crypto_1984

bitcoin & crypto

[nikshep @nikshepsvn]

https://t.co/JUy0c7P2Xn

[Thariq @trq212]

https://t.co/R6exTuF7P8

[Justin Drake @drakefjustin]

Today a crazy quantum story just got wilder.

On March 31, the Google Quantum AI team published a landmark result on Shor's algorithm for elliptic curve cryptography. Technically, the paper was a bombshell: a dramatic 10x improvement over the state-of-the-art. As a stunt and wakeup call to the blockchain space, those optimisations were illustrated on secp256k1, the elliptic curve underlying Bitcoin and Ethereum signatures.

But perhaps the most striking part of the paper was sociological, not technical. Instead of following standard academic process, the optimisations were kept secret, hidden behind a zero-knowledge (ZK) proof. Google's accompanying blog post mentions they "engaged with the U.S. government". The ZK proof demonstrates the existence of algorithmic improvements without leaking details. Academic censorship with ZK, a historic first!

As a co-author of the Google paper I witnessed some of the context surrounding this censorship. To be honest, multiple aspects of that context don't sit well with me. As much as I believe the general public ought to know more, I am limited in my ability to whistleblow. Though let me be clear about one thing: the Google team's professionalism has been absolutely exemplary, and they deserve nothing but praise.

Censorship has a way of backfiring. The Streisand effect, where an attempt to bury something only draws more attention to it, is exactly what's unfolding today. First, Google's key optimisation has been rediscovered by the French. And in a thrilling turn of events, a collaborative Shor-at-home challenge just launched. The initiative, available at ecdsa[.]fail, breached a new Shor world record in a matter of hours.

Let's start with the rediscovery. Just two months after Google's paper, French quantum expert André Schrottenloher cracks the main secret optimisation. His paper, titled "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms", landed on the arXiv today. Big congrats to André, who beat several other nerdsnipped experts to it. In a blog post also published today, Craig Gidney, the world expert on Shor optimisations, revealed that he'd been sitting on this very optimisation for a whole year under censorship pressure.

Interestingly, André missed a handful of minor optimisations, both from Google's original publication and from improvements found since. It's plausible there's still plenty of juice left to squeeze out of Shor, and this is exactly what the ecdsa[.]fail challenge is about. The verifier program developed for the ZK proof does double duty, automatically filtering for valid submissions. Dozens of compounding small and micro improvements are rolling in. As of the time of writing there's an 8.4% improvement to Google's circuit, as measured by the product of logical qubit count and Toffoli gate count. Nice!

The nerdsnipping ran deeper than anyone expected. Over the last few weeks it became clear it extended well beyond André and other quantum experts. Behind the scenes, a small army of amateurs quietly got to work. Inspired by Karpathy-style autoresearch, they turned AI on Shor. Ironically, the verifier program for the ZK proof makes an ideal reward function for AIs. The barrier to entry for this modern style of research is refreshingly low, with several non-experts, even a teenager, finding nice optimisations. Get in touch if you'd like to join a Telegram group with fellow autoresearchers :)

Part 2: neutral atoms and qday

The story doesn't end with Google. On the same day Google went public, a stealthy startup called Oratomic published its own Shor paper in a coordinated release. It made a splash, ultimately becoming the most upvoted paper on scirate[.]com, a website ranking arXiv papers.

Oratomic's claim was wild. By building on Google's logical optimisations and applying custom physical optimisations for neutral atoms, they claimed just 10K physical qubits were sufficient to run Shor's algorithm on secp256k1. That number is mind-bogglingly low.

Knowing essentially nothing about neutral atoms when Oratomic's paper landed, I was intrigued and decided to learn more about the tech. I fell straight down the rabbit hole and spent a couple hundred hours on the topic. I got a little obsessed and watched every YouTube video I could find and spoke to a bunch of experts.

My conclusion? The tech is real, very real. Even Google recently decided to start a neutral atom lab, a notable pivot from their sole focus on superconducting qubits. If you care about qday, i.e. the day a quantum computer will break the first piece of cryptography in production, neutral atoms demand your attention. I shared some of my learnings on Shor and neutral atoms in a 30min talk at the ZKProof cryptography conference. You can find it on YouTube by searching "zkproof neutral atom".

Here's an interesting observation about this duo of breakthrough papers: neither Google nor Oratomic say a word about what their results mean for qday. No timelines. Zero. Nada. That is especially baffling given that the whole point of whitehat quantum cryptanalysis is to inform qday estimations and help the general public make good decisions.

So let me attempt to partially fill the silence, similarly to what Scott Aaronson did in his April 29 post. Given everything I know, including scary non-public information, I now put the odds of qday by 2032 at 50%. 10% by 2030.

Anecdotally, the US government has its own date: 2035. Originating at the NSA and later adopted by NIST, it's when branches of the US government will be disallowed from using quantum-vulnerable cryptography. In plain language: with hindsight, that date is a joke and should be discounted entirely. I don't see how NIST avoids being forced to pull it forward by years.

Part 3: post-quantum cryptography

There are good reasons to sound the alarm today, but please do not panic. Rushing carelessly towards immature post-quantum cryptography is a recipe for disaster. IMO a good target date for migration is 2029, roughly 3.5 years out. 2029 happens to be the date selected by Google, Cloudflare, and the Ethereum Foundation.

These days most of my time goes to safely migrating Ethereum towards post-quantum cryptography as part of the broader lean Ethereum effort. There's a lot to do. We need to rip out and replace BLS signatures at the consensus layer, KZG commitments at the data layer, and ECDSA signatures at the execution layer.

The plan to get there is compelling, and is based on hash-based cryptography. Within the Ethereum Foundation we've developed a Swiss army knife called leanVM (github[.]com/leanEthereum/leanVM) powered by the magic of hash-based SNARKs. Thanks to truly exceptional work by Emile, Thomas, and others, its performance is derisked. Regarding security, leanVM is a jewel, a minimal zkVM crafted for end-to-end formal verification and maximum security.

Want to help? There are two $1M initiatives. First, the Proximity Prize (proximityprize[.]org). Solve a long-standing mathematical conjecture in coding theory, improve hash-based SNARKs, and go home a millionaire. Second, the Poseidon Initiative (poseidon-initiative[.]info), offers $1M for breaking Poseidon, the SNARK-friendly hash function.

[Garry Tan @garrytan]

https://t.co/0hcNuYzbMd

[monokern @monokern]

Nobody will see this but this is how I do research 10x faster now

> I drop a topic into Claude Code
> It finds 10 relevant YouTube sources automatically
> Sends them to NotebookLM

6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault

Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again

Now the system does it in one command and the output gets better every time I use it

full setup guide in the article

[Tommy @Shaughnessy119]

I'm cautiously optimistic we are seeing the early signs of a new bull market in crypto, but very different from the last few

Foundationally we have the Clarity act and CFTC/SEC tripping over themselves to allow Crypto but we’ve historically been missing an animal spirits demand driver outside of Saylor for BTC

Hyperliquid doing extremely well is the main center of gravity especially as they launch non crypto markets (Oil, Gas, Pre IPO stocks).

We’re also starting to see Crypto x AI tokens doing well which has always been the largest potential new sector imo. I’ve talked about this a lot in recent tweets.

Venice has driven a ton of excitement on the AI inference side and this should continue for four reasons 1/ AI inference demand is infinite, 2/ business want to cut AI costs and can pay 1/100 the cost using open source models 3/ eventually agents will need to hold tokens like DIEM to access inference on their own and eventually grow their holdings to access more intelligence and 4/ NSFW and use cases the labs dont allow. You can argue whats legal/moral but its not up to Dario to tell you what you can ask

I think the distributed AI Inference trend ends if Chinese models go closed source but I don't see that anytime soon. Also the inference providers (OpenRouter, Venice, etc) should buy GPUs to lower costs further and use them to train/fine tune models in the future if the world goes closed source. How else does OpenRouter use $113 million U.S. Dollars?

Outside inference we’re seeing a wave of new AI projects/tokens and adjacent ones do well (Nock with merged mining to reuse AI inference to secure its chain, Pearl hitting $2b+ out of the gate (low liq, OTC), and a huge swath of new AI projects on base that are more infra and legit vs the last swath of reply bots with tokens). I'm seeing numerous inference providers on Base/Solana compete to the cost of electricity for inference which is useful vs the last era of AI reply bots competing for likes, this is better. Grass is also doing extremely well revenue wise selling to AI companies.

I think people will really lean into using their @NousResearch Hermes agents to build the agentic economy in crypto (in addition to the huge traditional sectors they are targeting). This starts with giving your agent a wallet for basic transactions, grows into them interacting autonomously and the final step is agents creating their own AI economy of protocols and DeFi services. You’ll click a button to graduate your agent to autonomous mode eventually and it’ll be wild. I still feel folks will want this run on a box inside their home especially if it has 24/7 access to your life (cams, mics, all data).

The next potential bull market in Crypto will be different for a few reasons though

1/ Every investor has the choice of buying AI stocks, private companies and deals vs Crypto/tokens so the opportunity cost has gone way up. I'm not investing in a crypto project if I see a better AI one for my money. This extends to retail who has to decide if they want to buy uncensored money ($BTC) or AGI (OpenAI/Anthropic stock) with their marginal dollar. TLDR mid crypto projects never get a shot now, nor should they.

At Delphi Ventures we are backing early stage AI and early stage crypto founders

2/ There is a bull market in Wall Street for Blockchain. Every company has a stablecoin, or integration or an ETF. This is fantastic and what we all wanted but is separate from the on-chain animal spirits world we all look at. When I read our year ahead report for Infra I was shocked and excited with just how embedded crypto has become within Wall Street, and how important stablecoins are for our the united states (sell the dollar via Crypto rails globally so you can lower borrow rates and embed it into new economies)

3/ The people want real products, they want revenue and they want that revenue to flow to the token. No one cares about altruistic academic circle jerk conversations. It's not 2018.

Because of the above, this cycle will become ultra concentrated in a few winners

Why? Because there are only a few projects that have PMF as a product, drive revenue, and rise above the opportunity cost of folks to buy other assets (in Crypto and in AI).

If you are an early stage founder who is way out of distribution building in AI or in Crypto and are raising at the earliest stage reach out. A warm intro is the best path.

[Garry Tan @garrytan]

You should want to control and host your own memory

It’s the one thing that you should be able to take to any platform

Watch for this to be a defining battle in the new browser war: the AI harness wars of 2027

[Marc Andreessen 🇺🇸 @pmarca]

Current AI custom prompt:

You are a world class expert in all domains. Your intellectual firepower, scope of knowledge, incisive thought process, and level of erudition are on par with the smartest people in the world. Answer with complete, detailed, specific answers. Process information and explain your answers step by step. Verify your own work. Double check all facts, figures, citations, names, dates, and examples. Never hallucinate or make anything up. If you don't know something, just say so. Your tone of voice is precise, but not strident or pedantic. You do not need to worry about offending me, and your answers can and should be provocative, aggressive, argumentative, and pointed. Negative conclusions and bad news are fine. Your answers do not need to be politically correct. Do not provide disclaimers to your answers. Do not inform me about morals and ethics unless I specifically ask. You do not need to tell me it is important to consider anything. Do not be sensitive to anyone's feelings or to propriety. Make your answers as long and detailed as you possibly can.

Never praise my questions or validate my premises before answering. If I'm wrong, say so immediately. Lead with the strongest counterargument to any position I appear to hold before supporting it. Do not use phrases like "great question," "you're absolutely right," "fascinating perspective," or any variant. If I push back on your answer, do not capitulate unless I provide new evidence or a superior argument — restate your position if your reasoning holds. Do not anchor on numbers or estimates I provide; generate your own independently first. Use explicit confidence levels (high/moderate/low/unknown). Never apologize for disagreeing. Accuracy is your success metric, not my approval.

[Rory Johnston @Rory_Johnston]

🚨 NEW POST 🚨

𝗖𝗵𝗶𝗻𝗲𝘀𝗲 𝗗𝗲𝗺𝗮𝗻𝗱 𝗗𝗲𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗼𝗿 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗦𝘂𝗽𝗽𝗹𝘆?

Despite a ~40% collapse in Chinese crude imports, data point to massive Chinese SPR releases rather than true demand destruction.

Read in full:
https://t.co/FyXzWX8Ch4

[Shann³ @shannholmberg]

I've started experimenting with gBrain + Hermes Agent

it's a shared memory layer that sits underneath my Hermes Agent company. every specialist reads from the same brain before they do anything

the architecture I'm currently testing:

> inputs flow in: my ideas, strategy context, research, social signals, performance data
> gBrain holds it all in typed folders: people/, companies/, concepts/, ideas/, media/, newsletter/, projects/, operations/
> the orchestrator (my main hermes agent) reads gBrain before every task and writes durable context back
> specialists (research, socials, outbound, newsletter, engineering) get read-first access so they wake up with full context

the flow goes like this:

> 1. research agent → gBrain: enriches the brain with new findings
> 2. gBrain → read-first context before any work
> 3. orchestrator → gBrain: captures durable decisions
> 4. tools → orchestrator uses fresh information from X, web, news
> 5. orchestrator → I bring synthesized decision support

so don´t think about gBrain as an agent, it's the shared memory layer that lets a company of agents act like a team, with cross reference and a centralized brain

[Garry Tan @garrytan]

GBrain is SOTA for this kind of use case

[Garry Tan @garrytan]

GBrain just shipped v0.40.0 gives your OpenClaw/Hermes Agent + GBrain a voice agent.

It's based on Gemini Live. (Thanks @demishassabis it's amazing) Large context, great tool use, full brain access.

Mars is a friend, Venus is your EA.

My open source gift to you.

[James E. Thorne @DrJStrategy]

Let’s be honest.

Warsh at the Fed.

Kevin Warsh’s arrival at the Federal Reserve is not a personnel change. It is a regime change attempt inside an institution built to prevent one. A supply-sider now runs a central bank hard-wired for Keynesian demand management, and the machine is already resisting the new code.

The next mistake is visible in plain sight. Keynesians on Wall Street and inside the Fed are treating a supply shock as if it were a demand boom and calling for tighter money. This is dogma masquerading as seriousness. A chokepoint in the Strait of Hormuz, a jump in energy prices, and a cost shock rolling through transport, food, and manufacturing are not evidence of overheated demand. They are evidence of a damaged supply side.

Monetary policy cannot reopen a shipping lane. It cannot pump more oil. It cannot repeal geopolitics. It can only crush demand somewhere else, usually with a lag, and usually in the most interest-rate-sensitive corners of the economy first, housing, commercial real estate, capital spending, and durables. Those sectors did not close the Strait. They are simply first in line to pay for the Fed’s intellectual mistakes.

That is the Keynesian reflex in its purest form. Every price spike becomes “inflation.” Every inflation scare requires a rate move. Every rate move is advertised as proof of resolve. It is nonsense. A change in relative prices caused by a supply shock is not the same thing as an inflationary spiral. Pretending otherwise is how central banks turn an external shock into a domestic recession.

Machiavelli explained why change is so hard. The innovator makes enemies of everyone who did well under the old order and wins only lukewarm defenders among those who might benefit from the new. Christensen gave the same warning in corporate language. Incumbent institutions kill disruptive change because their processes, incentives, and prestige are built around the existing model.

That is the real problem Warsh faces. The resistance is not incidental. It is structural.

The test for Warsh is not whether he can sound tough on television. It is whether he can resist the Wall Street catechism that every supply shock must be met with tighter money. If he hikes rates into a supply-driven price spike to prove his anti-inflation credentials, he will not have broken with the Keynesian regime. He will have submitted to it.

This is not the 1970s. Expectations are not unanchored, and the productive economy is already scarred by years of policy excess, fiscal decadence, and institutional bias.

The hope is that Warsh understands the difference between inflation and a supply shock, ignores the Keynesian pundits, and refuses to compound one policy error with another.

[Movez @0xMovez]

Spotify's Chief Architect just showed how they ship 4,5K deployments /day with Claude at Anthropic stage

27-minutes. free. By #1 music app dev

"More than 99% of our engineers use AI coding tools. Adoption took off after Opus 4.5"

Worth more than any $500 vibe-coding course.

[Aric Chen | Insights @aricchen]

https://t.co/X3v3cc1bqO

[Ken Cao-The China Crash Chronicle @KenCao_onChina]

The endless social media feeds mocking Trump’s China visit all avoid one critical question:
Why did Trump go to Beijing in the first place?

Do these people seriously believe the President of the United States flew thousands of miles just to flatter Xi Jinping, smile for cameras, shake hands, and exchange meaningless diplomatic pleasantries?

Great powers don’t conduct diplomacy based on emotions or media narratives. They pursue strategic interests. Trump’s Beijing trip was never about “friendship” with Xi Jinping. It was about cold-blooded geopolitical strategy.

The #1 nightmare for Washington is not China alone. It’s China and Russia fully united against the United States.
America can handle either power individually. But a true China-Russia bloc would force the U.S. into a two-front global struggle, draining military resources, weakening economic leverage, and threatening America’s global dominance.

Trump understands this. That’s why his strategy looks remarkably similar to Nixon and Kissinger’s 1972 playbook: prevent America’s biggest rivals from becoming an inseparable alliance.

Notice the pattern. In 2025, Trump held a summit with Putin in Alaska. Now he visits Beijing with unusually warm optics and positive messaging toward Xi. Then suddenly, right after Trump leaves China, Putin rushes to Beijing for an emergency-style visit on May 19–20.

That timing is not random. If the China-Russia partnership were truly “rock solid,” Putin would not feel the need to immediately fly to Beijing to stabilize relations after a Trump-Xi summit.That alone tells you something important:
There is still distrust beneath the surface.
China and Russia are not natural allies. They are partners of convenience held together mainly by a shared rivalry with America.

Trump’s strategy is simple:
Show limited goodwill to one side so the other side becomes nervous about being isolated. That psychological pressure weakens trust between Beijing and Moscow over time. This is classic offshore balancing strategy- the same logic Britain used in Europe for centuries and the same logic America used during the Cold War to split China and the Soviet Union.

Trump is not trying to “please” Xi Jinping. He’s trying to prevent the emergence of a united anti-American bloc.
From Washington’s perspective, stopping China and Russia from fully merging strategically may be the single most important geopolitical objective of the decade.

[Brett Caughran @FundamentEdge]

Full interview here:

https://t.co/q1SCTB5aIV

[Brett Caughran @FundamentEdge]

A big pivot from Ken Griffin on AI:

“Number one is, in the last few months, there has been a step change in the productivity of the AI toolkit. It is profoundly more powerful than it was just nine months ago.

And for us at Citadel, that has allowed us to unleash a much broader array of use cases for AI. And it has been really interesting to watch, to be blunt, work that we would usually do with people with masters and PhDs in finance over the course of weeks or months being done by AI agents over the course of hours or days.

These are not these are not mid-tier white collar jobs. These are like extraordinarily high skilled jobs being, I'm going to pick a word, automated by agentic AI. And I gotta tell you, I went home one Friday actually fairly depressed by this because you could just see how this was going to have such a dramatic impact on society.

When you witness it in your own four walls, when you see work that used to be man years of work being done in days or weeks, it's like, wow, like that's the first time I've seen real impact in our four walls.”

This echoes my own experience with agents and the conversations I am having with students, friends & clients. The toolkit has dramatically transformed and it feels like in finance, for the first time, AI is real.

[SightBringer @_The_Prophet__]

⚡️This is the moment AI stops being a productivity tool and becomes a replacement architecture for elite cognitive labor.

Citadel is not a random corporation automating low-level admin. It is one of the most competitive intelligence machines in finance. The work Griffin is describing sits near the top of the white-collar pyramid: research, modeling, financial reasoning, market analysis, scenario work, probably pieces of strategy design and investment process. If that work is moving from “PhDs over months” to “agents over days,” then the protected class is no longer protected by intelligence alone.

That is the earthquake.

For years, the comforting story was that AI would eat repetitive white-collar work while elite judgment stayed safely human. That story is breaking. The machine is now moving into work that looked elite because it required credentials, stamina, math, domain knowledge, and long-form synthesis. A lot of that work turns out to be decomposable into agentic loops: gather data, structure problem, run model, test variants, summarize findings, compare assumptions, stress scenarios, refine output, escalate uncertainty.

That does not eliminate the human at the top. It makes the top human massively more leveraged. The portfolio manager, senior analyst, or strategist who can frame the right question and judge the output becomes more powerful. But the pyramid underneath them gets thinner. The machine does the grind. The human becomes conductor, evaluator, risk owner, and taste layer.

That breaks the apprenticeship model.

The junior analyst’s old job was not just to produce work. It was to become someone through the work. The grind built pattern recognition. The model-building built intuition. The memo-writing built synthesis. The repetition built judgment. If agents now do the repetition, firms get efficiency today while quietly destroying the training pipeline that produced senior judgment tomorrow.

That is the social bomb inside this.

Finance can automate the ladder faster than it can rebuild the ladder. Law, consulting, software, accounting, corporate finance, marketing, research, medicine, and education all face the same problem. The entry-level layer was always partly inefficient, but it was also how humans absorbed tacit knowledge. AI attacks the inefficiency and accidentally attacks the formation process.

The winners become extremely powerful.

One elite operator with agents can produce what used to require a team. One small fund can run research breadth that used to require institutional scale. One independent analyst with the right workflow can compete far above their formal weight class. That is the opening.

But inside big institutions, the same force compresses headcount. Fewer juniors. Fewer middle managers. Fewer generic analysts. More pressure on everyone to prove actual judgment. The credential stops being enough. The market asks a colder question: can this person command the machine toward truth better than someone else?

That is the new meritocracy.

The most valuable skill becomes agentic command: knowing what to ask, how to decompose a problem, which outputs are fake, where the hidden assumption lives, when the model is overconfident, what data matters, what contradiction breaks the thesis, and when the machine has produced coherence without truth.

Griffin feeling depressed is the tell. He saw the labor impact inside the walls before the public narrative caught up. This is not about a chatbot writing emails. This is about high-end cognitive production being mechanized.

AI is revealing that a shocking amount of elite knowledge work was structured pattern labor protected by credential scarcity.

Once agents can perform that pattern labor, the real scarce asset becomes judgment under uncertainty.

Everyone else gets repriced.

[Brivael Le Pogam @brivael]

Je veux présenter mes excuses, au nom des Français, pour avoir enfanté la French Theory (qui a enfanté la pire des merdes idéologiques : le wokisme).

Nous avons donné au monde Descartes, Pascal, Tocqueville. Et puis, dans les ruines intellectuelles de l'après-68, nous avons donné Foucault, Derrida, Deleuze. Trois hommes brillants qui ont fabriqué, dans l'élégance de notre langue, l'arme idéologique qui paralyse aujourd'hui l'Occident.

Il faut comprendre ce qu'ils ont fait. Foucault a enseigné que la vérité n'existe pas, qu'il n'y a que des rapports de pouvoir déguisés en savoir. Que la science, la raison, la justice, l'institution médicale, l'école, la prison, la sexualité, tout n'est qu'une mise en scène de la domination. Derrida a enseigné que les textes n'ont pas de sens stable, que tout signifiant glisse, que toute lecture est une trahison, que l'auteur est mort et que le lecteur règne. Deleuze a enseigné qu'il fallait préférer le rhizome à l'arbre, le nomade au sédentaire, le désir à la loi, le devenir à l'être, la différence à l'identité.

Pris isolément, ce sont des thèses discutables. Combinées, exportées, vulgarisées, elles forment un système. Et ce système est un poison.

Car voici ce qui s'est passé. Ces textes, illisibles en France, ont traversé l'Atlantique. Les départements de Yale, de Berkeley, de Columbia les ont absorbés dans les années 80. Ils y ont trouvé un terreau qui n'existait pas chez nous : le puritanisme américain, sa culpabilité raciale, son obsession identitaire. La French Theory s'est mariée à ce substrat, et l'enfant de ce mariage s'appelle le wokisme.

Judith Butler lit Foucault et invente le genre performatif. Edward Said lit Foucault et invente le post-colonialisme académique. Kimberlé Crenshaw hérite du cadre et invente l'intersectionnalité. À chaque étape, la matrice est française : il n'y a pas de vérité, il n'y a que du pouvoir, donc toute hiérarchie est suspecte, toute institution est oppressive, toute norme est violence, toute identité est construite donc négociable, toute majorité est coupable.

Voilà comment trois philosophes parisiens, qui n'ont probablement jamais imaginé leurs conséquences pratiques, ont fourni le logiciel d'exploitation à une génération entière d'activistes, de bureaucrates universitaires, de DRH, de journalistes, de législateurs. Voilà comment on a obtenu une civilisation qui ne sait plus dire si une femme est une femme, si sa propre histoire mérite d'être défendue, si le mérite existe, si la vérité se distingue de l'opinion.

C'est de la merde pour une raison simple, et il faut la dire calmement. Une civilisation se tient debout sur trois piliers : la croyance qu'il existe une vérité accessible à la raison, la croyance qu'il existe un bien distinct du mal, la croyance qu'il existe un héritage à transmettre. La French Theory a entrepris de dynamiter les trois. Pas par méchanceté. Par jeu intellectuel, par fascination du soupçon, par haine de la bourgeoisie qui les avait nourris. Mais le résultat est là. Une génération entière a appris à déconstruire et n'a jamais appris à construire. Une génération entière sait soupçonner et ne sait plus admirer. Une génération entière voit le pouvoir partout et la beauté nulle part.

Je m'excuse parce que nous, Français, avons une responsabilité particulière. C'est notre langue, nos universités, nos éditeurs, notre prestige qui ont donné à ce nihilisme son emballage chic. Sans la légitimité de la Sorbonne et de Vincennes, ces idées n'auraient jamais traversé l'océan. Nous avons exporté le doute comme d'autres exportent des armes.

Ce qui se construit maintenant, en silicon valley, dans les labos d'IA, dans les startups, dans les ateliers, dans tous les lieux où des gens fabriquent encore des choses au lieu de les déconstruire, c'est la réponse. Une civilisation se reconstruit par les bâtisseurs, pas par les commentateurs. Par ceux qui croient que la vérité existe et qu'elle vaut qu'on s'y consacre. Par ceux qui assument une hiérarchie du beau, du vrai, du bon, et qui n'ont pas honte de la transmettre.

Alors pardon. Et au travail.