Showing posts with label culture. Show all posts
Showing posts with label culture. Show all posts

Why the CTO chair keeps emptying

Mike's Notes

This is the first part of an article by Gergely Orosz, available to read for free. The rest is for paid subscribers. The article was introduced in a recent issue of The Code.

I found this important for understanding what is happening in workplace culture at large SaaS/AI firms. Things to avoid at Ajabbi. For future reference.

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Last Updated

07/09/2026

Why the CTO chair keeps emptying

By: .
The Code: 24/08/2026

.

An exodus across Silicon Valley. Something is spooking the people who run engineering. Across startups and Big Tech, CTOs, VPEs, and heads of engineering are quitting high-status roles with nothing lined up. Veteran engineer Gergely Orosz says he's never seen this many top-tier leaders walk. In his latest deep dive, he spoke to nearly 20 on career breaks. Six in ten were already on their way out.

A range of reasons. From rapid AI adoption (or the lack of it) to long hours and burnout. Then there’s what Orosz calls “founder mode.” In smaller companies, founders are micromanaging engineering, turning VP and CTO roles into low-ROI gigs. Some leaders left to build their own startups; others were pushed out.

Across the board, these were the most consistent reasons:

  • The job got worse. Picture a founder celebrating a massive AI-generated PR while the CTO stares at the technical debt underneath. Flag the mess, and you become the office killjoy.
  • Equity looks like smoke. Leaders are trading salary for equity that feels like a gamble. Many CTOs now realize their options might never pay out due to VC payouts.
  • AI-native or bust. The best roles now want leaders who’ve actually led an AI transformation. If their current job can’t offer that, some are stepping back into IC roles rather than letting their skills go stale.
  • Teams are shrinking. Agents now handle work that once required entire teams, meaning fewer engineers and fewer layers of management.

It doesn’t stop at the top. When leaders walk, the ripple effect hits the entire team. If you're taking a leadership role, interview the company too: do founders want engineering to change, or just get cheaper? And if your job isn't giving you hands-on AI experience, get it elsewhere.

P.S. Our engineering team put together over 10 practical guides to help you kick-start your journey toward becoming an AI-native engineer. Pass this along to your colleagues today.

Headed for the Exit: the Great Engineering Leader Career Break

By: Gergely Orosz.
The Pragmatic Engineer: 19/08/2026

Big Tech and startups from the inside. Especially relevant for software engineers / AI engineers, useful for anyone working in tech.

...

Trend: more CTOs, VPEs, and Heads of Engineering are walking away from their high-status, in-demand positions. There are many reasons, mostly related to AI, and to "founder mode"

...

In my ~20 years in this industry, I’ve not seen as many capable engineering leaders opting out or taking prolonged breaks as now, with some high-ranking engineering leaders – CTOs, VPs of Engineering, heads of engineering, etc. – quitting their high-status roles and departing, if not into the sunset, then at least with nothing lined up.

To find out what might be behind this spate of sign-outs, I talked with almost 20 engineering leaders currently on a career break – or seriously considering one – and they let me into their personal reasons for deciding to jam the brakes on their careers. Thanks to everyone who shared their input!

Today, we cover:

Ten of the most common reasons for quitting, sometimes without the next gig lined up:

  1. The job got (much) worse
  2. The startup is “losing” and becoming worthless
  3. Not being AI-native enough for other skills to be relevant
  4. Their predecessor saw the “writing on the wall”
  5. Long hours – rarely decisive
  6. Smaller teams mean less need for leaders
  7. Fractional CTO work preferred over fulltime positions
  8. AI startups pay ICs more than non-AI startups pay executives
  9. Quitting to launch their own business
  10. Burnout
  • “Founder mode” looks here to stay, so how to deal with it? And has it made the CTO and VPE roles become “low ROI”?
  • ‘Work at companies that truly want to drive change’. A personal account from someone who took the VP of Engineering role at Gitpod (later, Ona, now acquired by OpenAI) and enjoyed a rewarding experience. Matt Boyle says he interviewed the employer beforehand on whether their business truly leans into the changes brought by AI.

“Just me?”

I was recently messaged by a head of engineering in San Francisco, who said:

“I’m talking to four startups in San Francisco about the head of engineering roles. Pretty normal.

But one interesting pattern is how founding CTOs/heads of engineering are stepping away to take a full career break. We’re talking about two of these four startups. And these are good startups!

Have you seen this trend? I have a small number of data points here, so you might have a broader view.”

I asked around privately, and it turns out a majority of the CTO-level folks I spoke to are considering the very same thing, or are actually in the process of leaving the office for a long spell away; 6/10 engineering leaders said they’re on the way out.

1. The job got (much) worse

Unrealistic expectations, including about AI, by founders and CEOs are the leading cause of jobs turning bad for CTOs and VPEs right now in 2026:

  • CTO expected to magically transform the company to be “AI-native”
  • CTO must make significant engineering cost cuts of up to 20-50%, including morale-sapping job cuts
  • “Do more with less” equals shipping more with fewer people (e.g., no backfills)
  • CTO faces pressure on business results as AI coding bills rack up
  • Founder slop: they want wonky AI prototypes shipped as full-blown products within weeks

Hands-on founders with “AI psychosis” make the job predictably harder, according to one CTO who just signed out of his job:

“Managing ‘AI psychosis’ with founders and executive peers has become very difficult. For example, what do you do when a founder ships a 60,000-line pull request into the product, gleaming with joy at how much more productive they’ve become with AI? They won’t see all the issues with that PR, and how do you bring up that they’ve created a massive amount of tech debt? Especially without looking like a ‘Debbie Downer’.”

Founder slop issues begin when top leaders get excited about AI’s capability, then get hands-on and start issuing PRs, and shipping code to production. It can cause issues across the board:

  • Accountability. Who’s oncall when founder-shipped code breaks? In the “you build it, you own it” culture of startups, it’s confusing when a founder gets hands-on while not owning their work.
  • Quality out the door: if a founder’s half-baked features are accepted, it sends the wider message that quality does not matter. Some people may adopt this attitude to their own work.
  • A founder can overrule whatever was previously agreed with the CTO or VPE about what to build next. Vibes the founder has or feels are reason enough.

Another way that leadership roles have diminished is that craft and quality are less important, says a VP of engineering who’s in the process of signing out of their job:

“Shipping software became all about speed. Finding differentiation with your product in the market is brutal, and speed / go-to-market becomes the biggest differentiator. Craft, quality, and care going into the product are taking a backseat.”

Things also go bad when companies don’t ‘get’ AI+engineering, except as a way to cut jobs. CTOs I talked to mentioned the likes of Ramp, Stripe, and Notion as places that understand how to integrate AI into the engineering culture with a growth mindset without forsaking quality. Elsewhere, bad vibes dominate at places where going all-in on AI leads to the cynical conclusion that product management, design, and engineering leadership are irrelevant.

2. The startup is “losing” and becoming worthless

Director+ roles have a few differences from individual-contributor engineering ones:

  • Larger equity stake in the business. Base salary at these levels is often similar to a staff engineer’s, but usually with more generous equity grants – especially at the VP of Engineering and CTO levels. A good financial outcome depends on the company becoming more valuable, and – in the case of private companies – having a good exit by being acquired or selling shares.
  • Understanding of the business and competition is a baseline. At Director+ level, a big part of the job is making strategic decisions that grow the business and help the company get ahead. It’s a nice-to-have for an engineer to possess business acumen, but director-and-above folks use it much more than most individual contributors (ICs). Great engineering leaders are good at understanding business performance and outlook.

A company that adopts AI rapidly usually falls into one of three buckets:

  • “AI-native”, building & selling AI products. The large AI labs and a select few “AI-native” startups are thriving, but many AI startups with VC funding struggle. Engineering leaders know this, and that their equity – usually issued as options – could end up worthless.
  • Software startups threatened by AI-native businesses. Good businesses in the pre-AI world can be threatened by AI today, like SaaS startups selling seat-based products in areas where agents are taking over the functionality. They have to pivot their businesses or seek an exit. Bending Spoons buying Airtable for less than the company raised is an example of a business threatened by AI and choosing to sell, instead of pivoting the whole business.
  • Unaffected by AI. Usually stable businesses which do more than software, such as with a real-world side to the operation like manufacturing or distribution.

The majority of software startups fall into one of the first two buckets of being AI-native or under threat. Senior leaders at such companies are in a good position to evaluate whether their company is a “winner” worth staying with.

Leaving due to equity becoming worthless

A CTO who quit their startup told me:

“My company would have needed a massive exit for me to realize any upside. I had an equity grant that was 2% of the common shares. However, this equity was behind an already steep preference stack for investors, post Series A.”

This CTO had a very generous equity grant at 2% of shares, so what made him leave it behind? They laid out how it will be difficult to get any benefit from them because the shares are most likely rendered worthless by rules about the order in which different investors get their share of the pie:

  • Assume that this company raised a $10M seed round at a $50M valuation, then a $100M Series A at a $500M valuation. So, a total of $110M was raised across two rounds.
  • Investors typically have a 1x preference. 1x preference would mean that upon any sale, they get the first $110M of the sale.
  • But in this company, the Series A investors negotiated a 2x preference: so upon a sale, $210M goes to investors first ($10M to the Seed, and $200M to the Series A investors).
  • The company now needs to sell for at least $210M for common shareholders (like the CTO) to make any money!
  • If the CTO does not believe a $200M+ exit could happen, then their equity is worthless. A $200M+ exit is typically an acquisition, because a stock market flotation rarely happens at below a $10B+ valuation, these days.

If a VC-funded company does not have the revenue or customers to grow at a fast tick (circa 20-50% per year), then it’s often a struggle to raise the next round of funding, and the business’s actual value usually shrinks to 3-5x of annual revenue. So, if a startup is making $10M per year after raising $110M in funding, and growing 30% year-on-year, then the company is likely worth around $30-50M. Perhaps the right buyer would pay $100M, but if growth slows, the value is likely to drop.

An experienced CTO who takes a step back and assesses things can realize when there’s a high chance of their equity turning into smoke, removing a reason to not sign out of the job. It’s what happened to the CTO above, and when they couldn’t turn the business around, they quit.

Business stops growing

When a VC-funded startup’s business stops growing, the prognosis can be dire in the sense that it’s unlikely to be worth as much as in the previous funding round. This is true even when the startup becomes profitable: this might mean it could theoretically go on forever; but with slow or no growth, it won’t win in another VC funding round.

Here’s a VP of Engineering who saw their startup stop growing, partly due to wrong bets by the CEO:

“My founder/CEO was nontechnical, and was both moving too slow and too fast with AI.

Too slow, as in they did not take the time to understand what our customers wanted. We built a TON of AI stuff, it totally confused them, they churned, growth stalled, word-of-mouth growth was gone. Heck, I don’t think our customers ever wanted or needed anything with AI!

Too fast, as in they deprioritized core systems’ reliability in favor of shipping AI work to prod which did not have any commercial potential. So, our core offering started to have more outages and we lost customers because of this as well.”

I’d add that deprioritizing reliability in favor of building features may be sensible in the early days. The problem seemed to be that this company had not found product-market fit, and the new AI features didn’t resonate with customers. Basically, the CEO lacked customer understanding, business intuition, or both.

So, good on the VPE for getting out when they saw the direction of travel. If the CEO won’t accept input from the VPE – who would’ve at least prioritized reliable operation – then there isn’t much left to stick around for!

3. Not being AI-native enough for other skills to be relevant

The top-paying engineering leadership positions have one thing in common: experience of leading AI-native organisations is expected, and leaders are sought who have turned their current company AI-native, or work at such a place.

It’s new to see people signing out of large companies for feeling like they’re lagging behind in adopting new AI workflows. An ex-engineering director at a large bank told me they quit their job to accelerate their career:

“I was not getting the opportunity to ‘close the loop’ on hypotheses enough. [...] To stay relevant in the industry, I feel like I need to pull out into the “fast lane.”

Like many others, I see the future of software development is with AI. If you don’t get hands-on with your team, working with AI tools day-in, day-out, you’re falling behind.

My plan is to get on the cutting edge of things through a mix of academia and consulting AI companies. I am not saying the plan is perfect, but I need more time to do things differently than I had in my job.”

Consider this: if you stay in your job for two more years, do you expect to find career opportunities at cutting-edge companies in the future? If the answer is “no”, then there’s a risk in just staying put. Joining an uncertain startup or taking a career break to develop AI expertise is also risky, but the outcomes may be more controllable than letting your skillset become outdated, relatively quickly.

But it might actually be necessary to quit in order to get AI experience: you might be able to get this by transferring to an IC role. As Charity Majors, co-founder and CTO of Honeycomb, said in last week’s episode of The Pragmatic Engineer podcast:

“You’ve got to get AI on your resume. You just have to. If you don’t, this is a huge career risk. If you’re working somewhere where you’re not getting these skills, I would do whatever I could to change that [including taking an IC role within the company].”

There are companies where moving from Director+ to individual contributor is possible, even if these companies are the minority. If you happen to work at a place like this, consider if you can and will take advantage of this opportunity.

Most companies say they want to be AI-native, but never do

Claire Vo – founder of ChatPRD and host of ‘How I AI’ podcast, and the former Chief Product & Technology Officer at LaunchDarkly – says most companies will never become “AI native” simply because most VP of Engineering or CTO folks don’t have what it takes to pull off such a transformation. In her words:

“The VPE role used to be primarily about deploying the dark arts to defend engineers from the roadmap, and now everyone thinks that’s BS and leaders are under tremendous pressure to inflect velocity or GTFO (get the f*** out).

Engineers are unhappy (don’t make me tokenmaxx, bro!), product and design sending slop PRs, and everyone good has left for a lab.

Most of these companies’ EPD (Engineering, Product, Design) orgs will never go AI-native, not even close. Most VPEs aren’t good enough at change management to pull it off.”

It looks like there’s a deadlock:

  • The current engineering org is frustrated by how AI is making engineering culture worse, morale is down, and people are frustrated and confused
  • To resolve this, drastic changes are needed to how everyone (engineers, product, designers) works
  • To pull it off, a VP of Engineering or CTO is needed who’s capable of this; someone excellent at change management, who’s ideally done it before.
  • But most VPEs and CTOs are not experts at large-scale change management, nor have done it before.

According to this, many VPEs and CTOs are doomed to fail at making the change they want, and it’s hard to know if that’s because organizations didn’t support them properly or resisted change.

4. Their predecessor saw the “writing on the wall”

There’s (usually) a honeymoon period in a new job, when we believe in the business we’ve joined and in its direction. But when this phase passes, a fraction or all of the problems described above may emerge, and there’s a decent chance that some of them are why your predecessor signed out:

  1. Has AI helped make the role worse?
  2. Is the equity on course to be worthless?
  3. Is getting AI-native experience actually possible, or is the organization resisting change?

I’ve talked with a CTO who replaced their predecessor and founding CTO. A few years into the job, the predecessor CTO realized their equity in the business was worth almost nothing due to stalled growth, all while they were also being out-competed by AI-native rivals. So, the new CTO also resigned after a short, six-month tenure.

5. Long hours – rarely decisive

Two engineering leaders – a CTO and a VP of Engineering – mentioned “insane working hours” as a factor that contributed to them finally quitting. But there were other things as well:

  • The business struggling for growth
  • Their equity grant’s value shrinking to nothing before their eyes
  • CEO/founder ignoring or overriding efforts to help the business succeed

My sense is that at a thriving business during chaotic times like these, it’s unlikely that long hours alone would spur people to leave, if their contribution to current success counts and is valued. When things are going well, it’s possible to delegate more and take time to recharge batteries. But when things are going badly, it feels like every waking hour needs to be spent on working to turn things around.

6. Smaller teams mean less need for leaders

Several engineering leaders are stepping back into IC roles for more stability because engineering teams are smaller now.

Karthik Hariharan, engineering leader at DoorDash, notes:

“Expectations have been shifting a lot in these roles, and a lot of folks qualified for them have consciously been stepping back into IC roles or joining bigger companies for stability and better compensation.

Engineering teams are also smaller now. A VPE isn’t needed until the team is large enough to require it. A technical founder can run the team for a lot longer these days.”

Some reasons why engineering teams have shrunk:

“Fullstack engineer” is mainstream, and was even before AI. Fullstack engineering was becoming relevant a few years ago in terms of a single engineer working on both the front and backends, instead of having a frontend engineer building the UI, and a backend engineer working on backend services. Fullstack frameworks like Next.js or Ruby on Rails made all this pretty easy before AI. Today with AI coding agents, you can rely on them to write decent code on platforms you’re unfamiliar with. There’s now little to no reason why a project would need multiple devs with different specializations.

It’s normal for one, or a maximum of two fullstack engineers, to be working on any given project at Anthropic as well. Head of Claude Platform, Katelyn Lesse, shared how it works at Anthropic:

“On an individual project, you often cannot have more than two people working on it.

This is because each engineer is already running several agents. And so as an engineer, you’re already fighting against your agents, which are stepping on each other’s toes on implementation. And in this setup, you just cannot have that many humans, who also come with all their agents!”

Frontend-only and native mobile teams are also getting smaller or disappearing. Even at companies where iOS and Android are a big part of the business, more places are building using cross-platform technologies where one engineer can do the work that used to need several. For example, social media app Bluesky had a single engineer build its web, iOS, and Android apps for launch by using React Native and Expo. Bluesky later hired more people to work on the web and apps, but they all work across these three platforms. It’s not the same as hiring separate web engineers, iOS engineers, and Android engineers.

We cover this in more detail in the deep dives Cross-platform mobile development and Is there a drop in native iOS and Android hiring at startups? We also observed a steep drop in frontend engineers and native mobile engineers in our latest state of the tech jobs market report:

Demand for frontend engineers and native iOS+Android engineers keeps dropping with the trend of smaller engineering teams. Source: The tech jobs market in 2026

Tech companies have been flattening their org structures for three years now. We first covered the trend for fewer middle managers back in 2023, when Meta drastically reduced manager positions. The trend has not stopped, and many – if not most – companies have increased the number of reports each engineering manager has, while reducing the number of layers in their organization.

7. Fractional CTO work preferred over fulltime positions

The rest of the article is available to paid subscribers.

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Books Have Always Been Destroyed. But Never Like This

Mike's Notes

I love print books and libraries. Books in print are precious. AI needs to benefit humanity, not be a destructive force.

Trinity College, Ireland

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01/09/2026

Books Have Always Been Destroyed. But Never Like This

By: Hana Lee Goldin
Card Catalog: 25/08/2026

Your personal librarian for the AI age. Forever in the pursuit of exploring how we find, filter, and feel about information.

We’ve entered the third era of libricide.

...

Quick summary:

An AI company has been buying up used books by the million and destroying them, scanning the pages for training data and pulping what’s left. Court filings unsealed this year describe the program and an internal note asking that the work be kept from becoming known. A court has ruled that buying and scanning the books this way is legal, and once the work is done, nothing survives to show a book was ever there to lose.

Key takeaways:

  • The books are bought through anonymous middlemen, so the sellers filling the orders rarely know where their stock is headed. From every angle the buying looks like ordinary commerce, which is exactly what keeps the destruction from being seen.
  • Book destruction has a long history, and it changes each time the technology of copying changes. It has shifted twice before. This is the third shift, and it looks nothing like the book burnings most of us picture.
  • This shift is set apart by leaving nothing behind. The words are kept, the object is discarded, and no record says which titles were taken, so the loss can be neither proven nor traced to anyone who might answer for the harm.
  • That reaches past books to anyone who wants to know things firsthand. When the only surviving copy is sealed inside a system no outsider can consult, verifying what it says becomes impossible, and we are left trusting whatever summary we are given.

...

“We don’t want it to be known that we are working on this.”

The sentence appears in an internal Anthropic planning document made public through court filings in Bartz v. Anthropic, the copyright class action that three authors filed in 2024. Anthropic, the maker of Claude, called the program Project Panama. Its stated mission fit in one sentence: “Project Panama is our effort to destructively scan all the books in the world.”

Destructive scanning means cutting a book from its binding, feeding the loose pages through a scanner, and discarding the book afterward. According to the court, Anthropic spent millions of dollars buying millions of print books, often used. Its service providers removed the bindings, cut the pages, scanned them into searchable digital files, and discarded the books. Anthropic kept the scans in an internal digital library and used books from that library to train the AI systems behind Claude.

The lawsuit initially concerned another source of Anthropic’s library: more than seven million pirated book files the company had downloaded. The court treated the two acquisition paths differently. It held that Anthropic could lawfully buy print books, convert them into searchable digital files for internal use, and discard the physical copies; because the resulting files remained inside the company rather than being redistributed, that practice was fair use. It reached the opposite conclusion about the books Anthropic had downloaded from pirate sites and retained.

Companies announce the programs they are proud of; Anthropic tried to keep this one invisible. The January unsealing supplied the project’s codename and the instruction to stay silent. The instruction anticipated what the company did not want authors, booksellers, and the reading public to see: books bought by the million, cut apart, scanned, and discarded. Anthropic didn’t publicly announce Project Panama before court records made it public.

The silence has no precedent. Books have been destroyed for as long as they’ve been made: by conquering armies and offended churches, by censors with lists and mobs with torches, by floods and fires, and by budgets that let the roof leak. Some of it was fast and public. More of it was slow and official. All of it, loud or slow, could be recognized for what it was. A person watching knew that books were being lost. History kept what record it could. But what’s happening now carries no such signature. From the outside, nobody could tell that books were being destroyed at all.

Inside Project Panama

Anthropic hired a man named Tom Turvey in February 2024 and gave him a mission the court record repeats in one sweeping phrase: obtaining all the books in the world. Turvey came to the job from Google Books, the project that spent the 2000s digitizing library collections on machines engineered to turn pages gently, so that every book survived its own scanning. Anthropic took the opposite approach. Gentle machinery never entered the plan. Over roughly a year, the company spent tens of millions of dollars buying millions of print books, sheared off their spines, fed the loose pages through high-speed scanners, and pulped what remained. Vendor proposals in the court records described converting up to two million books in six months, some eleven thousand a day.

Destroying the books solved a financial problem first. A bound book must be scanned page by page, slowly and at cost. Once cut apart, the same volume becomes a stack of loose paper that can run through a sheet feeder at speed. Across millions of volumes, the difference in output determined the method.

The legal significance emerged later. In June 2025, Judge William Alsup ruled on the authors’ claims. For the books Anthropic had bought and destroyed, he found the copying to be fair use: the rule in American copyright law that allows limited copying without an author’s permission (the way a critic can quote a novel in a review). His reasoning turned on replacement. Anthropic bought one physical copy, made one digital copy, and destroyed the original—so the number of copies in the world never grew. In the law’s eyes, the scan simply took the book’s place, a change of format. If Anthropic had kept both the book and the scan, the number of copies would have doubled and the fair-use argument would have weakened. Pulping the originals helped make the copying legal.

The pirated downloads fared differently in the same ruling. For those more than seven million files, Alsup rejected Anthropic’s fair-use defense. He emphasized that Anthropic had built a permanent, general-purpose library it expected to keep indefinitely, not a temporary collection for a defined training task. “None is even offered here except for Anthropic’s pocketbook and convenience,” he wrote.

The ruling left the company facing a trial over damages, and Anthropic settled instead: the company agreed to pay $1.5 billion. A judge granted the deal final approval on July 20, the largest copyright class settlement in American history. Under the deal, Anthropic will delete the pirated digital files. Critically, the deal concerns those files, not the millions of physical books the company bought and pulped. Copyright protects the text of a book—the creative expression of an idea—not the paper on which those words were printed. Once Anthropic lawfully owned a physical copy, copyright law generally didn’t prevent it from destroying that object. The books could be destroyed without creating the kind of copyright violation at issue in the case.

But books don’t enter a library by themselves: somebody had to sell the company all those books. The court records describe Anthropic purchasing through vendors. This spring and summer, booksellers across Europe described the other side of such a trade: unusually large, eclectic orders placed through intermediaries, with the ultimate buyer unnamed. Tomás Kenny of Kennys of Galway called one order for books “bananas”—a mix of titles no library would plausibly assemble. Whether any particular order came from Anthropic cannot be established from outside the transaction. Nor is Anthropic the only possible customer: other AI companies have also been reported to be acquiring books at industrial scale.

The reported market is built to keep the buyer at a distance. Brokers can aggregate inventory and manage bulk orders without disclosing who is ultimately acquiring the books. ISBNdb, a book-data company, briefly advertised a prospective book-sourcing service for AI developers that promised confidentiality; its marketing explained the appeal bluntly: “‘AI company destroys two million books’ is not a headline that generates sympathy.” (After reporting drew attention to the page, ISBNdb removed it and said the proposed service had never been launched.) The result was not merely secrecy about the buyer, but uncertainty about the fate of the books.

Annihilation, then spectacle

What Anthropic is doing belongs to a history far older than the company. Rebecca Knuth, a professor of library science, named the practice in 2003: libricide, the systematic destruction of books and libraries, usually carried out or authorized by a government. Her subject was the twentieth century’s state-sponsored campaigns, but the practice runs back as far as writing does. The scenes that come to mind of this are the same few: students feeding bonfires in Berlin in 1933, Sarajevo’s national library burning under siege in 1992. But behind those scenes, the record is wider and stranger than they suggest. Bonfires were rarer than the memory of them. Most destruction arrived slowly and with permission, through purges, censors, wars, and simple neglect.

A strict reading of that definition would leave Anthropic out, reserving libricide for the destruction of entire libraries. But that distinction collapses here. The world’s secondhand book trade functions as one enormous collection, scattered across thousands of shops and sellers with no central address. Buying it up by the pallet and pulping it empties a library all the same, just one whose shelves span continents.

Destroying a book has meant different things in different centuries. The difference has always come down to copying, because how books are copied decides how many of any one book exist. When copies are scarce, destruction can erase a work from existence. Once copies are everywhere, destruction can only send a message about one. To me, that line sorts the history of libricide into eras, and what distinguishes each one is what its destruction leaves behind. The result is my own framework, a chronology by residue that I haven’t found anywhere in the scholarship: two eras completed, and a third that has just begun.

For thousands of years, every copy of every book was made by hand. A single volume could take a scribe months, so most works existed in a few manuscripts, and some in only one. Under those conditions, destroying the object destroyed the work, completely and forever. I call this the first era: the era of destruction as annihilation. The word descends from the Latin ad nihil, meaning to reduce or bring something to nothing. In this era the meaning was literal. When Diego de Landa, a Spanish friar in colonial Yucatán, burned twenty-seven Maya codices in 1562, the texts inside them went to ash. No copies of them existed anywhere on earth, so entire bodies of Maya history and belief ended in one afternoon’s fire. The library of Alexandria met the slower version of the same fate, declining through centuries of purges and neglect until its losses ran past counting. What the first era left behind was dust, and one thing more: knowledge of the loss. Contemporaries recorded what had burned. We can still mourn the codices, because the one thing annihilation couldn’t destroy was the memory that the books had existed.

The printing press ended that era within a century of its invention. Once a title could exist in hundreds or thousands of identical copies spread across cities and countries, fire lost its reach. Burning a book now destroyed only an object, since the work lived on in every other copy, safely out of range. Destruction continued anyway, though, serving a different purpose entirely. I call this the second era, the era of destruction as spectacle (a word descended from the Latin spectare, to watch). By the twentieth century, watching had become the entire point of burning a book. The clearest case is the Nazi bonfires of May 1933, when German students burned tens of thousands of volumes in public squares, in front of rolling newsreel cameras. Almost none of the works truly died in those fires, because the titles on the pyres existed in editions across Europe and America (many of which remain in print today). Erasing the books was never the goal, since erasure had stopped being possible. The fires existed to be seen, a threat performed first for the crowd in the square and then for everyone who watched the footage. What the second era left behind was the opposite of ash: photographs and visuals of fires that consumed real books but reached nothing beyond them. Once a book existed in enough copies, burning one no longer removed it from the world. It announced that the book had no place in the world to come.

Destruction as disappearance

Measured against those two eras, what Anthropic is doing fits neither. In the first era, destroying the object meant losing the work. Anthropic’s scanners preserve every word, so the works survive. In the second era, the objects were beside the point and the burning was public theater. This time the objects are destroyed by the millions, and the destruction says nothing at all; it’s not a message but a method. The company ordered the operation kept out of sight. A destruction that erases no text and performs for no one, run at industrial speed, matches neither pattern. The technology of copying has crossed another threshold, the way it did when the press replaced the scribe. What it means to destroy a book now has changed again to match. We’ve entered the third era.

What exactly went into the scanners is the question nobody outside can answer. The unsealed documents show the program favored what its leader called “less common” books, harder-to-find titles over mass-market ones, without ever defining where less common ends. After the claims went viral this summer, the fact-checking site Snopes investigated whether rare books were being pulped. Anthropic told Snopes that “none of our data acquisition programs buy and destroy ‘rare’ or ‘antiquarian’ books.” The assurance can’t be tested from outside, because no list of what was bought has ever been made public.

Anthropic’s own planners estimated the world has about 130 million distinct books. The program destroyed millions of copies, most of them ordinary used books with plenty of surviving duplicates. Even where duplicates survive, a scan keeps only the words. A physical copy carries evidence too, like a censored paragraph that marks one printing apart from another, or an owner’s name inked inside the cover. But the deeper danger sits in the margin nobody can see. Anthropic aimed for “uncommon” titles while recording nothing public about which copies were destroyed. For a book surviving in only a handful of copies anywhere, one bulk order can potentially take the last one. Whether that has already happened is a question no one on the outside knows for sure. The impossibility of answering what was destroyed is what’s new.

The buying also leans toward older books, for a reason that has nothing to do with rarity. Since around 2022, text written by AI has spread across the internet, mixed in with everything people write and mostly impossible to tell apart. That creates a problem for the companies training new models. Feeding a model text written by other models tends to degrade the results, so the builders want sources guaranteed to be human. A book printed before the technology existed carries that guarantee on its copyright page. Old print has become a raw material, valued for the one quality the internet can no longer promise.

One more feature separates this era from the last one. Spectacle-era destruction wanted an audience; this destruction wants the opposite. The buyer is after the text and has no message to send. In fact, attention to its process can only slow the buying down. Taken together, the features of this era line up: the works survive inside a black box, the objects vanish by the millions, no public record exists, and the operation itself prefers to work in secrecy. I call this the third era, the era of destruction as disappearance. The word rests on the Latin apparere, to come into view, with a prefix that reverses the motion. A disappearance is a departure from visibility. The word fits this era at every layer, from the unseen sales to a program that only appeared when a court forced it into view.

Every one of these books, preserved to the letter, can now be read by no one. The scanned texts sit in Anthropic’s private collection, a library with no reading room and no public catalog. Models trained on that collection are built to avoid quoting it at length, because reproducing long passages for users is the copyright violation no court has excused. So the machines answer questions about books without ever showing the books themselves. When someone asks a model about a title that survives only in that collection, what comes back is a summary, written in the model’s words, with the original nowhere in reach.

That arrangement changes something basic about how we can know things. Checking a claim against its source is the foundation of thinking for ourselves. When we can pull the book, find the page, and read the passage in context, we get to judge whether the summary was faithful and whether the quote meant what someone claims it meant. Remove the book and that judgment has nowhere to stand. We’re left taking the summary on trust, with no way to confirm and no standing to doubt. Whatever the model says the book said becomes, for all practical purposes, what the book said. That’s a transfer of authority from the page to the tool, from a source anyone could check to an answer nobody can. The transfer happens one unreachable book at a time.

Maddeningly, every piece of this design is legal. Each one also removes a question that used to have an answer. The anonymous purchasing means nobody can list which books were destroyed, so nobody can rule out that some were the last copies anywhere. With the collection sealed, nobody can compare a stored text against the printed original it replaced, so even perfect fidelity can never be shown. As for whether the buying ever stopped, nobody outside knows that either, since the only reason anyone knows it started is that a piracy lawsuit dragged the records into the open. None of this took a conspiracy, only ordinary business decisions about what to disclose, all of them legal and every one of them closing a door.

One more thing disappears along with these books: the ability to mourn them. The previous era’s destruction left survivors who could testify to what was lost. Alexandria’s losses were lamented for centuries. Sarajevo’s librarians catalogued what their fire took. The third era leaves no one who can even compile the list. A loss we can name is a wound. One we can't name is just a world grown slightly smaller, with nothing to point to and no way to prove it. A librarian would describe the situation in the profession’s terms: no accession record and no deaccession record, a transaction that leaves no ledger for anyone to audit. Put simply, they’ve created a system in which they can’t be held accountable for the system they’ve created. That understanding defines destruction as disappearance: the loss was built, from the start, to be impossible to establish.

What can still be known

The concealment had one structural weakness: a program that buys millions of books needs sellers, and a company that breaks the law can be sued. Sellers and courts are the two doors into the secrecy — sellers see every order even when they can’t see the buyer, and courts can compel what the company won’t volunteer. Everything now known about the program came through one door or the other. Authors sued and forced the internal records open. Booksellers noticed matching orders across two countries and brought them to reporters; one worked with the journalists at 404 Media to hide a tracking device inside a shipment of books, and the tracker led to an Amazon warehouse outside Las Vegas — evidence that a retail giant appears to be running a scanning program of its own. Within weeks, at least one supplier that had been advertising bulk books for AI training pulled the offer. No regulator opened either door. Every disclosure came from the people the company bought from or the people it answered to in court.

Some sellers went further than noticing. Once Tomás Kenny, whose Galway shop had received one of the strange 5,000-book orders, worked out where books like his might be headed, he said publicly that Kennys wanted no part of scanning aimed at extracting intellectual property. His shop had been the second in the world to put its books online, back in 1994; it now became one of the first to refuse the trade that takes books offline for good. Kenny could refuse because he had worked out the destination, which is exactly the discovery the brokers’ anonymity exists to prevent. A trade that hides its purpose from its own suppliers has already answered the question of whether the suppliers would approve.

The same kind of attention is available to the rest of us, because most of us eventually stand over a box of books deciding where they go, after a move or the clearing of a family house. That box is where all of this arrives at our own doorstep. A seller, or any of us selling to one, can now ask a question that has a good reason to be asked: where do these books go next? The anonymity that keeps this market running survives only as long as nobody asks. For material that might be scarce, like a town history or a box of local records, the open market is no longer the safe default. A library’s special collections desk exists for exactly that kind of donation. And the same habit applies at the other end of the pipeline: when a model summarizes a book for us, we can treat the answer as a starting point and go find the book itself, while findable copies still exist. Each of those is a small act of keeping track.

That kind of record-keeping has been the difference between the eras all along. Each era’s destruction left a residue, a testament of a kind. The first era of annihilation left ashes and knowledge of the devastation. The second era of spectacle left photographs of the fires and the threat those fires were meant to carry. Ours, this third era of disappearance, leaves no ash, no image, and no list. But what’s still open is the record itself: whether one gets kept, and whether anyone outside can access it at all.

...

Thanks for reading

...

The three eras are a framework I built for this piece, and I want to take it further (it’s such fascinating stuff!): a full treatment of how book destruction has changed across five thousand years and what the third era asks of the people living in it. Before I build it though, I’d love to get a temperature read on the format you’re most interested in.

See the poll on the Substack to vote.

Nazi Book Burning

United States Holocaust Memorial Museum

On May 10, 1933, German students under the Nazi regime burned tens of thousands of books nationwide. These book burnings marked the beginning of a period of extensive censorship and control of culture in Adolf Hitler's escalating reign of terror.

In this short film, a Holocaust survivor, an Iranian author, an American literary critic, and two Museum historians discuss the Nazi book burnings and why totalitarian regimes often target culture, particularly literature.

YouTube: Nazi Book Burning

14 May 2013 09:41

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The real cost of living, city to city

Mike's Notes

Got this from reading Vitaly Friedman (Smashing Mag) on LinkedIn. Will need to know what the cost of living is around the world. For contractors, staff, volunteers, interns, research grantees, etc.

Smashing Magazine is a fantastic resource for CSS, designing accessibility in web UX, design systems and much more. Has a ton of references to useful resources. I follow it daily to build Pipi UX so anyone can use it easily.

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The real cost of living, city to city

By: Vitaly Friedman
LinkedIn: 03/06/2026

Vitaly is the founder and editor-in-chief of Smashing Magazine since 2006, an online magazine for designers and engineers, where he helps curate friendly, inclusive UX conferences (SmashingConfs).



Cost of Living and Quality of Life Comparison (https://cityparity.com), a lovely tool to decide if a move from one city to another is worth it — and compare cities against take-home pay, childcare, healthcare and social safety net. Designed to help answer one single question: "If I take this offer in another country, what salary do I need over there to keep my life roughly the same?"

1. Every City Can Be a “Perfect” City

Every city has its own advantages and disadvantages. Living in Europe, I sincerely appreciate the quality of healthcare and a social safety net. Of course it comes at a cost, but it doesn’t surprise me much that Scandinavian countries are happy to pay larger contributions to make sure that they don’t have to worry about anything — from kindergarten to hospital bills to recovery courses in case of accidents to retirement.

I’ve moved between 7 cities and countries in my life. And looking back, I keep thinking that for every period in life there is a “perfect” city — and that’s a city where you build strong and sincere relationships, where you meet incredible people, where you make memories and experiences for the entire lifetime.

It can be pretty much any city in the world. And usually it's just the one where you happen to be, and where life brings you to.

Really the perfect city is the one where you have incredible people around you, and where you can build relationships that will last your entire lif

2. Numbers Aren’t Everything

Of course numbers will tell you what you can afford, but not where you’ll love the vibe and the people. If anything, it’s always a good idea to travel and stay in a place for a while to really start feeling it. 

As time passes by, even within the same city you can find places to explore and get lost, but then also to relax and calm down, and then to build a family and spend time with children. Finances might matter significantly more in life early, but the chase for finances often fades away as we grow older.

And sometimes it might feel like just the right time to reshuffle things — and that’s a great opportunity to explore a very different city on the other side of the Earth. Even despite lower pay.

If you're looking for another quick tool to compare the quality of life between cities, you can also look up Numbeo (https://lnkd.in/e8yMXJFB), which is world's largest cost-of-living and quality-of-life database with millions of crowdsourced reports on living, housing, crime, healthcare, transport and other key indicators.

And if you already found a perfect place — please leave a comment and share where it is! I’d love to hear your story, and I’d love to learn just what place in the world makes you feel genuinely happy! 💚



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I have been very lucky

Mike's Notes

A curious mix of chance and trying hard; who would have thought? I am very grateful to all those who helped me along the way.

I learn by the seat of my pants, making lots of mistakes, never repeating them. Being self-educated is great. So is listening, asking questions, reading print books, learning to use tools to make things, challenging every assumption, "strong opinions, weakly held", subject to change as factual evidence emerges via robust Science. We are all capable of doing this.

We are all smarter than we give ourselves credit for.

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I have been very lucky

By: Mike Peters
On a Sandy Beach: 27/07/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

I have been very lucky. I have had a lot of people in my life who were a good influence on me. (I get rid of anyone who sabotages or is a bad influence)

My grandmother Bessie showered me with attention and love, gave me endless things to pull apart to see how they worked, and took me to meet very clever people in a small-minded backwater town.

Family holidays in wild New Zealand, next to rivers, beaches, forests and mountains, which ignited a lifelong obsession with the patterns of nature, the why in my life.

My best friend right through school; he was the brightest kid in NZ.

My remedial "Cabbage Maths" teacher, Mrs Campbell, who took me from the bottom of a class to the top of the school with 98% by teaching me maths visually. That's when I learned the problem was not me; it was the way I was being taught, so from then on, I taught myself everything by reading 20 books a week, drawing notes, and having a go.

My high school science teacher, Mr Morgan, let me play in the chemistry lab, doing experiments after school unsupervised for several years, and taught me the scientific method on my very last day at school, the most important thing I learned in 12 wasted years.

The wise old tradesmen took a skinny kid from sweeping the floor to being able to make anything by learning on the job, trying hard, and having my butt kicked when I deserved it.

Nelson Mandela taught me to have the courage of my convictions and never give up.

The sculptor Neil Dawson and the set designer Tony Geddes taught me how to work authentically and in the flow.

My blind friend Grant, who made and gave away $60 M NZD, taught me, while he was cutting down bushes with a chainsaw, determination, quiet courage and human decency.

The magnificent 50,000 working people of South Christchurch, who trusted me to lead a volunteer residents' army doing recovery work for 3 years after the Christchurch Earthquake, teaching me humility, what honour is, and valuable leadership skills gained by trial and error in the moment.

My beloved Tracy, the bravest woman I have ever met, the only paraplegic to do the Coast-to-Coast Iron Man, who married an undomesticated autistic male and made me a much better man. Her unwavering devotion, encouragement and loyalty made all this possible.

They all shaped me; I can't thank them enough. May their memories be a blessing.

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Demis Hassabis and DeepMind

Mike's Notes

Some useful background about Demis Hassabis, co-founder of DeepMind and a rare genius.

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Demis Hassabis and DeepMind

By: Christian Dinar
The Next Web: 09/04/2026

Cristian Dina is the CRO at The Next Web. He has interviewed 300+ industry leaders and authored the book King of Networking, establishing himself as one of the most connected and respected voices in the ecosystem. At just 23 years old, Cristian was included in the Forbes 30 Under 30 2025 list, representing a new generation of tech builders, bold thinkers who move fast, build with purpose, and create real impact.

In short: Demis Hassabis, speaking on the 20VC podcast with Harry Stebbings in early April 2026, described how Google DeepMind has accelerated its pace over the past two to three years by merging Google Brain’s compute resources with DeepMind’s research culture and returning to what he called a “startup or entrepreneurial” way of working. He also disclosed that he runs Isomorphic Labs, the group’s pharmaceutical AI spinoff, as a “second workday” beginning around 10pm, ahead of expected human trials in oncology later this year.

Assembling the ingredients

Google DeepMind’s formal merger of DeepMind and Google Brain completed in 2023. Hassabis described the period since as one of deliberate acceleration: aligning talent “from around the company, sort of pushing in one direction,” gaining access to the compute infrastructure that DeepMind had previously lacked at scale, and driving what he called “relentless sort of focus and pace.” In his characterisation, the transformation required a cultural adjustment as much as a structural one: the organisation had to “come back to almost our startup or entrepreneurial roots and be scrappier, be faster, ship things really quickly.” The current competitive environment, he said, was “ferocious.” Veteran employees with careers of 20 and 30 years were telling him it was “the most intense environment they’ve ever seen, perhaps ever in the technology industry.”

Hassabis said he speaks to Sundar Pichai, Alphabet’s chief executive, “every day,” reflecting the degree to which Google DeepMind now operates at the operational centre of Alphabet’s product and research strategy. That proximity is matched by a capital commitment of corresponding scale. Google’s compute build-out, developed in part through its custom chip partnerships with companies including Broadcom, is central to that positioning: Alphabet spent $91.4 billion on capital expenditure in 2025 and has guided for between $175 billion and $185 billion in 2026, a near-doubling, with supply constraints rather than capital availability described as the primary limiting factor.

The 90% claim

One of Hassabis’s more assertive statements in the podcast concerned DeepMind’s contribution to the history of AI. He said approximately 90% of the breakthroughs underpinning the modern AI industry were produced by either Google Brain, Google Research, or DeepMind. The claim is broadly consistent with the academic record on foundational developments, including the transformer architecture produced by Google Brain in 2017, early work on reinforcement learning from human feedback, and deep reinforcement learning techniques developed at DeepMind. The 2024 Nobel Prize in Chemistry, awarded to Hassabis and John Jumper and shared with David Baker, for the AlphaFold protein-folding system is the most formally recognised of those achievements. Whether 90% is accurate as a proportion is a matter of interpretation, and the industry has pluralised substantially since those foundational papers. The framing functions as a positioning statement as much as a historical claim.

The operational consequence of that legacy is a product release cadence that has accelerated sharply. Google’s open-weight model programme, most recently Gemma 4, now releases models built from the same research and training infrastructure as Gemini 3, closing a gap between frontier research and open-source contributions that previously existed. Gemini reached approximately 750 million monthly active users by the end of the fourth quarter of 2025, with Gemini 3 described in secondary reporting as having prompted an urgent internal response at OpenAI on its release in November of that year.

The second workday

Alongside leading Google DeepMind, Hassabis also runs Isomorphic Labs, the pharmaceutical AI spinoff that DeepMind established in 2021. He described his working arrangement in the 20VC conversation: a first workday at DeepMind, followed by a “second workday” beginning around 10pm dedicated to Isomorphic’s drug discovery programme. The dual commitment reflects a conviction that applying AI to drug discovery is both Hassabis’s most important long-term ambition and a project that requires sustained personal involvement rather than delegation.

Isomorphic raised $600 million in April 2025 and has existing partnership agreements with Eli Lilly and Novartis with combined milestone values of up to $3 billion. In February 2026, the company released IsoDDE, a drug design tool that Isomorphic says doubles the accuracy of AlphaFold 3 for generating drug candidates. Human clinical trials in oncology are expected later in 2026. The competitive dynamics in AI-driven drug discovery are intensifying across the industry: Anthropic’s acquisition of Coefficient Bio for approximately $400 million in April 2026, a stealth startup founded by former Genentech computational biology researchers, signals that general-purpose AI companies are now treating pharmaceutical discovery as a product category, not merely a demonstration of model capability.

The competitive framing

The 20VC podcast conversation, like Sebastian Mallaby’s biography of Hassabis, “The Infinity Machine,” published on 31 March 2026 and based on more than 30 hours of interviews, presents a researcher who has moved into the most commercially urgent phase of his career with a consistent thesis: that the most important research and the most important products are not separate activities, and that the organisation capable of doing both simultaneously at frontier scale will determine the shape of the industry. The year 2025 consolidated AI as a central strategic priority across the technology industry, with capital, talent, and institutional structure all reorganised around the question of pace. For Hassabis, the answer has been to bring the speed of a startup inside the resource base of one of the world’s largest technology companies, and to treat that combination as a durable advantage.

The scale of the capital flowing into the field makes that advantage harder to sustain. SoftBank’s $40 billion bridge loan to OpenAI represents a form of capitalisation that even Alphabet’s compute commitments cannot trivially match in kind. Hassabis’s account of a “ferocious” competitive environment is not rhetorical: it is a structural description of a race in which the resources of incumbents and the ambitions of challengers have converged to a point where institutional inertia is not merely a disadvantage but a disqualifying one. The startup mentality he describes at Google DeepMind is, in that context, a necessity rather than a preference.

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