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John Fletcher (𝔦, 𝔦)

@Dr_JohnFletcher • 4,919 subscribers

Chief Scientist @ The Innovation Game (TIG) @tigfoundation | Cambridge PhD in Maths + Theoretical Physics | SciFi | DeAI | ❤️ { Maths, Science, Computers }

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Dear Andrej Karpathy, Update on this. Earlier this month The Innovation Game (𝔦, 𝔦) announced a new SOTA routing algorithm had been collaboratively developed and submitted to The Innovation Game. The algorithm demonstrated the largest single perfomance jump in the modern history of the field on standard academic benchmarks: This success is a powerful proof of concept. I believe the wider implications will also interest you. As you know, the "Source" in AI is algorithms and data. These algorithms are typically for "hard to solve but easy to verify" problems. Remarkably, this allows the creation of a market for pricing improvements to these algorithms (roughly, the market is created by "racing" the algorithms, to see which can produce proof-of-work fastest). Availability of a market mechanism means open development of the algorithms can be funded by capturing a portion of the value they generate, and allocating it back to algorithm developers. The allocation is efficient, naturally integrating information (such as hardware availability) through revealed preferences. Importantly, market allocation is also "impersonal", which mitigates the risk to community cohesion that has historically afflicted Open Source projects offering monetary reward. Note: That a market for pricing code could extend Open Source to areas requiring monetary reward was (as far as I know) first suggested by Eric Raymond in 1999 Eric S. Raymond : Conclusion: The structure of Open Source AI means it can operate commercially. For example, value captured via Open Source "dual licensing", with allocation of the value by a market generated by proof-of-work. I'd love to hear your thoughts on this. Please see for more detail.

Dear Andrej Karpathy, Update on this. Earlier this month The Innovation Game (𝔦, 𝔦) announced a new SOTA routing algorithm had been collaboratively developed and submitted to The Innovation Game. The algorithm demonstrated the largest single perfomance jump in the modern history of the field on standard academic benchmarks: This success is a powerful proof of concept. I believe the wider implications will also interest you. As you know, the "Source" in AI is algorithms and data. These algorithms are typically for "hard to solve but easy to verify" problems. Remarkably, this allows the creation of a market for pricing improvements to these algorithms (roughly, the market is created by "racing" the algorithms, to see which can produce proof-of-work fastest). Availability of a market mechanism means open development of the algorithms can be funded by capturing a portion of the value they generate, and allocating it back to algorithm developers. The allocation is efficient, naturally integrating information (such as hardware availability) through revealed preferences. Importantly, market allocation is also "impersonal", which mitigates the risk to community cohesion that has historically afflicted Open Source projects offering monetary reward. Note: That a market for pricing code could extend Open Source to areas requiring monetary reward was (as far as I know) first suggested by Eric Raymond in 1999 Eric S. Raymond : Conclusion: The structure of Open Source AI means it can operate commercially. For example, value captured via Open Source "dual licensing", with allocation of the value by a market generated by proof-of-work. I'd love to hear your thoughts on this. Please see for more detail.

38,141 次观看

Its not every day you wake up to find that the Pope has made your lifes work the central focus of his papacy: “Disarming AI means freeing it from the mentality of “armed” competition [..] This entails a race for ever more powerful algorithms and larger datasets, driven by the desire to secure geopolitical or commercial dominance." - POPE LEO XIV, May 2026 Here, “disarmed” means “neutralised” in the sense that this should not be a differentiator The Innovation Game (TIG) was created to keep data and algorithms open, in order to prevent monopolistic control It's not just an aspiration, it's an economic mechanism that makes open data and open algorithms the rational economic choice • All algorithms are published openly by TIG • If you are willing to make the data you process with an algorithm open, you can use it free-of-charge • Alternatively, if you would like to keep this data private, there is a fee to pay for using the algorithm • All fees are used to fund more open innovation Its an elegant, global, self-reinforcing engine The logical end point of monopoly is that innovation stops We cannot allow that to happen Pope Leo XIV I would be grateful for your thoughts on The Innovation Game

Its not every day you wake up to find that the Pope has made your lifes work the central focus of his papacy: “Disarming AI means freeing it from the mentality of “armed” competition [..] This entails a race for ever more powerful algorithms and larger datasets, driven by the desire to secure geopolitical or commercial dominance." - POPE LEO XIV, May 2026 Here, “disarmed” means “neutralised” in the sense that this should not be a differentiator The Innovation Game (TIG) was created to keep data and algorithms open, in order to prevent monopolistic control It's not just an aspiration, it's an economic mechanism that makes open data and open algorithms the rational economic choice • All algorithms are published openly by TIG • If you are willing to make the data you process with an algorithm open, you can use it free-of-charge • Alternatively, if you would like to keep this data private, there is a fee to pay for using the algorithm • All fees are used to fund more open innovation Its an elegant, global, self-reinforcing engine The logical end point of monopoly is that innovation stops We cannot allow that to happen Pope Leo XIV I would be grateful for your thoughts on The Innovation Game

11,722 次观看

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We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow

We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow

John Fletcher (𝔦, 𝔦)

88,195 次观看 • 4 个月前

Andrej, This sounds extremely useful, and I think it might be even more significant than it first appears. What you describe is not just a knowledge base for information. The structure of the wiki, the queries you file back, etc, encode *how* you do research: which questions to ask, which connections matter, what's worth pursuing. That's “know-how” (in the sense of Michael Polanyi). This sort of knowledge is, currently, overwhelmingly absent from training data, because it was never written down (since there was no point). Now there is, because it significantly improves the AIs performance. But notice what's happening. You propose to build the most efficient mechanism ever devised for making tacit expert know-how / methodology explicit and machine-readable, and then transmitting it, via API, to a third-party model provider. Every query against the wiki is a reasoning trace: see attached video clip. The compiled wiki itself is a structured map of your research process. This is the mechanism described here: Expert know-how is being externalised and captured through ordinary productive use of AI tools. The user gets a better tool. The platform gets a transferable problem-solving strategy. The fact that this works so well could, in a sense, be the problem: the better it works, the more indispensable it becomes, the more know-how flows out, and, realistically, the less choice people have *not* to use it. Your instinct that "there is room here for an incredible new product" is right. But whoever builds it will be sitting on the highest-fidelity capture mechanism for expert know-how ever constructed. The question is: is the data subject to a “data network effect”, by which I mean, the kind of “data flywheel” which gave Google a 25 year monopoly over search? If so, you might be building not only more most powerful tool humanity has ever possessed, but this power might end up in the hands of a single entity. It would be great to hear your thoughts around this.

Andrej, This sounds extremely useful, and I think it might be even more significant than it first appears. What you describe is not just a knowledge base for information. The structure of the wiki, the queries you file back, etc, encode how you do research: which questions to ask, which connections matter, what's worth pursuing. That's “know-how” (in the sense of Michael Polanyi). This sort of knowledge is, currently, overwhelmingly absent from training data, because it was never written down (since there was no point). Now there is, because it significantly improves the AIs performance. But notice what's happening. You propose to build the most efficient mechanism ever devised for making tacit expert know-how / methodology explicit and machine-readable, and then transmitting it, via API, to a third-party model provider. Every query against the wiki is a reasoning trace: see attached video clip. The compiled wiki itself is a structured map of your research process. This is the mechanism described here: Expert know-how is being externalised and captured through ordinary productive use of AI tools. The user gets a better tool. The platform gets a transferable problem-solving strategy. The fact that this works so well could, in a sense, be the problem: the better it works, the more indispensable it becomes, the more know-how flows out, and, realistically, the less choice people have not to use it. Your instinct that "there is room here for an incredible new product" is right. But whoever builds it will be sitting on the highest-fidelity capture mechanism for expert know-how ever constructed. The question is: is the data subject to a “data network effect”, by which I mean, the kind of “data flywheel” which gave Google a 25 year monopoly over search? If so, you might be building not only more most powerful tool humanity has ever possessed, but this power might end up in the hands of a single entity. It would be great to hear your thoughts around this.

John Fletcher (𝔦, 𝔦)

41,341 次观看 • 5 个月前

Remember: Research and engineering are *not* the same $TIG is unique

Remember: Research and engineering are not the same $TIG is unique

John Fletcher (𝔦, 𝔦)

11,405 次观看 • 1 年前

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