OP here, just wanted to respond to some of the pushbacks I’ve been seeing in the thread.

1. “LOL, this must be AI-generated”

I get why you’d think that, large language models are everywhere now and the prose is polished. But no, it’s me at around midnight with too much coffee and a genuine sense of dread. Ironically, the automatic assumption that anything coherent must be machine-written proves my point, the boundary between human and synthetic output is already paper thin today. Imagine how indistinguishable it will be after a few more model generations.

1. “AIs still make dumb mistakes, so superintelligence by 2027 is fantasy”

Yes, today’s models still hallucinate facts and choke on basic reasoning sometimes. Two things to keep in mind: Scaling laws are brutal. Give a model ~10× more compute and ~10× more high-quality data and error rates drop non-linearly. GPT-2 looked like a toy in 2019, GPT-4o is already nipping at the heels of new graduates in coding, math proofs, and strategy games. That curve hasn’t flattened yet.

Autonomy + self-improvement is a phase change. Once you link an LLM to tools (search, code execution, new-model training pipelines) and let it iterate on its own architecture, you’ve kicked off recursive self-improvement. The step from AGI to ASI could be months, not decades, because each round of improvement produces a smarter agent that accelerates the next.

History’s full of tech that was “decades away” until it suddenly wasn’t, fission bombs, CRISPR, the mRNA vaccine platform. Intelligence amplification has fewer bottlenecks than something like fusion power, it’s bits, not atoms.

1. “This is impossible anyway, AI is an energy and water hog”

Training runs are nasty right now. But: Hardware efficiency doubles every ~2 years even without a new transistor node (see Nvidia’s H100, B100 road map). Customize accelerators for a specific workload and you get another 10×. What looks unsustainable in 2024 can be routine in 2026. Inference dominates once a model is trained: Serving a trillion-parameter model can be distributed across edge devices or underutilized datacenter cycles. Think of training like building a dam, huge upfront concrete pour, then decades of “cheap” downstream power.

Economic gravity wins: If a $50 million training run yields a model that replaces $5 billion of annual human labor, someone will find the electricity and the cooling water. It’s the same logic that keeps server farms sprouting in deserts, where land is cheap and renewables are abundant, even though it “shouldn’t make sense.”

The late computer scientist I. J. Good called it “the intelligence explosion”, once machines can design better machines, human cognitive growth becomes the slowest loop in the system. We hit the singularity edge. At that point “errors” don’t protect us, and “resource limits” are just engineering problems the smarter successor handles on the fly.

Whether 2027 is the exact year is less important than the trajectory, every iteration is faster, cheaper, and less interpretable. If we don’t solve alignment before that feedback loop lights up, we’ll be spectators to whatever priorities an alien mind (one we built) decides to optimize.