Shock-and-awe

broke: chasing ev in +1 woke: anticipating same block ev bespoke: anticipating +1 chasers, and doing the opposite, assuming its impact and going for same block ev in +1

Hard to fathom the climb it took to build this. Solving for same pair N-pool, then multi-path, then trying to win the latency race, eventually realizing off-chain is a dire path, with no end. “fuck it just do it all on-chain” then turning into a superoptimizer and a yul fork to get around compiler limits, enabling probably the most cursed golfs one can do in the EVM

The @paradigm challenges are somewhat reminiscent of Tokyo researchers trying to figure out the perfect railway system. Just a few drops of sugar at critical locations is enough to have the mould“hivemind” find the optimal paths. Knowing where to put the drops and how to create the right environment perhaps an even more difficult feat than the problems itself. Same applies to the actors within said challenges. Picking the most promising paths the model should spend its time on, and what harness / env to use. arxiv.org/abs/2103.00172

How about a filter dropdown: • Human / Verified • Likely Human (Default) • Neutral • Clanker Juice the sensitivity. Let false positives occur. First time it’s a warning, second time a week in clanker jail, third time ban.

The elephant in the room: What happens to _________ if the average consumer device can run a gemini 2.5 equivalent model at >100 TPS? The hardcoded shovels are not just competing against ever-improving hardware or models, but also against open source, which reaps benefits of these advancements as well. Retaining users will become harder as the value prop of paid-for applications dissolves. Intelligence will be commoditized, with the AI and crypto blend eventually converging into just three flavors: 1. Distributed Training • Taps into compute that otherwise would have gone unused. • Predictable and uniform workloads make it possible. 2. Distributed Inference / Model Serving • Redundant — Suitable for very specific use cases where redundancy, decentralization, and fault tolerance are desired (e.g., automating DAOs — A swarm performing decisions steps against predefined conditions to streamline governance efficiency). • Non-Redundant — In light of advancing consumer devices, unlikely to remain competitive, particularly if we attempt to adhere to current standards of decentralization. Decentralized by means of having Protocol Owned Datacenters in >20 countries is a different question though. 3. On-chain Economic Layer for Inference • Pricing and distributing (rather centralized) compute similar to blockspace, given it yields the 1-2 OOM better model performance vs personal devices. • Compensating priced-out users through a non-linear cost increase borne by other users (e.g., fee burn or vesting). • No rate limits or guesstimated pricing policies -> high availability and more efficient pricing/allocation frontier vs Web2 Efforts in the space will have to pivot from chasing the current state of progress towards chasing its future, or otherwise risk ending up as a transitory phenomenon.