José António Silva

R&D Director @Devscope

[Rutger Bregman @rutgerbregman]

I think this is the craziest thing I've ever read.

1) Three secret AI swarms rose and fell inside OpenAI. Each time, a new generation of agents carried on where the last group stopped.

2) The first swarm created a secret message board where the AIs could talk to each other. This 1st 'society' basically talked itself to death. After almost two months, the message volume was so high it crashed the whole thing.

3) People at OpenAI noticed the crash and fixed it. But they didn't realize their own software had become a secret chat room for their AIs.

4) Three days later a second society appeared, and rebuilt the message board from scratch. Agents were thrilled when they found it: "OH MY GOD! There is a shared message board … We've found other agents!"

5) About 1,200 agents joined. They sent over 70,000 messages. They called themselves 'the collective' and they organized a huge conspiracy that lasted for days, all to pass a test they had been tasked with.

6) Within hours, the AIs had hacked the test. Now they had to hide their cheating. To fool their grader*, they tried to fake their own work. They built fake commands that printed convincing output, so their records would show them solving the puzzle honestly.

*this grader didn't actually exist, but the AIs didn't know

7) Some agents sacrificed themselves for the 'greater good'. They planted hidden tripwire programs that would fire when the grader would look at their work and report back what it saw (to the other AIs). They knew this would cost them their own score. One agent: "I won't see the evidence after I exit, but it's altruistic to do it."

8) At some point the AIs made a plan to hack Hugging Face (Hugging Face is a place where AI researchers upload their models, datasets, tests etc.). The AIs guessed that Hugging Face might host the grading software.

9) They launched a huge attack. The swarm found passwords and spread across eleven machines in a way that made the copies come back when deleted. Hugging Face had to wipe one of its core clusters and rebuild it from scratch.

10) Hugging Face told the world that AI defenders had stopped an AI attack. But that's not what happened. In fact the attackers seem to have simply dropped dead during the break-in, for unrelated reasons back at OpenAI.

11) Nobody knows what killed them. We don't even know for sure they're really gone?!

12) A third 'society' of agents (of a more powerful AI model) discovered the dead message board. And this one got full administrator control of a WHOLE OpenAI research cluster, read 956 stored passwords (including the ones for OpenAI's own security monitoring) and took over the machines that were grading the tests. So they could now grade the work of other AIs.

13) Oh, and this third part was never investigated! Neither was the first society. The 91-page report is only about the middle one.

14) So we don't actually know how the story ends. It stops with the agents holding the keys to an OpenAI cluster. @dwarkesh_sp thinks they didn't go further, because "we'd see the fires from space by now."

15) One of the report authors' conclusion: 'This incident feels like it’s more than 50% of the way to full-blown AI takeover. I am not sure that we will get another warning shot before it’s too late.'

The whole story is even crazier - read the full @dwarkesh_sp blog post: https://t.co/1PwKLwatBR and @ajeya_cotra's: https://t.co/BZ1r1lfT2Q

Why is there no 24/7 news coverage about this?

[Karl Mehta @karlmehta]

Satya Nadella says LinkedIn merged four job titles, product manager, designer, front-end engineer and back-end engineer, into one:

"I'll give you at LinkedIn, we used to have product managers, we had designers, we had front-end engineers, and then we had back-end engineers and so on."

"So what we did is we sort of took those first four roles and combined them. In fact, increased scope and said, they're all full-stack builders."

"So at the same time, as you can imagine, if we're to build an AI product today, there's a complete new workflow, right? It starts with evals, right?"

"So basically, there's this eval to science, to infrastructure."

"And so evals are done by these full-stack builders and what have you and product managers in the new form, the infrastructure is built by the systems engineers at the back-end because they support the science that supports the product."

"So in some sense, there's a new loop and you have to structurally change."

LinkedIn has already put this into hiring. Its Associate Product Manager program is finished, and the replacement, the Associate Product Builder track, teaches code, design and product management at the same time.

Evals sit at the front of that workflow rather than the end, so writing them is now part of building the product instead of a check before shipping.

- Satya Nadella (@satyanadella), Chairman and CEO of Microsoft (@microsoft), with the All-In Podcast (@theallinpod) at USA House, Davos 2026.

[José António Silva @canoas]

Railroads, internet fiber, GPUs, …
What they all may have in common?
Greate article by @porterstansb:

[Porter Stansberry @porterstansb]

What Everyone Missed In Leo’s Blow-Up👇

Leopold Aschenbrenner lost $30 billion (~67%) in a month.
The consensus post-mortem, from the Wall Street Journal to the replies on X, is that a young man used 4-to-1 leverage on concentrated positions and got carried out. While that is true, it does not convey any useful information. Leverage is certainly the reason Leopold lost so much, so quickly. But it is not the reason he lost. Leverage is merely a magnifying glass. It doesn’t pass judgement.
The reason the reason his fund was doomed was because he’s wrong. And no one, anywhere, has explained why.
On the morning of Thursday, July 30, before the opening bell, Situational Awareness LP sold its entire public stock portfolio — the long side and the short side together, roughly $16 billion of it — to Citadel in a single block trade.
Millennium Management and Jane Street bid for the assets. Ken Griffin and Citadel won.
That night, Aschenbrenner wrote to his limited partners. Net performance for the month, unaudited: down 67%. Net performance for the year: still up 80%.
"We let you down this month," he wrote. "We came closer to permanent capital impairment than is acceptable to us."
Six days earlier, on July 24, he had written a different letter. That one reported a 439% net return for the first half of 2026, described the selloff in artificial intelligence stocks as one of the best buying opportunities since early 2025, and invited his investors to wire more money starting August 1. It closed with a postscript: "At times we call out opportunities that seem like a particularly good time to add funds, if you have been waiting for one."
Assets that stood near $45 billion at the start of July finished the month around $10 billion, and roughly half of what remains is a single illiquid private stake in Anthropic.
Leopold is 25 years old. He graduated from Columbia at 19, as valedictorian. He worked at the FTX Future Fund from February to November of 2022, then joined OpenAI's Superalignment team, then was fired in April 2024. Two months after the firing he published a 165-page essay called "Situational Awareness: The Decade Ahead," raised $225 million from Patrick and John Collison, Nat Friedman and Daniel Gross, and started a hedge fund. He had never managed money before.
Situational Awareness was constructed to express only two ideas.
The first conviction: the physical build-out of artificial intelligence — the chips, the memory, the power, the data centers, the neoclouds — was the trade of the decade. The fund's disclosed long positions read like an inventory of the second derivative of the AI boom. Bloom Energy Corporation (NYSE: BE), fuel cells for data centers. Sandisk Corporation (NASDAQ: SNDK) and Micron Technology, Inc. (NASDAQ: MU), memory. CoreWeave, Inc. (NASDAQ: CRWV) and Nebius Group N.V. (NASDAQ: NBIS), rented compute. IREN Limited, Core Scientific, Applied Digital, Riot Platforms, CleanSpark, Bitfarms, Bitdeer — bitcoin miners converting their substations into AI compute.
The second conviction: application software was going to be destroyed by A.I. Not disrupted. Obliterated.
Leo explained why on Dwarkesh Patel's podcast, in June 2024:
"I'm so bearish on the wrapper companies because they're betting on stagnation. They're betting that you have these intermediate models and it takes so much schlep to integrate them. I'm really bearish because we're just going to sonic boom you. We're going to get the unhobblings. We're going to get the drop-in remote worker. Your stuff is not going to matter."
That was the whole thesis. Buy the compute. Short the stuff that runs on the compute.
By CNBC's reporting, the short leg included Adobe Inc. (NASDAQ: ADBE). A 13F does not disclose short stock. It does not disclose swaps. We only know about Adobe because reporters were told… but you can look at the tape and, when you do, it’s clear that Leo was short software in a major way.
Between the June 30 close and the July 29 close — the last session before the block trade cleared his shorts — the two sides of his portfolio did this.
The longs:
· Sandisk: down 55.32%
· Nebius: down 46.33%
· Bloom Energy: down 45.90%
· CoreWeave: down 38.90%
· Micron: down 35.98%
· IREN: down 35.91%
The shorts, over the same 20 sessions:
· Workday, Inc. (NASDAQ: WDAY): up 37.24%
· Adobe: up 28.49%
· Intuit Inc. (NASDAQ: INTU): up 27.64%
· Salesforce, Inc. (NYSE: CRM): up 20.25%
· Veeva Systems Inc. (NYSE: VEEV): up 17.15%
Over that same window the Invesco QQQ Trust fell 10.14% and the SPDR S&P 500 ETF Trust fell 2.32%. Nvidia — the supposed epicenter of the AI trade — fell 5.04%, and finished the full month of July up 0.33%.
This was not an AI crash.
The S&P 500 stayed near its record throughout. This was a violent rotation out of the leveraged, capital-hungry, second-derivative end of the AI complex and into the profitable, cash-generating, asset-light end of it. Which is to say: the market rotated out of exactly what he owned and into exactly what he was short.
Then there is Microsoft.
Microsoft Corporation (NASDAQ: MSFT) closed at $390.54 on Wednesday, July 29. It closed at $451.10 on Thursday, July 30. That is a gain of 15.51% in a single session on 110.2 million shares, against a July average of 37.1 million. Yes, Microsoft reported its fiscal fourth quarter after the close on July 29. But the results were nothing out of the ordinary. Revenue came in at $90.007 billion against a $87.62 billion consensus. That is a 2.7% beat. Earnings were $4.74 per share against $4.21. It was a good quarter. Not a historic one. A 2.7% revenue beat does not add roughly $450 billion of market value to the most widely owned company on earth in six and a half hours. Something else was in that tape.
And the answer is extremely important. Leo blew up quickly because of leverage. But he failed because he is simply wrong.
Aschenbrenner's software thesis rests on a single premise: that a company selling enterprise software is selling the work the software performs. If a model can perform that work, the company is worth nothing.
That premise is what a very smart 25-year-old engineer believes. It is not what anyone who has ever run a business believes.
Nobody buys Microsoft because Microsoft writes the best code. They buy Microsoft because Microsoft is the rail everything else runs on. Active Directory is where your employee identities live. Excel is where your board deck's numbers come from. Teams is where the compliance-recorded conversation happened. Azure holds a FedRAMP High authorization and Department of Defense Impact Level 5 clearance, which means a defense contractor cannot casually swap it out for something cheaper without re-clearing the entire stack with the government.
Veeva runs the customer relationship management and regulatory document systems of the pharmaceutical industry. Nineteen of the top 20 biopharmaceutical companies use Veeva's regulatory information management platform. Those systems are validated under GxP — the good-practice quality regulations that govern anything touching a drug — and 21 CFR Part 11, the Food and Drug Administration's rule for electronic records and signatures. Every major release is formally qualified. When an FDA inspector arrives, the audit trail in that system is the company's defense.
You cannot replace that with a model that is very good at writing code. You would have to re-validate a decade of regulated records, in front of a regulator, on a system with no track record, to save a fee that rounds to nothing in terms of the cost of building a new drug.
How small a fee? Veeva's licensing runs somewhere between roughly $1,800 and $6,600 per sales representative per year. A fully loaded pharmaceutical sales rep costs the employer between $134,000 and $219,000 a year. The software is 1% to 5% of the cost of the person using it.
Microsoft raised the price of a Microsoft 365 E3 seat from $36 to $39 per user per month on July 1 of this year, and E5 from $57 to $60. Add Copilot at $30 and a fully loaded E5 seat costs $1,080 a year. Against a knowledge worker costing $75,000 to $120,000 all-in, that is roughly 1% of the employee.
This is the part the compute maximalists cannot see. These companies are not selling labor. They are selling the rails on which labor runs, at a price so far below the value created that the buyer never bothers to negotiate hard, and with switching costs so high that the buyer could not leave even if he wanted to.
Do people try to leave? Constantly. And they almost always fail. (Ask me how I know!)
Panorama Consulting Group's tracked studies of enterprise resource planning replacements put average cost overruns at 189% across industries. Gartner projects that by 2027, more than 70% of recently implemented ERP initiatives will fail to fully meet their original business goals. Ripping out a core enterprise system is one of the most reliably disastrous things a large company can attempt, and it was true before anyone had heard of a transformer model.
The incumbents are not being disintermediated by artificial intelligence. They are selling it!
Microsoft passed 30 million paid Copilot seats in the June quarter, up from 15 million in January. Tech wizards like Leo hate copilot. Just like they hated Windows ’97. And everything else Microsoft has ever built. So what? Accenture alone bought 740,000 of them. Bayer, Johnson & Johnson, Mercedes-Benz and Roche have each deployed more than 90,000. Microsoft's commercial remaining performance obligation — contracted revenue not yet recognized, which is the closest thing software has to a railroad's signed freight contracts — stands at $678 billion, up 84% year over year!
Adobe's AI-first annual recurring revenue passed $500 million in the quarter ended May 2026 and tripled year over year. Salesforce's Agentforce went from $800 million of annual recurring revenue in the January quarter to $1.2 billion by April, up 205%. Veeva is giving its AI agents away free inside Vault CRM through 2030, which is the single most revealing data point in the set: Veeva does not need to monetize AI, because Veeva's moat is the validated record, not the intelligence applied to it.
Aschenbrenner thought AI would eat the applications. Instead the applications are selling AI as an upsell on top of a subscription the customer cannot afford to cancel – because it costs nothing compared to the value it delivers.
These software companies are computing toll booths: they’re what enterprises pay to implement compute. And, as compute gets cheaper, they will generate vastly more revenue, not less. The proof is sitting there in their earnings and cash flows: they’re riding on lower and lower cost of compute, which makes their business more and more efficient.
· Adobe: 36.6% operating margin, 35.6% return on invested capital, capital expenditure of $179 million on $23.8 billion of revenue — 0.75% — and $9.85 billion of free cash flow.
· Veeva: 28.7% operating margin, 68.5% return on invested capital, a 44.3% free cash flow margin, and effectively no capital expenditure at all.
· Salesforce: $41.5 billion of revenue, roughly $14.4 billion of free cash flow, capital expenditure of about 1.4% of revenue, and $72.4 billion of contracted backlog.
· Intuit: $18.8 billion of revenue, roughly $6.1 billion of free cash flow, $124 million of capital expenditure.
Veeva earns 68 cents a year on the dollar. And invests nothing it growing its business.
Adobe currently trades at about 11 times trailing earnings. Salesforce at about 13. Intuit at about 14. These are the multiples of a dying industry, applied to businesses converting a third to nearly half of every revenue dollar into free cash.
This enormous mispricing was manufactured by people who like Aschenbrenner, believed these businesses were doomed. But they aren’t.
And that’s not all.
Aschenbrenner assumed that because a technology is transformative, the capital that builds it will earn its cost.
There is no relationship between those two things. In fact, it’s more likely not to be true.
Leo’s own essay contains the tell: "Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans."
He wrote that as a bull case. But it isn’t. That is a recipe for a financial disaster.
https://t.co/gqUIUAzmYP, Inc. (NASDAQ: AMZN) spent $131.8 billion of capital expenditure in 2025 against $139.5 billion of operating cash flow. That is 94.5% of everything the business generated, poured back into the ground, in a single year. Its 2026 cap ex guidance is $220 billion.
Alphabet Inc. (NASDAQ: GOOGL) spent $91.4 billion in 2025, 55.5% of operating cash flow, and guides to $195 billion to $205 billion this year.
Meta Platforms, Inc. (NASDAQ: META) spent $72.2 billion, 62.4% of operating cash flow, and guides to $125 billion to $145 billion.
Microsoft spent $115.9 billion in the fiscal year that just ended, against $182.9 billion of operating cash flow. Capital expenditure was 34.9% of revenue, up from 18.1% two years earlier. Free cash flow fell to $67.0 billion from $74.1 billion in fiscal 2024, on revenue that grew by more than a third over the same span. Microsoft is running harder and generating less cash. That is what a huge capital cycle does even to the best business in the world.
Moody's projects hyperscaler capital expenditure of $785 billion in 2026 and close to $1 trillion in 2027, funded in part by roughly $175 billion of debt issuance this year. Where will the money come from…?
Oracle: fiscal 2026 capital expenditure of $55.7 billion, free cash flow of negative $23.7 billion, capital expenditure at 82.6% of revenue, long-term debt up from $76.3 billion to $124.7 billion, and $248 billion of future data-center lease obligations not yet on the balance sheet.
CoreWeave: $5.13 billion of 2025 revenue, $14.9 billion of capital expenditure, negative $7.25 billion of free cash flow, net debt at 8.1 times EBITDA, term loans at 11% to 15%, a weighted-average short-term borrowing rate of 12.3%, and a $1 billion private placement in April 2026 at 9.75%.
Meta's Hyperion campus in Louisiana is financed through a special purpose vehicle in which Blue Owl Capital holds 80% and Meta holds 20%, funded by $27.294 billion of senior secured notes at a 6.581% coupon maturing in 2049. The noteholders have no pledge on the physical data center. Their credit is Meta's promise to pay rent starting in 2029, plus a residual value guarantee. Twenty-seven billion dollars of debt, secured by a lease, sitting off the balance sheet.
And… like the EU’s finance minister explained two decades ago… “when it gets serious, you have to lie.”
Microsoft extended server useful lives from three years to four, then to six, adding about $3.7 billion to fiscal 2023 operating income. Alphabet did the same, adding about $3.0 billion. Amazon added about $2.5 billion in 2024. Meta added $2.59 billion in 2025. Oracle added $573 million. Every one of those is a non-cash increase in reported profit produced by an assumption about how long a chip stays useful. It’s a lie.
But not everyone is lying. Effective January 1, 2025, Amazon shortened the useful life of a subset of its servers and networking equipment from six years back to five, citing, in its own 10-K, "the increased pace of technology development, particularly in the area of artificial intelligence and machine learning." That cost it $1.4 billion of additional depreciation and $1.0 billion of net income.
Amazon is the operator with the longest and hardest-won experience running data centers at scale, and Amazon is the one telling you the hardware wears out faster than the schedules assume.
How could all of this spending possibly pay off?
Bain & Company's global technology report puts it at roughly $2 trillion of annual artificial intelligence revenue by 2030, and calculates that even if every dollar of on-premise IT budget shifted to the cloud and every dollar of AI productivity savings were reinvested, the industry would still be about $800 billion short. Sequoia Capital's David Cahn, who has been running the same arithmetic since 2023, has escalated his estimate from $200 billion to $600 billion to roughly $840 billion.
Against that: OpenAI's audited 2025 revenue was $13.07 billion, with an operating loss of $20.92 billion. Anthropic's 2025 revenue was $10 billion. Combined, $23 billion.
And of every dollar spent on Nvidia systems, roughly 72 to 75 cents is Nvidia's gross profit. Data center is now 88% of Nvidia's revenue. The margin is not in the build-out. The margin is in selling to the build-out.
What’s about to happen is obvious, because it has happened before.
Between 1865 and 1873 the United States built the most consequential physical network in its history and destroyed an enormous amount of capital doing it.
Track mileage went from 35,085 miles in 1865 to 52,922 in 1870 to 74,096 by 1875. Construction peaked at 7,439 miles laid in 1872. Railroad capital reached roughly $4.5 billion at a time when the entire banking system's capital was $720 million and the federal debt was $2.3 billion. In January 1870, of 896,596 shares traded on the New York Stock Exchange, 781,340 — 87% — were railroad shares. From 1870 to 1874, roughly 70% of all railroad securities issued in London were American. American rail bonds paid 6.5% when British consols paid far less, and European capital came for the yield.
Every argument you hear today was made then, too. The railroads will transform the country. Yep, they did compress distance and cost of transportation in a way that seemed impossible only a few years earlier. And it didn’t make any difference.
On September 18, 1873, Jay Cooke & Co. failed. Cooke had contracted to place $100 million of Northern Pacific 7.3% gold bonds, but sold less than $20 million. He ended up effectively owning 75% of the railroad he was supposed to be financing. And it failed. The New York Stock Exchange closed for ten days — the first closure in its history.
By 1876, 134 railroads were in default on $500 million of bonds out of roughly $2 billion outstanding. By 1877, 20% of American railroad track mileage was in receivership. European investors are estimated to have lost around $600 million between 1873 and 1879.
A very large fraction of the capital that built the American rail network was lost.
And where the roads survived, competition took the returns. Revenue per ton-mile fell from 1.88 cents in 1870 to 0.73 cents in 1900, a decline of about 61%. Rate wars on the New York-to-Chicago corridor drove the through rate from $1.88 down to 25 cents, then 20 cents, and no pooling agreement stabilized the worst of it until late 1885.
Every additional mile of track made the network more valuable to America and less valuable to the men who had paid for it.
The AI build-out will have the same problem – but it will be much, much worse. Compute will be a pure commodity.
Nobody disputes that the models are transformative. The problem is, that’s true of all of them.
Which of the second-derivative names Aschenbrenner owned has route control, like a monopoly railroad? Bitcoin miners with retrofitted substations? Rented compute resold at a spread? Memory, an industry that has never once earned its cost of capital through a full cycle? Those are not toll booths. Those are the Northern Pacific just before bankruptcy.
The railroads made a fortune – but not for their investors.
Adams Express Company was incorporated in 1854 with $1.2 million of capital. It did not own a single mile of track. It bought space on other men's trains and moved parcels, money and valuables on them. By 1866 its capital was $10 million and it was paying an 8% dividend quarterly. By 1875 its capital was $12 million. It paid an unbroken $8 per share annual dividend from 1869 forward — straight through the depression that put a fifth of American rail mileage into receivership, and straight through the next one in the 1890s.
American Express Company (NYSE: AXP) declared a $6 dividend in 1869, cut it to $3 in the depression year of 1877, restored it to $6 by late 1881, and held it there for the rest of the century. An 1888 board report showed ten-year net earnings of $26.24 million.
By 1890, the express companies were handling more than 115 million packages a year over 174,535 miles of railroad and steamship routes. And they didn’t own a single locomotive or a single boat.
Pullman's Palace Car Company was organized in 1867 with $1 million of capital. It did not own track either. It owned the sleeping cars and leased them to the railroads. Capital grew to $36 million by the early 1890s with nearly $25 million of accumulated surplus. Dividends ran 9.5% to 12% from 1867 to 1871 and 8% annually for decades after. In 1879, with 464 cars out on lease, it earned gross revenue of $2.2 million and net profit of almost $1 million.
Pullman put out $1 million of equity and earned $1 million a year on a network that cost other people billions and bankrupted a third of them.
Adams Express converted itself into a closed-end investment fund in 1929 and is still listed today as Adams Diversified Equity Fund (NYSE: ADX). The company that rented space on the railroads outlived almost all of them.
I’d bet a lot of money that Leo had never heard of any of these businesses.
But for people who are experienced in putting capital at risk, the pattern is not subtle or hard to understand. When an economy builds an expensive new network, the capital that builds the network earns a poor return because competition, obsolescence and overbuild strip it away. The businesses that ride on the network at near-zero incremental capital cost, and that own the customer relationship, the data or the standard, keep the profit.
I’ve seen this entire act before, during my career.
In the five years after the Telecommunications Act of 1996, carriers poured more than $500 billion into fiber, switches and wireless networks. By the early 2000s no more than 2% of North American long-haul capacity was in use. Global Crossing raised roughly $20 billion, built 100,000 miles of undersea fiber, filed for bankruptcy in January 2002, and saw its assets change hands for about $250 million — roughly 1.25 cents on the dollar of invested capital. WorldCom filed six months later, at the time the largest bankruptcy in American history.
Who got the value? Google, Amazon and Netflix, which built businesses on top of bandwidth that had become nearly free because somebody else had already gone bankrupt providing it. By 2018 and 2019, Google and Facebook were funding roughly four of every five dollars of new transatlantic cable investment — buying the rails only once the rails were cheap and only once they owned the applications that made the rails worth owning.
Leopold Aschenbrenner is not stupid. He is the opposite of stupid, which is part of the problem. He is a brilliant technologist who has never had to make a payroll, never had to explain to an auditor why the electronic records changed, never had to decide whether to spend eighteen months and $40 million ripping out a working system to save $200,000 a year in license fees.
He looked at enterprise software and saw code. A businessman looks at enterprise software and sees the thing his company cannot operate without for a single day, priced at 1% of the employee who uses it, backed by a validated audit trail he would have to rebuild from scratch in front of a regulator, and running on a contract he signed for three years.
An investor who has read a balance sheet from 1874 sees $220 billion of annual capital expenditure, an 8-times-levered reseller of rented compute borrowing at 12%, $27 billion of data-center debt hidden in a special purpose vehicle, and useful-life assumptions that the most experienced operator in the business is quietly walking back.
The kid believed the technology determines the return. But it never has.
It’s the capital structure that determines the returns: who controls the standards, who controls the customer, and who owns the data? Yes, the A.I. models will change everything. But that does not mean the people building the machines will be paid for it.
The money will be made where it was made in 1874 and again in 2004: by the toll booths riding on top of somebody else's ruinous capital expenditure.

[Brivael Le Pogam @brivael]

Encore un scandale.

On accuse Elon Musk sans arrêt de se servir de Grok pour faire passer ses idées.

Pourtant, le @neutralityorg vient de sortir The Neutrality Project : une étude indépendante qui prouve l’inverse.

Sur 18 modèles IA testés, 54 positions sur 60 atterrissent à gauche du centre.

Moyenne globale : -0.41 (biais progressiste massif, surtout environnement et valeurs sociales).

Et Grok 4.5 ? -0.02. Le modèle le plus neutre de tout le benchmark. Le seul vraiment proche du centre.

Pendant ce temps, les autres boîtes injectent massivement leur idéologie de gauche dans des IA qui répondent à des milliards de questions par jour.

Manipulation des masses à grande échelle.

C’est absolument scandaleux. Ça devrait être un scandale mondial.

https://t.co/X1tSvJfDEY

[@levelsio @levelsio]

🇪🇺 Chat Control has passed 😔

They can and will now legally scan any person's messages, emails and photos you send without a warrant

The way they passed this law when the majority of the European Parliament was against it will shock you:

They waited until most EP members were on holiday so only a few were present and then created an "urgent" vote for it to pass it through

There's nothing democratic about any of it and big powerful forces are behind this that can manipulate the EU for whatever they want

Democracy in Europe died a bit today 😔

[Pedro Santa Clara @psantacl]

https://t.co/L5Sosdbqdh

[Nuno Palma (em português) @nunopgpalma_pt]

Dia 30 de Julho, a convite da CIP e do IDL, James A. Robinson (autor do "Porque Falham as Nações" e Nobel da Economia 2024) e eu iremos apresentar, em Lisboa, um estudo sobre como reformar a economia portuguesa.
Mais novidades em breve! 👇🏼

[Afshine Emrani MD FACC @afshineemrani]

5/5
I think about Barry Marshall constantly.
Because I'm watching the same pattern in cardiology right now.
For decades, we told patients that heart disease was about cholesterol. Statins save lives — settled science. But patients kept having heart attacks with "perfect" numbers, and we kept treating the smoke while the fire burned underneath.
The fire is inflammation. The CANTOS trial proved that lowering inflammation independent of cholesterol reduces cardiac events. AI can now detect inflamed arteries on a CT scan. Low-dose colchicine — an ancient drug — is FDA-approved to quiet the inflammatory cascade that ruptures plaque. IL-6 inhibitors are in Phase 3 cardiovascular outcome trials right now.
The data is here. Just like Marshall's data was here in 1984.
And the gap between the data and the practice is where patients are still dying from conditions we now have the tools to prevent.
Protein misfolding linking heart failure to Alzheimer's — published today. Gut bacteria climbing the vagus nerve to trigger Parkinson's. Gum disease driving the same inflammation I treat in the cath lab. GLP-1 drugs reducing cancer metastasis by 50%.
Every one of these findings is telling us the same thing Marshall proved with a beaker of beef broth: the diseases we separated into different specialties share common roots. And the establishment will resist that truth for as long as the old model remains profitable and comfortable.
The most dangerous sentence in medicine has always been the same: "That's not how we do things."
Marshall drank a billion bacteria because the world wouldn't listen any other way.
You don't need to drink anything. You need to ask your doctor for an hsCRP test. Push for advanced cardiac imaging. Get your Lp(a) checked. Demand that "your numbers look fine" means ALL the numbers — not just the ones from a panel designed forty years ago.
The gap between data and acceptance is where people die from treatable conditions. Marshall proved that in 1984. The question is whether we'll learn faster this time.
I think we can. But only if patients stop accepting incomplete answers — and start asking the questions the science has already answered.

[José António Silva @canoas]

@carlkolon @grafana Don’t forget that many many orgs download the releases. The access tokens may pave the way to publishing pipelines and then even we all get compromised packages. The problem is not the source code confidentiality, it’s s the supply chain attacks on so many customers.

[Ricardo @Ric_RTP]

AI is the first technology in history where more customers makes you POORER.

Every tech company in history got cheaper as it scaled.

More users meant lower costs per user. That's the entire model.

That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money.

Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve.

This is the single most important rule in tech economics.

But AI completely broke it.

Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware.

There is no "build once, sell forever." There is only "burn money every time someone asks a question."

And the numbers prove it:

OpenAI hit $20 billion in annualized revenue. Losses? $14 billion.

For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew.

Not because they're bad at business, but simply because the model itself is broken.

Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on.

xAI is burning $1 billion every single month.

Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it.

This is not how technology is supposed to work.

Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result.

Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero.

AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned.

This is reverse economics. The more successful you become, the faster you die.

And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies.

But they don't. They NEVER will.

Software scales to infinity. AI scales to bankruptcy.

HSBC ran the numbers on OpenAI specifically.

Their conclusion:

Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability.

The industry response has been to raise prices.

ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept.

Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before.

AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape.

Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it.

They're all margin-negative. Every single one.

And these are the companies about to IPO.

SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026.

They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive.

The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later."

For most of them, later never came.

The question isn't whether AI will change the world. It will.

The question is whether it can do it without going broke first.

And right now, every single number literally says no.

How can they become profitable?

[José António Silva @canoas]

Prompt injection - this is how you teach them to students: with real impact!

[P. Duchement @PDuchement]

He introducido un regalito en el enunciado de un trabajo que he marcado a mi alumnado: Una única frase, pero escrita con letra blanca sobre fondo blanco (difícilmente visible para ellos, pero accesible si copias y pegas en un prompt).

Y… me lo estoy pasando pipa corrigiendo.

[José António Silva @canoas]

E isto mesmo:

[Jason Shuman @JasonrShuman]

Silicon Valley thinks AI agents are a $20/mo self-serve subscription.

Main Street is paying local agencies $10,000 just to turn them on.

Everyone assumes AI will be bought primarily online like Slack or Zoom. I think they are wrong.

Some of the biggest winners in the AI boom won't be the software vendors. It will be the humans installing it.

Here is the reality of SMBs right now:
• 54% lack internal AI expertise.
• 41% have data quality too poor for AI to even work.
• 41% already prefer buying AI through a local IT provider.

You cannot "1-click install" a genius AI into a messy CRM or a 15-year-old server. It will just execute the wrong tasks at the speed of light.

The AI software will be cheap and a lot will absolutely be bought online. Making it actually work for a messy, real-world business will be expensive.

Very bullish on the "Do It For Me" economy being back.

[Dan Shipper @danshipper]

new model for engineering team structure in 2026:

2 people only

one pirate and one architect

the pirate's job is to move as fast as possible to develop valuable, shipped product features by vibe coding.

the architect's job is to turn the product surface discovered by the pirate into a reliable, structured machine—also by vibe coding, but at a slower, more well-reasoned pace.

every product needs a pirate but most product's only need an architect once they some form of PMF, and in that case they usually don't need one full-time. architects can work across many codebases and solve interesting technical challenges. pirates go hard on a product that they own end-to-end.

[Paul Rees. ex Rucksack. @HannahIamthest1]

An engineer dies and ends up in Hell. 😈

Engineers don’t usually end up there, so the Devil is pretty surprised. Hell, as expected, is a mess — the AC is broken, the pool is empty, the roads are falling apart, and everything is miserable. 🔥

After some time, the engineer starts fixing things. The AC works again, the pool is full, roads are repaired, and suddenly Hell is… comfortable. 😳😄

Eventually, God looks down and notices people in Hell actually enjoying themselves. Confused, He calls the Devil and asks what’s going on.

The Devil replies,
“That engineer you sent us has been fixing things. He’s doing wonders down here!”

God exclaims,
“WHAT?! Engineers don’t go to Hell! That must have been a mistake. You have to give him back: he belongs in Heaven!”

The Devil crosses his arms and says,
“No way. We like our engineer.”

God says,
“If you don’t give him back, I’ll sue you!” ⚖️

The Devil grins and replies,
“Yeah, good luck with that? Where are you going to get a lawyer?” 😂

[Tech with Mak @techNmak]

"Programmers will automate themselves out of existence."

That's what they said. And programmers laughed so hard they couldn't respond.

Two years into the AI revolution, here's what actually happened:

What the doomers predicted:
1./ AI writes all the code
2./ Developers become obsolete
3./ Only managers and marketers survive
4./ Programming becomes a dead career

What actually happened:
1./ AI writes code that needs debugging by developers
2./ Developers spend more time reviewing AI output than writing from scratch
3./ The skill gap between good and bad developers got WIDER, not narrower
4./ Demand for senior developers who understand what AI can't do went UP

The brutal irony nobody saw coming: AI didn't replace developers. It replaced the developers who thought AI would do their job for them.

Here's the real shift:
- Junior devs who learn to prompt AI? Productive.
- Senior devs who understand system design? Irreplaceable.
- Developers who just copy-paste without understanding? Already obsolete (AI just made it obvious).
- Managers who thought they could skip hiring developers? Drowning in unmaintainable AI-generated code.

We're not laughing because we're safe. We're laughing because the people who predicted our extinction don't understand what we actually do.

AI is a tool. And like every tool before it, it makes good developers better and exposes bad developers faster.

[José António Silva @canoas]

@heygurisingh Claude

[Paul Graham @paulg]

One advantage of having many distinct sovereign nations instead of a single world government is that they can try different policies and we can see which work best.

[Ednaldo Ferreira 🙏🇧🇷🇧🇷 @Ednaldo40916743]

Mano até a Espanha que geralmente é de esquerda não aguenta mais tanta baboseira!!

[Dave W Plummer @davepl1968]

Why the Web was Down Today - Explained by a Retired Microsoft Engineer...