Crypto PTSD tells me $SPCX will end up as crypto hyped low-float/high-FDV token launches. Tradfi is at the 'High-FDV is a meme' stage crypto was 2 years ago. But $SPCS can't do 10x at current valuations, especially as unlocks start. Meaning all upside to IPO was taken by insiders. Retail is exit liquidity. Exactly, the set up that Cobie criticized within crypto high-FDV launches. Crypto's mission is to fix this by giving access to retail at lower valuations. But pre-IPOs aren't it. Anyone who bought the pre-ipo perps at 1x-like leverage are underwater. At least we can easily short it. Worse, PreStocks trades at 41% DISCOUNT and 'early' retail crypto degens got rekt. So, crypto x Tradfi merge means that crypto investors are exit liquidity to hyped IPOs. To fix this we still need native crypto assets to do well, those like are born onchain with broad distribution like mining, fair launches or airdrops. When we return to this key crypto value proposition and we start winning, retail will come back to crypto.

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?

Compute is fungible, memory is sovereign.

on chain god

We've heard this same narrative over and over. TAO is cool, but the majority of enterprises and end users will still be going with the large companies like anthropic, OpenAI, Google, etc for their LLM of choice.