Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology. Animal intelligence optimization pressure: - innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world. - thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ... - fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics. - exploration & exploitation tuning: curiosity, fun, play, world models. LLM intelligence optimization pressure: - the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on. - increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards. - increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy. - a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death. The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

Finally got around to writing down my thoughts about self-driving vans: Waymo meets Van Life medium.com/p/waymo-meets-van…

Got @browser_use working with local Deepseek on my Mac today and it's...not fast. Got it to do a few easy things but it doesn't work with a surprising number of sites. I guess feels like GPT-2 level - can sorta see how it's going to be amazing, but not there yet.

Have to say I’m blown away by @cursor_ai with Claude 3.5 Sonnet. After a couple days I can now implement features in minutes that would have taken hours before. I know I’m a little late to the party but holy cow.

Hey @JetBlue would love a quick approval of a new plane for flight 277 FLL-SFO. All the restaurants are closed and we’d all like to get home tonight!

Riding the train in Europe is incredibly civilized. Is there a place in the world that has both this kind of civilization and rapid innovation? Or are they somehow opposites?

AgFunder has amazing research on Food and Ag investment. Must read if you're in the space.

Crazy idea of the day: rich entitled people might make good entrepreneurs. They can often fund an idea, but more importantly, they usually have lots of experience bending the world to their (unreasonable) will. Of course other things are required for success, but still.

This was a wonderful celebration of an amazing person who loves what she does. Inspiring.

Pointed at founders but also a great explanation of why acceptance does not equal apathy.

My daughter at Christmas Eve dinner: stuffing is the only acceptable way to eat celery.

So excited to celebrate #tenyears of @AgFunder! Our amazing editorial crew led by @iamlbt created an 83-page dive into the last decade in agrifoodtech, showcasing our commitment to the industry. Can’t wait to see what the next decade brings! bit.ly/AgFunder10

We avoided investing in vertical farming, except for one company. @IGSfarm. Where others sought to be the grower, IGS positioned itself as the John Deere for vertical farming offering a system that created significant CAPEX and OPEX savings for its operators. This week IGS announced the GigaFarm, a huge 900,000sqft facility in partnership with the UAE to grow 2 billion plants/year, and replace 1% of the country’s fresh produce imports. This is how you do vertical farming. agfundernews.com/breaking-in…

I think @nuunhydration should revive the old @alkaseltzer commercial “Plop, plop, fizz, fizz. Oh! What a relief it is!” Would drive sales of both I bet.

Saw a bike taxi with a sign “Zero carbon emissions!” - does the driver not exhale?

Ten years ago I couldn't have imagined investing in a company solving for cow farts. Often the best ideas initially sound like the worst ideas. Great to be partnered with with our friends at @DCVCBio and CH4s new investor, Cleveland Avenue, supporting founders cracking EXISTENTIAL THREATS. agfundernews.com/ch4-global-…

Rob wrote it better than I could. So excited to be backing the amazing team at Nium to revolutionize green energy with green ammonia.