pull down to refresh

We thought we were participating and building. Bullshit. We were the fuel: cheap liquidity, telemetry, and prompts. Welcome to the post-utopian era where your wallet and your prompts are the biomass the new feudal lords grind down to press into financial products and Enterprise plans. The dream of digital sovereignty has been recycled into barns segmented by ARPU, rationed “access,” and calibrated extraction. We were sold two parallel epics: the one about financial decentralization and the one about “AI for everyone.” Two stories of technological emancipation. In both cases the arc looks way too similar: romantic phase, chaotic explosion, financialization, infra‑regulatory capture, and finally the stage where you, the user, stop being the protagonist and become input. Granular liquidity in crypto. Traffic and prompts in AI. You are throughput. Not a sovereign customer—inventory optimized for yield extraction. While memecoins tied to political or media figures reach absurd market caps without cash flow or utility, protocols actually building technical foundations get displaced because distribution of attention and speculative expectation weigh more than intrinsic value. Narrative > technical merit. Same thing you’ll see in AI with “alignment” branding versus model transparency. The end-user layer is emptied of novelty. The model that scales is B2B2C: you build for institutional desks, custodians, or regulated funds; they bundle the experience and milk their retail herd. You operate as structural liquidity and compliance layer. And here comes the variable we ignored before: licenses, whitelists, KYC, permissions. Compliance went from nuisance to moat. You can fork code; you can’t fork a license or banking access. Base (the L2 leveraged by Coinbase’s regulated position) and the expansion of a stablecoin like USDC illustrate this: legal legitimacy turned into a TVL magnet. A couple cycles ago the conversation revolved around consensus, ZK proofs, L2 scaling, privacy. Today serious liquidity concentrates where there’s a narrative digestible by an investment committee: asset tokenization (RWA), structured instruments, capture of yield flows. The TPS race and TVL inflated via incentives are already historical residue. Capital efficiency matters: how many times can I rehypothecate the same dollar of collateral, how to isolate and commercialize the temporal component (principal vs yield), how to internalize MEV for the protocol instead of letting it leak invisibly. It’s not the magical glow of cryptography gaining traction, it’s financial engineering applying techniques familiar to traditional traders: implicit forward curves, basis trades, optimized carry. Artificial intelligence repeats the pattern with different raw materials. Same principle: value extraction through control of the orchestration layer. The general user feeds metrics (DAU, tokens generated, use cases), produces an implicit dataset of prompts to calibrate safety heuristics, response ranking, and monetization strategies, and serves as a statistical shock absorber to justify more hardware investment that later gets reserved for high‑margin clients. The user senses or suspects gradual degradations: less precise answers, variable latency, silent cuts in context length. Meanwhile Enterprise clients enjoy privileged lanes: separate queues, higher precision (less quantization), stabler sampling. Nobody announces it on a banner, but the economic incentive is trivial: prioritize expensive compute where the contract justifies cost; amortize the rest with compressed versions. Both sectors subject users to novelties just attractive enough to maintain throughput, but calibrated not to hand over so much value that margins crack. You are efficient cattle: you consume interface, produce signal, generate spread. Initial decentralization or openness functions as the bootstrap phase: attract critical mass under an ideal, identify monetization vectors, then close the gates gradually. Sovereignty isn’t paranoia: it’s designing a gradient where the marginal cost of extracting value from you without consent goes up. Your knowledge is your ammunition.
                                     Thanks for your time
21 sats \ 2 replies \ @optimism 18h
We need not your keys, not your coin for AI.
Not your runtime, not your slop?
reply
100 sats \ 1 reply \ @roistrdn OP 1h
Same pattern, currently saving up for a server with local models, the only way to break the cycle
reply
Yes. I do all my "production" inference either locally or on an encrypted spot aws g4dn instance for large models (which was a headache to work out and i still think i should work on tuning it to get more juice for it - its very expensive)
I do test some of the commercial models at times but honestly the only one I've used that outperforms on coding is claude 3.7 sonnet (claude 4 regresses on coding for me) and not by enough margin to not use qwen3-coder. They both get into endless logic loops when dealing with complex code beyond the trivial in which thrir bad vibes created dumb bugs - very profitable for the provider too when youre paying or are capped per token.
reply