I don’t feel like I have the authority to give advice, but I’ll teach mine the same fundamentals I learnt in college… physics, math, logic (if they’re STEM oriented). AI or not, these things don’t change and train your brain to be analytical.

Curious about GPU💚LPU?

👏 Congratulations to the @Oracle Cloud team on being among the first to bring up NVIDIA Vera Rubin NVL72. Together, we're delivering the infrastructure for the agentic AI era.

Jevons paradox in terms of the Opus → Fable release. 1: Fable drafts all replies. I just approve. I get my life back. 2: Sending is cheap. I send 10x more. I trade some life back for productivity. 3: Everyone hits Phase 2. I can't keep up with approving the replies. Less life than before Phase 1. Better models → more productivity → demand for even better models →

As I wrote this, I saw X go into meltdown over tokens. You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.” But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat. It's blind spending. We don’t know which spend worked. A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not. Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation. For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro. In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper. This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.

spent my 11-hour flight back from europe working on a very long report. started as a slack message but morphed into a several pages long doc. wifi was as shitty as it gets. after finally making it home i realized that the computer had forcefully restarted. opened slack: draft was gone :( hail mary: claude pls save me, no clue how but pls try it checked APFS snapshots, time machine, slack indexeddb, write-ahead logs, service worker / http caches, local storage, app logs, hibernation image... nothing. all gone but then... it realized i have alfred installed. so it checked the clipboard snapshots alfred keeps in sqlite. sad news: alfred clipboard memory gets deleted after 24h. aggressive retention policy. however! when sqlite runs DELETE, nothing gets actually deleted. it only marks pages as reusable, but it doesn't override the physical bytes. so claude decided to do a raw-scan of the db, reverse eng alfred data format, figure out the portion containing the timestamp, stitched everything back together across overflow pages... and handed me the exact final version of my report, the last one i cmd C'd all this, in a single shot ... day 200 of "what if you had an elite hacker you can ask anything to"

After AlphaGo, the skill of human Go players noticeably improved. I suspect we will see a similar pattern in math.

Q: How are job postings for software engineers rising rapidly despite AI agents automating coding? A: Because there’s far more code to manage than ever before. We’re already seeing a 14x YoY increase in GitHub commits, and it’s accelerating. AI has dramatically lowered the cost of writing code, so it’s now being used across far more businesses, applications, and use cases. We’re at the beginning of a massive productivity boom driven by the proliferation of bespoke software throughout the entire economy. Coding has been AI’s breakout use case this year. The fact that it’s increased demand for software engineers — rather than decreased it — should call into question the entire “AI will cause mass job loss” narrative.

It would take ~1.3 million of these to store the weights of a 1T parameter model at FP16…

Jensen - We need a lot more $NVDA GPUs 😂

Many people outside the tech industry are not fully grasping the latest advancements. the-decoder.com/fields-medal…

Attention is all you need. AI's is endless. Yours isn't. Give it the toil. Keep the taste.

💚

The GB300 is the best AI computer

Elon Web Services (EWS)

x.com/AltimeterCap/status/20… x.com/AltimeterCap/status/20…

Today we’re announcing $1 billion in new funds to back the bold founders shaping the next era of finance and technology. I’ve been following the flow of assets my entire career and have never seen a more dynamic time. Financial infrastructure is being rebuilt from the ground up, new assets and markets are emerging, and an agentic economy is developing as AI agents begin to transact on behalf of humans. These areas, among others, are what will define the coming years as we deploy these new funds. We’re excited for what’s ahead, and wrote about our thesis in the post below.

Jevon's Paradox?

For 50 years, software engineering ran on code rationing. Writing code was expensive, so we rationed it carefully through roadmaps, RFCs, prioritization meetings, and scope reviews. This created a role: the No Engineer. No, that won't scale. No, we don't have bandwidth. No, that's out of scope. No, we need a design doc first. The No Engineer was valuable for 50 years. Every "no" saved real money. Their judgment was the rationing system. LLMs will be the end of code rationing. Code is cheap now. And while the No Engineer is explaining why something can't be done, the Yes Engineer has already shipped three versions of it. If you're a Yes Engineer, the next decade is yours.

My job used to be writing code. Now it's marriage counseling for Codex and Claude Code.