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.

- 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.

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.

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.

I agree that the economics of GenAI are currently upside down. The CapEx spend is eye-watering, and the path to profitability for the foundational model providers is still hazy. We are almost certainly in an age of inflated-expectations hype-cycle peak that will self-correct, and yes, "winter is harsh on tulips".

However, the claim that the technology itself is a failure is objectively disconnected from reality. Unlike crypto or VR (in their hype cycles), LLMs found immediate, massive product-market fit. I use K-means clustering and logistic regression every day; they aren't AGI either, but they aren't failures.

If 95% of corporate AI projects fail, it's not because the tech is broken; it's because middle management is aspiring to replace humans with a terminal-bound chatbot instead of giving workers an AI companion. The tech isn't going away, even if AI valuations might be questioned in the short term.

Many of us use AI to not write text, but re-write text. My favorite prompt: "Write this better." In other words, AI is often used to fix awkward phrasing, poor flow, bad english, bad grammar etc.

It's very unlikely that an author or reviewer purely relies on AI written text, with none of their original ideas incorporated.

As AI detectors cannot tell rewrites from AI-incepted writing, it's fair to call them BS.

Ignore...

Pair this with tight blood-pressure control (aim systolic <130 mmHg) and a healthy BMI—every incremental improvement helps. Together, LDL, BP, and BMI form the most potent triad of interventions most people can implement now and expect to see substantial benefits 20–40 years down the line.

A few references: https://mylongevityjourney.blogspot.com/2022/08/a-short-summ...

Result: 70% NVDA, 30% GBTC (Bitcoin), and 0% QQQ or TQQQ. Honestly, not a bad mix — especially for a small, high-risk slice of your portfolio.

Next, I compared TQQQ (Triple Qs) vs. QQQ using the same 10-year monthly data. The optimizer picked 100% TQQQ, which again makes sense if you’re doing this in a tax-advantaged account like a 401(k) or IRA and only with money you’re willing to take some risk on.

Then I expanded the dataset — 55 years of returns across major asset classes (S&P 500, gold, short- and long-term Treasuries, corporate bonds, real estate, etc.) — and asked for the optimal portfolio. The winner: ~85% S&P 500, 15% gold, though 75/25 gives nearly the same return with a better Sharpe ratio.

A few quick takeaways:

Gold → GLDM ETF is the best vehicle.

QQQ → QQQM or TQQQ are the best versions.

And if you’re feeling adventurous: 70% NVDA, 30% IBIT (Bitcoin) isn’t crazy.

For what it’s worth, I’ve been running 75% stocks / 25% gold for a while now, but I’m thinking of carving out ~10% of the stock portion for a more aggressive tilt: TQQQ (6%), NVDA (2%), IBIT (1%) — because why not?

2. GPT5 thinking tends to do better with i) trick questions ii) puzzles iii) queries that involve search plus citations.

3. Gemini deep research is pretty good -- somewhat long reports, but almost always quite informative with unique insights.

4. Gemini 2.5 pro is favored in side by side comparisons (LMsys) whereas trick question benchmarks slightly favor GPT5 Thinking (livebench.ai).

5. Overall, I use both, usually simulatenously in two separate tabs. Then pick and choose the better response.

If I were forced to choose one model only, that'd be GPT5 today. But the choice was Gemini 2.5 Pro when it first came out. Next week it might go back to Gemini 3.0 Pro.

Why? I once took over a massive statistics codebase with hundreds of configuration variables. That meant, in theory, upwards of 2^100 possible execution paths — a combinatorial explosion that turned testing into a nightmare. After I linearized the system, removing the exponential branching and reducing it to a straightforward flow, things became dramatically simpler. What had once taken years to stabilize, messy codebase, became easy to reason about and, in practice, guaranteed bug-free.

Some people dismissed the result as “monolithic,” which is a meaningless label if you think about it. Yes, the code did one thing and only one thing —- but it did that thing perfectly, every single time. It wasn’t pretending to be a bloated, half-tested “jack of all trades” statistics library with hidden modes and brittle edge cases.

I’m proud of writing branchless (or “monolithic” code if you prefer). To me, it’s a hallmark of programming maturity -- choosing correctness and clarity over endless configurability, complexity and hidden modes.

This new breed of AI facility is defined by an unprecedented push for density and power at every level. At the component level, AI chips like Nvidia's Blackwell GPUs consume over 1,000 watts each, leading to server racks that draw over 130 kilowatts—a 30-40x increase over traditional racks. Such extreme power density has made conventional air cooling obsolete, forcing a complete industry transition to complex liquid cooling systems. This explosive growth extends to the entire facility, with new AI campuses requiring hundreds of megawatts of power, and gigawatt-scale projects already underway. Unlike the fluctuating usage of traditional datacenters, these AI supercomputers run at near-maximum capacity 24/7, placing a constant, massive strain on energy grids.

The consequence of this technological revolution is a global "arms race" for energy, driven by the belief that achieving Artificial General Intelligence (AGI) is a multi-trillion-dollar prize. Hyperscalers are no longer just tech companies; they are becoming major energy players. The video highlights stunning examples, such as Microsoft restarting the Three Mile Island nuclear reactor and Amazon building next to another nuclear plant to secure power. With individual AI campuses planned to consume as much electricity as entire industrialized nations, the video concludes that the insatiable appetite of AI is setting it on a trajectory to become the world's single largest consumer of power, quite literally beginning to "eat the world."

Comments URL: https://news.ycombinator.com/item?id=45193682

Points: 3

# Comments: 1

1. Runaway inflation driven by unchecked bank leverage and the collapse of FED independence. Mark my words on this.

2. Cascading crises, financial and otherwise, because deregulation always ends up the same way. When government abandons its responsibility to regulate (protect), disaster follows. COVID was just one example of such systemic failure. Financial Crisis was another.

3. Structural decay of U.S. institutions -- a uniquely Trump-era risk. Once core systems are weakened, the damage can become permanent, as history shows in other countries.

4. Erosion of America’s scientific and technological edge through diminished research funding, reduced skilled immigration, and a breakdown of meritocracy.

It increasingly feels like watching the fall of Rome, with at least a 25% probability -- something even Niall Ferguson acknowledges.

Comparison with the "evils of Biden administration" feels so hollow, I feel speechless when people bring that up as an argument. It's like comparing stage 4 cancer with flu.