ChaΦs

Quant-Trader | φ ∼ |t|^β |

Life is Chaos 🧬

The Paradox does not override.....
Create a new operational Domain
(A ∧ ¬ A ≠ ∅)

[ChaΦs @InfinitusChaos]

Reminiscences of the future that could have been.....

[zerohedge @zerohedge]

Morgan Stanley Explains Why Power Is The Biggest Threat To The AI Revolution https://t.co/iShQd94NaY

[zerohedge @zerohedge]

Hyperscalers vs token prices

[ChaΦs @InfinitusChaos]

Be a Metaplayer

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Jake Brukhman @jbrukh]

Unlike many investors in crypto, I did not pivot to AI in the last few years. However, since 2020, I built some of the deepest understanding in this industry on the intersection of AI and decentralized networks (crypto, web3).

From the start, it was very clear that AI models are a centralizing force and the biggest target for government control. That point became market fact last night, with @AnthropicAI’s export control compliance.

As an investor in decentralized AI, I know that d-networks are a counterbalance to this state of affairs. In particular, the starting point of sovereign, open, public, decentralized AI is the seemingly insurmountable compute problem.

How are people supposed to source more industrial compute for frontier training than these huge trillion dollar companies? The answer is simple: there is enough commodity GPU compute in the world to compete on the frontier, but to make use of it we need new algorithms for training.

That’s what a few companies like @gensynai @PrimeIntellect @bageldotcom @Pluralis @NousResearch @MacrocosmosAI @covenant_ai set out to research, while everyone on the planet told them it was impossible.

The result is that it is not only possible, but it can be cheaper and nearly as efficient as the alternative process.

The second major problem is economic sustainability. Open source models are great, however, they are not economically viable as they don’t have a business model. So far in decentralized AI, only @Pluralis has an answer — by breaking up the weights of the model among participants, we create a business model for tokenized AI models.

This is the moment of truth — will AI become fully centralized and fall under censorship and unilateral government control? Or will the AI world realize the importance of public AI on open decentralized networks?

[Naval @naval]

Science is not a process, a credential, or an institution.

It is the unflinching pursuit of truth, carried out by the few, co-opted by the many.

[ChaΦs @InfinitusChaos]

Modern quant trading is no longer "market modeling" but rather:

Competition between intelligence systems that optimize against other intelligence systems under execution, info-incomplete and latency constraints. The machine does not replace the player, it expands the game space

[ChaΦs @InfinitusChaos]

Garry Kasparov was likely the first world chess champion to grasp, on a grand scale, that the computer was not merely an opponent but an extension of human analytical capacity—and this concept extrapolates directly to quantitative trading and alpha research

[ChaΦs @InfinitusChaos]

Does it hold up against delays, slippage and latency?
Is it capturing hidden risk instead of alpha?
That is the difference between having a prediction and having a monetizable edge.

Those moments when Chess, Quant and Life are One