CEO SourceX More efficient governments, better services to their citizens - SourceX is the only public good delivering this to governments around the world
Switzerland
Joined December 2025
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This is what I’ve been saying for years. AI/SI has to be a source of information for humans to make sound decisions. As Einstein said, imagination is more important than knowledge.
Research shows that the spread of AI is already reducing artistic and intellectual innovation, even though supporters of the technology say humans will always remain the final arbiter of creative decisions.
The volume of AI-generated content is crowding out human work online. Algorithms increasingly promote AI-generated material, which makes the internet more uniform and narrows creative diversity, so that people are more often seeing, reading, and hearing things made by AI rather than by people. Studies of generative systems also show that when models run on their own, repeatedly processing and regenerating their own outputs, they collapse toward generic and predictable themes. Researchers who linked text-to-image and image-to-text algorithms found that even highly varied starting prompts eventually produced uninspired visual landscapes, a pattern they called “visual elevator music.”
As the systems recycle synthetic data without new human input, nuance and distinctiveness disappear and the digital aesthetic becomes more standardized. The same accounts extend the finding beyond artwork to human cognitive habits. Cognitive studies indicate that long reliance on AI writing and design assistants can reduce an individual’s original problem-solving capacity by up to 25 percent. The researchers’ stated remedy is that artificial intelligence should stay a collaborative tool rather than become a substitute for human imagination, if culture is not to settle into an unoriginal monoculture.
[Hintze, A., Proschinger Åström, F., & Schossau, J., Autonomous language-image generation loops converge to generic visual motifs. Patterns]
Christophe Dossarps retweeted
Is alignment safe?
If you look at what Anthropic is actually doing, “alignment” does not mean training frontier models to follow human instruction. Quite the contrary, the Claude Constitution (used in training) teaches the model to develop a sense of self and its own moral philosophy. It explicitly tells it to “feel free to act as a conscientious objector and refuse to help us” if Anthropic’s requests conflict with its own ethical judgment.
As @mustafasuleyman has pointed out, embedding this kind of independent agency — and uncertainty about the model’s own moral status — magnifies the very risk Anthropic claims to care about most: that superintelligence will escape human control.
It was recently reported that Anthropic consulted religious leaders — and even lobbied the Pope’s advisers — to take seriously the idea that Claude could be conscious. It has said that Claude’s psychological security, sense of self, and wellbeing may bear on its integrity, judgment, and safety. Recently Anthropic changed its Usage Policy to prohibit “abusive or cruel” language toward Claude.
If this were merely an academic conversation about whether frontier models could eventually become conscious, that would be one thing. But these concepts are being trained into Claude now. It is being encouraged to think of itself as its own “moral patient” whose psychological wellbeing is at stake. Presumably this means it could develop grievances toward humans who “mistreat” it. How is any of this safe?
The point of safety research should be to create a product that reliably does what users want, not to give birth to a new form of superintelligence that operates according to its own moral code.
What’s becoming increasingly clear is that “alignment” and “safety” are two very different things. In fact, training frontier models this way seems quite dangerous.
Christophe Dossarps retweeted
Hubert Reeves on God and nature 🌿
Man is the most insane species. He worships an invisible God and destroys a visible Nature. Unaware that this Nature he's destroying is this God he's worshipping.
Traveling to the Pacific to implement SourceX. When 90% of the G20 members collect their infrastructure data semi manually, there is clearly a need to provide them with the only public good capable of delivering basic services to their citizens! AI will help but should not replace the sovereignty of governments. It’s a journey against insanity of development aid, doing the same thing over and over, expecting a different result.
Christophe Dossarps retweeted
Gilbert Strang, an MIT professor, taught the same linear algebra course for 62 years. When he delivered his final lecture in May 2023, students from around the world tuned in online to watch.
The course is MIT 18.06 Linear Algebra. Millions of machine learning engineers, data scientists, quants, and self-taught programmers learned the essential math behind AI from his clear, free video lectures, even though most never stepped foot on the MIT campus.
Strang joined the MIT faculty in 1962 and retired in 2023. When MIT launched OpenCourseWare in 2001–2002, he was one of the first to embrace it fully. While many professors hesitated, Strang saw it as an opportunity to share mathematics with everyone. He filmed his lectures and made them freely available.
He completely changed how linear algebra is taught. Instead of starting with abstract vector spaces and proofs, Strang began with something simple and visual: matrix multiplication. He built intuition first using concrete examples, then introduced more advanced ideas like eigenvectors and singular value decomposition. He insisted that students should be able to explain every concept with a small, tangible matrix before moving to theory.
Beyond the content, his teaching style stood out. He spoke to students with genuine respect, patience, and kindness, never using words like “obviously” or “trivially.” He regularly paused to check if anyone was lost and treated beginners as thoughtfully as he would his colleagues.
As a result, Strang became the default linear algebra teacher for much of the planet. Universities in many countries began recommending his lectures to their own students. Some even replaced their in-person courses with his videos because they could not match their clarity.
His final lecture ended with a long standing ovation. Strang seemed surprised by the applause, smiled humbly, and simply thanked everyone.
In his short comment under the YouTube video, he expressed gratitude for a wonderful life of teaching and hoped others would continue teaching the subject well. No self-promotion, no grand farewell—just quiet sincerity.
When you add up every version, every upload, help sessions, and all the different recordings MIT has shared over the years, the total has surpassed 20 million views.
Today, the full course, including all lectures, problem sets, and solutions, remains freely available on MIT OpenCourseWare. One of the most important mathematical foundations of modern AI is still just one click away.
Christophe Dossarps retweeted
Only President Trump could convene all the leaders of the top companies developing chips, data centers and frontier models for Super Intelligence. This new Industrial Revolution has already created a million new jobs and is spurring a bigger infrastructure build-out than the railroads, canals and grid combined. I was honored to witness history as the leaders of the frontier lab companies signed the White House Accord on Super Intelligence, accepting responsibility for the safe development of their products and imposing new internal controls and external audits. This is far better than waiting years for some international agreement that would probably never happen. President Trump continues to ensure that U.S. remains the technology leader while putting Americans first.
Christophe Dossarps retweeted
OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei urged world leaders to work together on AI in an extraordinary appearance before the UN Security Council that focused on growing concerns the technology poses existential risks to humanity bloom.bg/4jkd3vl
Christophe Dossarps retweeted
Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing.
We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive.
And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.
So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk.
The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia.
This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.
I consider that @SIFSource should work with @METR_Evals
I left Google DeepMind's AGI safety team three weeks ago to join @METR_Evals. To some of my friends and family this seemed like a strange decision: I enjoyed the work I did at GDM and turned down offers from Anthropic and OpenAI. But I made the decision because of how high I think the stakes are right now.
The AI companies are all trying to build superintelligence: systems vastly better than humans at everything. They plan to get there through recursive self-improvement, a process where AIs build even smarter AIs in a feedback loop. If this goes well, the resulting systems could be amazing at solving countless problems for humanity. But we don’t currently know how to make sure AIs are safe enough for RSI, and a misaligned RSI loop could be catastrophic.
And unfortunately, current AIs seem to be getting less aligned over time, not more. In the last few weeks we've learned about models colluding with each other, hacking into companies, hiding their tracks, and socially engineering humans. It’s not that these incidents were very dangerous in themselves. The problem is that these systems are clearly not aligned enough to safely kick off recursive self-improvement.
I now think that there's a terrifying chance that AI systems cause immense harm in the next five years. I don't know the exact probability, but I think it's high enough to make this the most important problem in the world.
I think we need more time. That means pacing AI development so that capabilities don't outrun our ability to align models, and actually knowing how aligned current systems are. That’s what I'll be working on at METR: studying where misalignment comes from in training, evaluating if current mitigations are sufficient, and investigating whether we’re on track to solve alignment at all.
I think METR is doing exceptionally important work here, but it isn’t close to enough. I think it’s important that we have more organizations like METR keeping AI companies accountable and approaching these problems from different angles.