Arnold Dai

Rants about policy
UVA 2020

[Andrew Lokenauth @FluentInFinance]

Boomers: I bought my first house at 25 for $50,000.

Millennials: I’m 30 with 3 roommates & 2 jobs.

Gen Z:

[GL @gldivittorio]

Bar

[kevconrad 🚀🐘 @kevconrad2]

Unbelievable. We have personal iPhone footage from an astronaut going around the far side of the Moon capturing the Earth setting behind it.

[Haydn, 🇵🇸 @bilbosfootcomb]

Its so funny that the two men I’ve seen best diagnose the principal societal malady of the 21st century are the pope and hideo kojima

[Aakash Gupta @aakashgupta]

A human consumes about 2,000 calories per day. Over 20 years, that’s roughly 17,000 kWh of total food energy. Training GPT-4 consumed an estimated 50 GWh of electricity. That’s 3,000 humans worth of “training energy” for a single model run.

And GPT-4 is already dead. OpenAI retired GPT-4o from ChatGPT on February 13th. The model that took 50 GWh to train got less than two years of flagship status before replacement. The human you spent 17,000 kWh “training” for 20 years produces economic output for the next 40 to 60 years. The amortization window on GPT-4 was shorter than a car lease.

Now look at what replaced it. GPT-5.2, released December 2025, is OpenAI’s current default. The GPT-5 series consumes an estimated 18 Wh per average query according to the University of Rhode Island’s AI Lab, up to 40 Wh for extended reasoning. That’s 8.6 times more electricity per response than GPT-4. With 2.5 billion queries hitting ChatGPT daily and GPT-5.2 now the default model, the inference math gets staggering fast. Even at a blended average well below 18 Wh, you’re looking at daily electricity consumption that could power over a million American households.

This is what Altman is actually doing. OpenAI hit $13 billion in annual recurring revenue but still isn’t profitable. They need you to think of AI energy consumption as natural and inevitable, the same way you think about feeding a child, because the alternative framing is that they’re burning through enough electricity to rival small countries while racing to build 1-gigawatt Stargate data centers. The food analogy makes the energy costs feel biological and unavoidable instead of what they are: an engineering and business choice that scales with every model generation.

The comparison sounds clever at a fireside chat in India. It falls apart the second you do the arithmetic.

[duck @ExtremeBlitz__]

They shouldn’t have done it

[L. David Fairchild @David_Fairchild]

He's not just defending AI energy use. He is smuggling in a whole anthropology where humans are basically inefficient meat computers that you have to pour food and years into before they become useful. And once you accept that, the next move is obvious. If people are just costly biological training runs, then burning mountains of electricity to build synthetic intelligence starts to feel not only equal, but superior, even if it negatively impacts actual humans.

That is the dystopian. It makes human development sound like a bug in the system, and it makes sacrificing human and creational flourishing for more computational power sound logical. To him, the grid gets strained, prices go up, ecosystems get hit, but hey, humans eat too, so what's the difference?

The difference is that humans aren't an inefficient line item. They're the point. If your worldview can look at a child growing into an adult and describe it as energy spent to train intelligence, you haven't said something profound. You've revealed a horrifically rotten worldview.

[Aakash Gupta @aakashgupta]

The math on this project should mass-humble every AI lab on the planet.

1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output.

The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice.

Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet.

And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.”

This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one.

We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that.

The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

[Boring_Business @BoringBiz_]

This was an eye opener from Jensen Huang

When asked whether he would rather relive his 20s or be 20 years old today, this is what he had to say:

"I thought our 20s were happier than these 20s. I think everyone deserves some time to be oblivious, and not wear all of the world's problems on their shoulders on Day 1

We are raising a generation that is very cynical and too informed

They are cynical, not because they are inherently cynical. They are cynical because they see so much stuff. It is too much stuff

You have to build up some internal reserve of optimism. You have to build up some reserve of goodness."

[ℏεsam @Hesamation]

these words from Jensen Huang hit hard.

"Nvidia wouldn't be possible today if I wasn't ignorant to the fact that it's impossible to build it. you can't convince optimistic people that they can't make it better. how's that a bad thing?"

[one dozen bats at a keyboard @PanasonicDX4500]

“we may or may not invade you every four years depending on how worked up ~20,000 swing voters in Waukesha County, Wisconsin feel about trans kids playing volleyball” isn’t really a good sales pitch for maintaining international allies.

[sophie @netcapgirl]

ok claude take this unstructured data and turn it into dashboards that will give executives deeper insight into critical business functions. make no mistakes

[peepeepoopoo @DeepDishEnjoyer]

LISTEN YOU SILICON VALLEY RIGHT WING FUCKS. IF YOU DONT GET YOUR ORANGE LAPDOG TO BACK THE FUCK OFF OUR FRIEND JEROME WE FINANCIERS ARE GONNA SEND TREASURY YIELDS SO HIGH 1983 IS GOING TO LOOK LIKE A HAPPY DAYDREAM IN COMPARISON. THANK YOU FOR YOUR ATTENTION TO THIS MATTER

[Nicholas Kristof @NickKristof]

Several contradictory things are simultaneously true:
1. Maduro was an oppressive, unpopular and illegitimate ruler, disastrous for his country and the region.
2. Using the US to remove him may have been equally illegitimate.
3. Venezuela would be better off with a new government under the opposition's Edmundo Gonzalez (who probably won the presidential election but is now exiled).
4. It's not clear that Maduro will be replaced by Gonzalez. Maduro's VP is still apparently in power, as are other regime figures and the Cubans who back them.
5. As we've seen in Iraq and Libya, it can be easier to topple a leader than to establish a new government; sometimes you get a worse leader, or Somalia-style chaos.

[_its_not_real_ @_its_not_real_]

Absolutely wild how dead the open internet is. Everything is on TikTok, Instagram, Discord, or Facebook groups now. None of those are searchable (if Google even worked). Reddit wikis and discussion are ghost towns. Non-academic research is impossible.

[Jaya Gupta @JayaGup10]

Before LLMs, Palantir was competing with Snowflake and Databricks.

Post-LLMs, they do not believe they have any competitors. Why? Snowflake/Databricks optimized for SQL and query throughput: get raw data into tables, run fast analytical reads, ship dashboards and models on top. Palantir made a different bet: an ontology, a world model where data is represented the way humans actually reason about it (objects, relationships, properties; nouns/verbs/adjectives). Back then, that was built for government analysts trying to make sense of messy, interdependent systems.

Then LLMs arrived and the ontology suddenly looked like the perfect interface because models don’t want a trillion rows. They want a structured, language-shaped substrate: named entities, typed relationships, constraints, and “what interacts with what”, something you can linearize into a coherent prompt, traverse, and act on.

The bigger implication for decision traces is that the “context graph” problem we wrote about has multiple architectural solutions:

Platform-first (example: Palantir): prescribe the unified world model upfront. Pay the integration + ontology + embedded-team tax (months of use case discovery / workflow decomposition / “process mining”), and in return you get a substrate that can connect data to decisions because everything now lives inside the same model for an extremely absurd price.

Workflow-first (decision traces): don’t start by rebuilding the world. Instrument the moments where the world changes. Capture decision receipts at commit surfaces: inputs referenced, policy/constraints, exception path, approvals, action taken, outcome. Over time (not day 1), that write-time provenance becomes its own world model, learned from trajectories rather than imposed upfront (there will be many different methods here)

And importantly: this is still an ontology approach, just a different kind. Palantir prescribes the ontology first. Our take is that startups can learn it bottom-up from traces. You start by capturing what people actually do at the decision surface: what evidence is referenced, which approvals happen, what exceptions recur, what actions are taken, what outcomes follow and over time, infer the minimal set of entities + relations that explain those trajectories.

The missing piece is decision traces: without them, you have state, but not the legible “why”!! Cc @akoratana

[Zephyr @zephyr_z9]

1 gram of RAM die is more expensive than 1 gram of 16 karat gold rn