När når vi AGI?

Abstract
Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token dependencies. Furthermore, indiscriminately verifying these extended blocks wastes critical batch capacity on tokens with high rejection risks, severely degrading throughput in high-concurrency serving systems. We introduce DSpark, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification. To maintain draft quality, DSpark utilizes a semi-autoregressive architecture—coupling a parallel backbone with a lightweight sequential module—to introduce intra-block dependency modeling and mitigate suffix decay. To optimize system efficiency, DSpark employs confidence-scheduled verification, dynamically tailoring the verification length for each request based on estimated prefix survival probabilities and engine-specific throughput profiles. On offline benchmarks across diverse domains, DSpark substantially improves the accepted length over state-of-the-art autoregressive and parallel drafters. When deployed within the DeepSeek-V4 serving system under live user traffic, DSpark successfully mitigates verification waste. Compared to the established production baseline (MTP-1), DSpark accelerates per-user generation speeds by 60%–85% at matched throughput levels. More importantly, by preventing severe throughput degradation under strict interactivity constraints, it enables performance tiers that were previously unattainable, shifting the Pareto frontier of our serving system. To facilitate community progress, we open-source the DSpark checkpoints alongside DeepSpec, an algorithm-driven training repository for speculative decoding.

How Workers Will Be Replaced

Let’s start from this premise: AI can do all cognitive and physical work, at human level or better, and cheaper than humans. I can’t prove this will happen, but the goal of this post is to argue that if it happens, then everything else follows. And it’s absurd to think it can’t. Five years ago this technology barely existed: if you sent a transcript of a conversation with Claude Fable back in time to 2020 or thereabouts, nobody would believe it was real.
So, the year is 2036 (likely earlier), businesses have replaced most human workers with AI in the pursuit of profit maximization. Corporations are a small raft of human executives, floating on top of a vast ocean of AIs and robots. The AIs can do all cognitive and physical work at human level or above, and they are cheaper overall.
Imagine a pyramid. At the base you have the AIs and robots doing all economic activity. At the top you have the state, which has the monopoly on violence. The state enforces, and therefore can alter the definition of, property rights. In the middle you have this hair-thin layer of people with shares in the companies that foomed and catabolized the whole economy: the permanent overclass. They own the companies, maybe sit on the board, some might still be CEOs but it’s a purely ceremonial role since AIs do all the actual organization work.

Conclusion

Having read all this, consider this: there are people who think having equity in these companies will secure for them some kind of permanent existence in the future. They think planet-spanning minds will not only respect the property rights of primates, but will privilege some of these primates over others, because they have a piece of paper with about a kilobyte of magical primate words such as “whereas” and “notwithstanding”.
Just reason it out. Does it make sense?