starchild3001

this might appear funny to some but encouraging this kind of behaviour will come to bite us eventually. hacked models, misaligned models, free models are the precursor to several terminator scenarios.

When the overall economy grows, the demand for human labor often grows with it — it doesn’t shrink.

That has been the story of 200+ years of industrialization: new technology eliminates some jobs, but it also creates new industries, new demand, and new kinds of work.

We heard the same panic about radiologists. In 2016, Geoffrey Hinton famously suggested we should stop training radiologists because AI would outperform them. Yet in 2026, we need more radiologists, not fewer. The job is changing, not disappearing.

You even see a similar dynamic with immigration. Immigrants don’t just “take jobs”; they also create demand, start businesses, pay taxes, and expand the market. Remove them, and the economy often shrinks — meaning fewer jobs overall, not more.

TL;DR: AI is not simply “coming for your job.” Yes, the nature of work will change. We no longer employ “human calculators,” but society didn’t run out of work. We created better, more productive jobs than doing arithmetic by hand all day.

A few thoughts:

1. Chat, being 3 yr old, is a fairly mature and solved problem today. Top companies aren't even talking about it anymore! Gemma 31B does it amazingly well (for $0.4/1M token output). Practically every near-SoTA and SoTA model does simple "chat-like" QA amazingly well -- summarization, basic question answering, single- or few-step search.

2. Tasks -- or knowledge work on a computer -- are the new frontier. Computers have become competent only recently, and only for some of the tasks so far. I'd guess another 2-3 yr development cycle, after which "el cheapo" models will be virtually indistinguishable from SoTA.

As tasks are the new game in town, AI labs can still charge a premium for it. That premium has disappeared already for chat; most users cannot tell 99% correct answer from 95% correct answer; nor do they always wish for maximum accuracy.

3. What comes after Tasks? I think today's AI startups should figure that one out and solve it before everyone else.

For those who missed: Gemini coding and agentic capabilities have been lagging the sota models (Opus mostly) since Dec 2026. If you're a co-lead and your model is underperforming there has to be some consequences. I don't know as a fact if this has anything to do with Noam's departure, but work performance is never about past successes.

Would you be better off trusting something like Claude Desktop app / Claude Cowork instead? OpenClaw stories are very scary to me.

I owned a model 3 and two model Ss. I largely regard model S as the best sedan ever made (in less than 100k USD luxury price range). No, model 3 doesn't even come close. Plus you can buy a 3yr old model S "like new" at the price of a new model 3.

Tesla is committing suicide here by eliminating the best sedan ever and by committing to an idea (taxis) that mainly serve the low end market.

Message to Musk: people like to own things. Only the low end segment don't mind sharing their means of transportation. High end won't share. Biggest profits in tech are in the high end market as Apple and Samsung have repeatedly shown.

How to read this brilliant blog post by Gary Marcus?

- Replace all the occurances of "LLM" by "human";

- Replace all the occurences of "Scaling" by "additional education".

Voila! You get an article that actually makes sense, plus you'll get a better sense of where the technology is -- these models are behaving much like humans across many tasks. They aren't perfect. But they are getting better everyday, and are quite useful.

Thank you AI developers and researchers for making progress everyday! No "thank you" to people like Gary Marcus who'd be called a "perma bear" in financial parlance.

Despite consensus forecasts predicting a slowdown to 1.8% growth, the US economy appears poised for acceleration in 2026 driven by a potent combination of aggressive fiscal and monetary loosening. Treasury Secretary Scott Bessent’s optimism is underpinned by the implementation of the "One Big Beautiful Bill Act," which delivers retroactive tax cuts, alongside a rebound in government spending following a record 43-day shutdown and the potential for the Supreme Court to invalidate certain tariffs, resulting in significant corporate refunds. This fiscal stimulus coincides with the Federal Reserve’s pivot to lower interest rates—a trend likely to intensify as President Trump seeks to appoint dovish leadership to the central bank. While this synchronized stimulus supports bullish stock market projections and complements favorable global conditions like low oil prices, it carries significant risks of reigniting inflation and spiking long-term bond yields; nevertheless, the absence of immediate shocks suggests the economy has ample scope to outperform expectations.

By late 2025, Boston’s Kendall Square biotech hub faces a severe downturn marked by a "biotech winter" of plummeting venture capital and soaring lab vacancies. Caused by high interest rates, domestic policy uncertainties, and intensifying global competition, this crisis has triggered a significant talent exodus, leaving recent PhD graduates overqualified and underemployed as companies freeze hiring to cut costs. The contraction has stalled critical medical research and threatened Boston’s economic stability, though industry leaders remain cautiously optimistic that adaptation strategies—such as AI integration and renewed merger activity—could spark a recovery by 2026.

This reads like a mid-life crisis. A few rebuttals:

1. Yes, humans cause enormous harm. That’s not new, and it’s not something a single technology wave created. No amount of recycling or moral posturing changes the underlying reality that life on Earth operates under competitive, extractive pressures. Instead of fighting it, maybe try to accept it and make progress in other ways?

2. LLMs will almost certainly deliver broad, tangible benefits to ordinary people over time; just as previous waves of computing did. The Industrial Revolution was dirty, unfair, and often brutal, yet it still lifted billions out of extreme poverty in the long run. Modern computing followed the same pattern. LLMs are a mere continuation of this trend.

Concerns about attribution, compensation, and energy use are reasonable to discuss, but framing them as proof that the entire trajectory is immoral or doomed misses the larger picture. If history is any guide, the net human benefit will vastly outweigh the costs, even if the transition is messy and imperfect.

The distinction Karpathy draws between "growing animals" and "summoning ghosts" via RLVR is the mental model I didn't know I needed to explain the current state of jagged intelligence. It perfectly articulates why trust in benchmarks is collapsing; we aren't creating generally adaptive survivors, but rather over-optimizing specific pockets of the embedding space against verifiable rewards.

I’m also sold on his take on "vibe coding" leading to ephemeral software; the idea of spinning up a custom, one-off tokenizer or app just to debug a single issue, and then deleting it, feels like a real shift.