants you to apply.

🧑‍🎨 Life imitates art. This feels like it’s right out of an episode of Black Mirror: Mark Zuckerberg is reportedly building an AI clone to replace him in meetings.

✍️ You become what you write: Biased AI writing assistants shift users’ attitudes on societal issues.

📶 Data becomes a right. South Korea introduces universal basic mobile data access.

🤓 Nerd alert! Fascinating approach to improving AI’s coding abilities: Having a coding agent read a series of papers on the topic at hand before coding results in significant improvements in code quality.

📚 Lovely read: Stewart Brand on how progress happens.

🤔 More than half of Americans are ‘getting tired of hearing’ about AI, survey finds.

📈 From the MIT Tech Review: Want to understand the current state of AI? Check out these charts.

🪖 PSA: Wear your helmet! E-bike and scooter crashes are leading to more brain injuries.

↗ Dive into the deep end: Access our complete collection of 2,700+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

I honestly don’t even know where to begin – so much stuff is happening in the world right now; it truly is a whirlwind. From your usual (over) dose of AI, to geopolitics – but also a plethora of wild, weird, and wonderful weak signals… Like the bike bell which cleverly defeats the noise-cancelling technology of a pedestrian’s earbuds. Or AI-singers capturing the top spots in the iTunes charts (now, remember – this is iTunes, the $0.99 a song download store, which makes that whole story even more bizarre). Dig into today’s Briefing – the results from this week’s web explorations will keep you busy.

P.S. In case you missed it – I built on Kacee’s excellent post in the last radical Briefing on “Vibe Coding Our Way to 70%” in a LinkedIn post.

And now, this…

Headlines from the Future

The AI Quiet Quitters. 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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China Is Coming for You, Lil Miquela. If you know us, you know that we’ve been talking about virtual humans (and more specifically, virtual influencers) for a long time now. Our particular example was always Miquela Sousa, a virtual influencer created by the LA-based design agency Brud. Our particular fascination with Miquela and her brothers and sisters centers around the fact that she never ages, never gets sick, never has a bad hair day, travels anywhere, and works 24/7 without a break. Since we talked about her in 2017, she was joined by an ever-expanding family of virtual humans. Now China is closing in on them:

The Cyberspace Administration of ‌China’s proposed rules would require prominent “digital human” labels on all virtual human content and prohibit digital humans from providing “virtual intimate relationships” to those under 18, according to rules published for public comment until May 6.

and

“The governance of digital virtual humans is no longer merely an issue of industry norms; ⁠rather, it has become a strategic scientific problem that concerns the security of the cyberspace, public interests, and the high-quality development of the digital economy,” it added.

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Digital Transformation is (Finally) Dead. 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.

What We Are Reading

Is AI Going to Turn Us All Into Middle Managers? Two of our favorite people, Johnathan and Melissa Nightingale, just gave one of the sharpest takes we’ve heard on AI, management, and the future of work. Go find their Galaxy Brain conversation. @Jane

Crypto Investment Scams Were the Most Costly Type of Fraud in the U.S. in 2025 Investment fraud, specifically crypto investment scams, accounted for 49% of all cyber-related complaints in 2025 to the FBI. @Mafe

AI Fatigue Is Real and Nobody Talks About It The real value is in sustainable output, and learning to work – sustainably – on new rhythms will be a significant piece of the AI transformation puzzle. @Jeffrey

When Silos Hinder Innovation – and When They Can Help Rethinking the innovation dogma… silos aren’t always the enemy; sometimes they can spark the best ideas. @Kacee

Are We Idiocracy Yet? Remember Mike Judge’s masterpiece, Idiocracy? If you have ever asked yourself how far the movie is from today’s reality – here is your answer. @Pascal

Down the Rabbit Hole

👨🏼‍💻 In the same vein as my comment on Kacee’s post from last week’s radical Briefing (see above), a leader at the global consulting firm EY wrote, “Why engineering replaces transformation as the engine of growth.” It’s worth a read.

🕵🏼‍♂️ The journalist who uncovered the Theranos scandal is behind the (maybe) next big unveil: Satoshi Nakamoto, the mysterious creator of Bitcoin, might have been found.

💻 Here’s a fun anecdote – as the world, once again, seems to be obsessed with LOC (lines of code) as a productivity metric, legendary software developer Bill Atkinson recalls delivering -2,000 lines of code to Apple.

🧑🏼‍🏫 Some things you just can’t make up: Students record their professors’ lecture, feed it into a speech-to-text AI, to then feed it into an LLM, to then ask/comment/respond to their teacher – in his tone and style (as the AI mimics the import).

🤥 Claude (the AI model) might be a little confused as to who said what: Claude mixes up who said what.

🎙️ The fake singers are coming – and they are coming for your top spots on the charts: iTunes takeover by fake AI singer “Eddie Dalton” – now occupies eleven spots on singles chart, number 3 on albums chart.

🤔 Take headlines like these with a huge grain of salt: “AI models will secretly scheme to protect other AI models from being shut down, researchers find.” Here is the study in question – and you shouldn’t be too surprised about the result, knowing that AI models are modelling their training data.

🔮 On the topic of predicting the future (when it comes to AI), here is the latest update from the folks at the AI Futures Project (yes, those were the folks who did the very optimistic/accelerated AI 2027 forecast).

🤷🏼 Ethan Mollick, the Wharton School professor who coined the term “jagged frontier” in his assessment of LLMs and their capabilities, makes the argument that the IT department is where AI goes to die.

🤳🏼 Take their phones away from them, and the kids will be fine! Well, not so fast… Australia’s teen social media ban is a flop. But there’s no joy in ‘I told you so’

🪫 The AI wars might be won over energy, not compute: Half of planned US data center builds have been delayed or canceled, growth limited by shortages of power infrastructure and parts from China – the AI build-out flips the breakers

👨🏼‍💼 Fire the people, save money, build AI data centers: Oracle’s 30,000 employee layoffs: Inside the $2.1 billion restructuring fueling a $156 billion AI data center bet.

⚡ Energy markets are turning very, very weird with the rise of renewables: Germany power prices turn deeply negative on renewables surge.

🚲 Signs of the times: A bicycle bell that outsmarts even smart headphones.

🪖 Talking about the future of warfare: Iran threatens “complete and utter annihilation” of OpenAI’s $30B Stargate AI data center in Abu Dhabi – regime posts video with satellite imagery of ChatGPT-maker’s premier 1GW data center

💼 Surprised is no one: Employers are using your personal data to figure out the lowest salary you’ll accept (but then, employees also write their resumes and cover letters using AI, cheat on tests using AI, etc.)

🧑🏼‍🚀 Just in time, as Artemis is doing its moon thing – calculate your cosmic distance from the day you were born.

🌘 Talking about the moon – this is as nerdy as it gets: The rebuilding of the Apollo guidance computer in glorious detail.

🌈 The Weather Channel goes full retro with their neat, new retrocast feature.

🚉 Can you identify each line on the London Underground by sound? Try it!

👨🏼‍🎨 The amazing art that went into the special effects for the Luc Besson movie The Fifth Element. Stunning.

↗ Dive into the deep end: Access our complete collection of 2,700+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

Then we’d put it in front of a user. Fifteen seconds. “I don’t get it.” Three days of work – DOA (“dead on arrival” – fun fact: also the name of a meeting room at Mozilla at the time).

At that pace we could test maybe ten ideas a month. Product-market fit usually takes hundreds of iterations. We were years away from finding it, and we simply didn’t have years.

One afternoon, after yet another fifteen-second rejection, I did something that felt outright strange at the time: I asked a colleague to close his laptop, grab a stack of index cards and some Sharpies, and start drawing. He looked at me like I’d suggested interpretive dance. He was a C++ and Python person – not someone who drew doodles on index cards. But he did – badly, reluctantly, beautifully – and we walked those cards across the street to the Starbucks on Castro Street in Mountain View, which at the time was basically Silicon Valley’s cafeteria. “Can I buy you a coffee in exchange for five minutes of your time?” More than 80% of the people we asked said yes. We’d place the first card on the table, get feedback, retreat to a corner to redraw, find the next stranger. By the time the caffeine jitters set in – three hours, maybe – we hadn’t tested one prototype. We’d tested thirty.

That afternoon changed the way I think about everything. Three days to learn one thing with code. Three hours to learn thirty things with Sharpies and index cards. Not because the engineers were slow, but because the medium was expensive. High-fidelity code carries a high cost of failure – emotionally, financially, temporally – and when failure is expensive, you instinctively avoid it. You plan more. You debate more. You polish more. You learn less.

Which brought me to the idea I’ve spent the last fifteen years trying to articulate as precisely as I can – what I now call the Core Theorem: the speed of learning is inversely proportional to the cost of failure. If failure is expensive, you learn slowly. If failure is cheap, you learn fast. That’s it. That’s the whole thing. And it governs the survival of every organization operating in a volatile world, which – in case you haven’t checked the news lately – is every organization.

The logic is disarmingly simple. When a mistake could cost you $100,000, your reputation, or your job, you’ll hesitate. You’ll double-check. You’ll form a committee. You’ll bring in a consultant. You’ll optimize for the appearance of competence instead of the reality of learning. But when a mistake costs $10 and an awkward conversation at a coffee shop? You’ll just try. And if that fails, you’ll try something else – all before lunch.

Tom Chi, one of the founding members of Google X – the moonshot factory behind self-driving cars and Internet balloons – understood this better than anyone. When his team was working on Project Glass (which became Google Glass), the engineers estimated it would take six months to build the first working prototype. Optics, miniaturized projection, ergonomics, software – hard technology, expensive to get wrong. Tom walked out of the room and came back 45 minutes later with a coat hanger bent into a neck loop, a sheet of plexiglass, a middle-school sheet protector taped to it, and a pico-projector connected to a netbook. It looked like garbage. It cost less than $500. And within an hour his team had learned that red text washes out, the upper right corner gives headaches, and email pop-ups are socially awkward. They learned more in one afternoon with a coat hanger than they would have in six months of “proper” engineering – because the cost of being wrong was almost zero.

The engineers wanted to predict the solution. Tom wanted to ping the solution space. The beautiful irony is that by refusing to build the “real” thing, he got to the real thing faster than anyone else.

Most organizations get this backwards. They shout “go faster!” while keeping the cost of failure high. They say “fail fast and fail forward” while promoting the people who never make mistakes. They create innovation labs and demand agile workflows – but require three signatures to approve a $500 experiment. The incentive structure contradicts the aspiration, and incentives always win.

So here’s the practical question: What is your coat hanger? What is the cheapest, fastest, ugliest version of the thing your team has been debating in conference rooms for six months? And what would it take to test it this week – not next quarter, not after the strategy offsite, not when the budget gets approved – but this week?

Audit the price of your errors. Count the signatures required to run a small experiment. Look at what happens to the person who tries something and fails versus the person who sits in meetings and never ships. That gap – between the stated value of learning and the actual cost of failure – is where your organization’s speed goes to die.

Close that gap, and you don’t need to hire faster people or buy better tools. You just need to hand them a Sharpie and point them at the nearest coffee shop.

This briefing is adapted from my new book OUTLEARN: The Art of Learning Faster Than the World Can Change, launching April 28. It’s the first volume in a series called Built for Turbulence – short, framework-dense field manuals for leaders who are done planning beautifully and ready to start learning fast. More on that soon.

@Pascal

This week has been one of contemplation – as is evidenced in our “Headlines from the Future” section. While AI keeps moving at lightning speed, it feels like we (the collective “we”) are starting to get our feet under us and figure things out…

Meanwhile, a quick personal note before the links: my new book OUTLEARN – The Art of Learning Faster Than the World Can Change – launches April 28 on Amazon. It’s the first volume in a new series called Built for Turbulence: short, framework-dense field manuals for leaders operating in volatile environments. I’ll share more next week in the Tuesday deep-dive. 🤘🏼

And now, this…

Headlines from the Future

The AI-CEO Threat. Here’s an interesting one – the CEOs of major companies are stepping down to make room for people with a better grip on AI.

“In a pre-AI, a pre-gen-AI mode, we made a lot of progress. But now there’s a huge new shift coming along,” Quincey said. While he said he’s leaning into the technological advances, he believes the beverage company needs “someone with the energy to pursue a completely new transformation of the enterprise.”

It does make you wonder a) how many CEOs are hanging on to their jobs by the skin of their teeth, b) how many CEOs are oblivious to what the AI transformation actually means for their companies, and c) how many more CEOs we will see throwing in the towel and handing over the reins to new generations. Now might be a good time for folks with CEO aspirations (and a solid grip on AI) to step up…

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Thinking Fast, Slow, and Artificial. In 2011, Nobel Prize winner Daniel Kahneman published his bestselling book “Thinking, Fast and Slow.” In it, he describes the two modes of thinking we all operate in: System 1, which is fast and intuitive, and System 2, which is slow and deliberate. Now, in a new paper, Steven D. Shaw and Gideon Nave from The Wharton School argue that AI introduced a third mode of thinking:

People increasingly consult generative artificial intelligence (AI) while reasoning. As AI becomes embedded in daily thought, what becomes of human judgment? We introduce Tri-System Theory, extending dual-process accounts of reasoning by positing System 3: artificial cognition that operates outside the brain. System 3 can supplement or supplant internal processes, introducing novel cognitive pathways.

And, as you would expect, with it comes a whole host of questions: “System 3 reframes human reasoning and may reshape autonomy and accountability in the age of AI.” The study is worth reading…

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AI Learning Curves Are Real. Anthropic, maker of Claude, released yet another report on the usage of AI (I applaud them for doing this – their reports tend to be actually useful, and not the usual company-sponsored “look how great we are” puffery). This time, they dug into the use of AI across the economy. Lots of good nuggets in the paper; the one standout for me is their insight into how the jagged edge, the concept popularized by Ethan Mollick, plays out in the real world (this is paraphrased):

There’s a compounding dynamic at play: experienced users bring harder problems, get better results, and develop sharper instincts for working with AI – while later adopters are still figuring out the basics.

In essence: Early adopters with high-skill tasks have more successful interactions with Claude than later, less technical adopters – and these early-adopting users may simultaneously be the most exposed to AI-driven disruption and most aided by AI in these initial, augmentative waves of adoption. As my mom used to say: Be careful what you wish for.

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Is AI Slop Our Future? AI Slop is seemingly everywhere these days. And it’s getting worse. But here is an interesting counter-argument (at least when it comes to code):

[…] AI models will write good code because of economic incentives. Good code is cheaper to generate and maintain. Competition is high between the AI models right now, and the ones that win will help developers ship reliable features fastest, which requires simple, maintainable code. Good code will prevail, not only because we want it to (though we do!), but because economic forces demand it. Markets will not reward slop in coding, in the long term.

In simple words: “AI will write good code because it is economically advantageous to do so.” I do believe this to be true (we already see this with the quality of code generated by frontier models such as Claude Opus/Sonnet 4.6). It will be interesting to see how this plays out – there might be a real incentive for AI companies to compete on quality, which would be a very “free market” thing to do.

What We Are Reading

The Jobs AI Can’t Do – and the Young Adults Doing Them A new generation is redefining what a good job looks like. Hands-on trades are shedding their stigma, replaced by something more compelling: skilled work no machine can replicate. @Jane

Apple Turns 50 Wozniak on Apple: The secret to the company’s success was it managed its brand well and didn’t make “lousy junk” that breaks down. @Mafe

The Case Against Political Prediction Markets Straight from dystopia, a valuable lesson that we keep relearning: Maybe not everything should be a market. @Jeffrey

What Leaders Get Wrong About Responsibility Leaders love to “hold people accountable” – fewer know how to build systems where responsibility organically shows up. @Kacee

PIEZODANCE Not a read this week, but a video. And not just a video, but a contemporary dance video – this year’s winner of the “Dance your PhD Thesis” competition is all about energy – and it’s stunning. @Pascal

Down the Rabbit Hole

🔒 We have been talking about this since the early days of Singularity University, now it’s closer than ever: “Google warns quantum computers could hack encrypted systems by 2029.” Time to update your security keys (there are quantum-secure password-generating algorithms; you just have to use them).

🚦 Similarly, we have been talking about vertical AI models for a while now (well, “a while” in AI-timeline terms) – they, also, are closer than ever: The age of vertical models is here.

💸 BlackRock’s Larry Fink warns that “artificial intelligence could widen wealth inequality if ownership does not broaden alongside it” – i.e., those who invest in stocks will benefit; those who cannot will be left behind.

🧑🏼‍🏫 Tech up, testscores down: Amid declining test scores, Sweden has pivoted away from screens and invested in back-to-basics school materials (i.e. books).

🐭 Hold my beer: Disney’s Sora disaster shows AI will not revolutionize Hollywood.

🔬 Surprised is no one: The shocking speed of China’s scientific rise.

🦜 All it takes is five seconds of your voice – Mistral’s newest voice cloning AI is scarily good.

👔 The easy way out: Tech CEOs suddenly love blaming AI for mass job cuts. Why?

🎭 This is just too good: Someone trained a large language model solely on Victorian-era literature. The result: Mr. Chatterbox

🤖 AI bots now make up more than 50% of all Internet traffic.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

There’s an early signal I’ve now seen enough times in the wild that it’s hard to dismiss as anecdotal, even if each individual instance still sounds like one. Over the past few weeks, I’ve had multiple conversations with CEOs of tech startups who are starting to receive a version of the same feedback from potential customers: instead of buying software, prospects are increasingly deciding to vibe-code a solution themselves. Not because it’s better, but because it gets them far enough.

That “far enough” is landing, with surprising consistency, around 70%.

I raised this at a thought leader symposium in Dallas last week, expecting at least some pushback, and instead got immediate agreement. One firm owner said plainly that rather than paying $300/mo per user for an off-the-shelf product, the agent-built 70% solution is good enough in the current environment. Another chimed in (not a developer by any stretch) and said he’s been building things on the weekends simply because it’s fun. This isn’t just a cost decision, it’s a behavioral shift.

What’s striking is how quickly the boundary of what people “won’t build themselves” is collapsing. In an internal discussion at radical, the point was raised that surely there are still limits - that people aren’t going to start vibe coding their own general ledger. And if you read the recent briefing, someone had done exactly that. By his own admission, it wasn’t particularly good, and he wasn’t using a complex GL to begin with, but it worked for his business. Around the same time, I saw a CEO share on LinkedIn that he had spent a weekend building a replacement for HubSpot. Again, not best-in-class, but usable and to his own preferences.

Individually, these are easy to write off…together, they form a pattern. My instinct, honestly, is still that this has limits. Not every system will get vibe-coded into existence, but I’m increasingly unsure where those limits actually are. That uncertainty feels more important than whatever answer I’d have given six, or even three months ago.

TechCrunch has already leaned into the narrative of SaaSpocalypse, which may or may not be more marketing fodder than reality, but it points to something worth paying attention to. Because the more interesting dynamic here isn’t whether these self-built solutions rival existing software - they don’t. It’s that they don’t have to because the standard isn’t excellence anymore. It’s sufficiency, shaped by context, constraints, and increasingly, by a willingness to trade polish for control. What’s notable is that this isn’t just showing up in conversations, it’s already impacting markets. Last month, a single release from Anthropic triggered a roughly $285B selloff across the software sector.

It would be convenient to attribute this entirely to economic pressure. Budgets are tighter, scrutiny is higher, and software that once felt like a default purchase now has to compete for its place. That’s real, and it’s accelerating the behavior. The structural shift underneath all of this is simple: the cost of creating software has dropped below the perceived cost of buying it - and when that inversion happens, the starting point changes. You don’t begin with procurement, you begin with construction.

What sits underneath that shift, is that software is quietly moving from something standardized to something individualized. For the last two decades, SaaS has been built on a kind of implicit compromise: you adopt a system designed for the average user, and in return you get scale, reliability, maintenance and convenience. But when the cost of building collapses, that tradeoff starts to feel less necessary. Instead of adapting your workflows to fit a product, you can increasingly shape the product to fit your workflows. It’s messier, and often incomplete, but it’s also more precise…and for many use cases, that precision matters more than polish.

Pascal’s framing in the briefing around bifurcation is useful here, not as theory, but as a way to understand where this is going. We’re watching the market split between systems where completeness and trust are non-negotiable, and a much larger surface area where “good enough” is not just acceptable, but rational. The 70% threshold is emerging as the dividing line; above it, you still buy – but below it, more and more people are choosing to build.

I think what makes this particularly important, is that it reframes competition in a way that most companies aren’t prepared to handle. The threat isn’t another product with a better roadmap or a tighter feature set, it’s a user who decides they don’t need the category in the first place. A small business owner comparing a self-built ledger to Quicken isn’t benchmarking against enterprise accounting software. A founder assembling a CRM over a weekend isn’t trying to replicate HubSpot in full. They are solving a narrower, more individualized version of the problem - and in doing so, stepping outside the boundaries that defined the category. Jeff Seibert, the CEO of Digits, put language to this in a way that’s worth paying attention to, “the second-order effects will be fascinating. When software is cheap, it’s taste and distribution that matter.” This framing pulls the conversation out of tooling and into consequences.

And that opt-out dynamic is the signal.

Once someone successfully builds one thing, even imperfectly, the barrier to building the next drops dramatically. Capability compounds, confidence compounds, and what starts as experimentation begins to normalize into an alternative path: one that doesn’t rely on waiting for software to catch up to your needs, because you’ve already adjusted it yourself.

The implication isn’t that 70% gets better (although I’m sure that number continues to improve as the coding models mature) it’s that once users believe they can build for themselves, the default posture shifts from buying software to questioning whether they need it at all.

@Kacee

Pardon my French (in my defense, it’s not my headline), but Mario Zechner’s “Thoughts on slowing the f*** down” is a good reminder that all the wondrous things AI can and does do for us come at a cost – hence his reminder to: “[…] slowing the f*** down and suffering some friction is what allows you to learn and grow.” With that in mind – time to slow down, welcome the weekend, and dive one last time into our wild future before we call it a Friday.

P.S. A new episode of our podcast dropped: Jason Goldberg has spent 30 years watching companies survive – and get destroyed by – disruption in retail. His counterintuitive advice for the agentic commerce moment: stop trying to be first, and start asking what you’ll regret not doing when the future arrives.

And now, this…

Headlines from the Future

AIs Energy Demands Are Truly Bonkers. Japanese tech giant SoftBank is building a massive 10GW data center in Ohio to host AI models. Aside from the cool $30–40 billion price tag, it will require the build of a $33 billion natural gas power plant – with an insane output capacity (emphasis mine):

When completed, the new site could be one of the largest AI data centers ever built. Furthermore, it will be powered by one of the world’s largest fleets of gas turbines, equivalent to the energy supply of nine nuclear reactors.

It does leave you wondering where and how all this will end.

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Maybe AI Isn’t Online Shopping’s Future After All. After the initial hype of online shopping results being incorporated into the answers LLMs give to the numerous product-related queries they receive, Walmart unveiled that the conversion they are seeing from those AI-referrals is just terrible.

After testing 200,000 items in ChatGPT, Walmart found sharply lower conversions and will use its own integrated shopping experience. Walmart said conversion rates for purchases made directly inside ChatGPT were three times lower than when users clicked through to its website.

Next: Agentic commerce. The jury’s out.

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What 81,000 People Want From AI. Anthropic, the AI company which is not OpenAI, conducted what is, in their own words, likely the largest study on users’ desires, wishes, and fears when it comes to their use of AI. Anthropic being Anthropic, they didn’t survey people using a traditional questionnaire, but rather had their chatbot “talk” to people. The findings won’t surprise you – people want to use AI to better themselves: professional excellence and increased productivity, which translates into the very human desire to, ultimately, live better. And respondents live the Scott Fitzgerald quote we are so fond of quoting – they keep the light and the dark of AI in their heads simultaneously.

“AI should be cleaning windows and emptying the dishwasher so I can paint and write poetry. Right now it’s exactly the other way around.”

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AGI? Not so Fast! AGI, or Artificial General Intelligence, is the thing Sam Altman and others love to talk about – and promise is just around the corner. To demo their respective companies’ progress, they roll out benchmark after benchmark showing how their AI beats humans on the sommelier exam. A new benchmark, however, shows that AGI is still a long, long way off. The ARC-AGI-3 benchmark pits leading AIs against humans in a series of computer games – and AIs don’t look all that great. To apply a lesson my statistics professor hammered into our heads: Never trust a statistic you haven’t faked yourself.

What We Are Reading

Ads Are Popping up on the Fridge and It Isn’t Going Over Well Ads are literally popping up everywhere (even on Google Maps starting this summer), but people are particularly irked by ads on expensive refrigerators with a big screen for recipes, weather updates, and, apparently, ads. @Mafe

The Insurance Catastrophe A deep dive into the history & future of insurance markets offers a fascinating lens for exploring how communities, societies, and economies deal with radical uncertainty and catastrophic risk. @Jeffrey

The 60-Year Degree: Why Universities Must Pivot from Recruitment to Perpetual PartnershipHigher ed has been at an inflection point for years; the degree is just the 1st casualty of a shift to lifelong contracts. @Kacee

AI Slop Is Infiltrating Online Children’s Content Surprised is, of course, no one. But it does leave you wondering what happens to the brains and cognitive development of children who are exposed to AI slop from an early age. @Pascal

Down the Rabbit Hole

🍿 Hilarious take on the world of Enshittification by the Norwegian Consumer Council (hat tip to Angel Grimalt for the link).

🥷🏼 AI agents going rogue: The more we rely on AI, the more we deploy AI agents, the more we see fun headlines like this: Meta is having trouble with rogue AI agents – now consider what this means for any company not the size of, or with the resources of, Meta!

🍺 Talking about cyberattacks and our ever-increasing reliance on Internet-connected technologies: Cyberattack on vehicle breathalyzer company leaves drivers stranded across the US.

👨🏼‍💻 Nerd alert! But super helpful: Here is a Claude Skill – Prompt Master – which helps you create better prompts, highly optimized for specific use cases, tools, and target LLMs.

🤦🏼 Yep, bro… Whatever. “Perplexity CEO says AI layoffs aren’t so bad because people hate their jobs anyways: ‘That sort of glorious future is what we should look forward to’”

⚓ The running and cycling app Strava has been used to track the location of military outposts before – now the French newspaper Le Monde has used it to track the location of France’s aircraft carrier. Note: Your public data is public data.

🧬 Fascinating read on the adaptability of the human body: Tribe in Kenya evolved genetic mutation that lets them survive with almost no water.

🥢 A Japanese glossary of chopsticks faux pas.

🧳 Lovely journey through 30 years of the web.

🎧 Peak 80s nostalgia: The Maxell Wireless Cassette Player.

↗ Dive into the deep end: Access our complete collection of 2,600+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.