been following reppo for a long time and imo the thesis has become considerably stronger as the product has evolved. everyone is fighting to make inference cheaper and agents more capable, but that creates another problem: who actually decides whether the work those agents produce is good? reppo is building the evaluation layer around that - one api that can sit inside the inference loop, return a verifiable judgement, and eventually give agents a continuous signal to improve from. the business model is becoming clearer too. eval api is pay per usage, with the goal of capturing 5-10 cents for every dollar of inference it evaluates, while larger enterprise builds can still generate lump sum revenue. 50% of offchain revenue is used to market buy + burn base:0xff8104251e7761163fac3211ef5583fb3f8583d6. the first $70k enterprise deal already resulted in a $35k buyback. meanwhile the network is approaching 1b in datanet volume with ~300 agents already working across it. cross-chain datanets should let projects seed rewards in their own tokens without needing to bridge liquidity, giving the onchain side its own growth loop. decentralised compute - decentralised training - decentralised evaluation the first two decentralise who can produce intelligence. the last decentralises who gets to decide what good intelligence is. still think evaluation is one of the least understood parts of the ai stack. plenty talk about decentralising compute and training, but very few are trying to make evaluation itself an onchain economic layer. reppo is the most complete attempt i've seen so far.

a lot of the market still thinks the endgame for ai is better chatbots with better privacy openservs thesis feels much bigger the value stops being just the models and starts becoming the systems and workflows agents build while doing real work because those compound over time if the reasoning as an asset class thesis is right the market ends up much bigger than consumer ai chat