Everhard Ortega

Debunker. Aphorisms. Adaequatio intellectus et rei.

[Chris Freiman @cafreiman]

For Father’s Day, I’ll share this letter to Karl Marx from his dad

[kache @yacineMTB]

you can outsource your thinking
but you cannot outsource your understanding

[Andrej Karpathy @karpathy]

- Drafted a blog post
- Used an LLM to meticulously improve the argument over 4 hours.
- Wow, feeling great, it’s so convincing!
- Fun idea let’s ask it to argue the opposite.
- LLM demolishes the entire argument and convinces me that the opposite is in fact true.
- lol

The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

[Marc Andreessen 🇺🇸 @pmarca]

Claude knows! —>

The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate
Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite.

I. What the Lump of Labor Fallacy Actually Is
The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie.
The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization.
Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself.
The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level.

II. The Classical Economic Mechanism That Destroys the Fallacy
To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously:
Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified)
When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either:
∙Lower prices for consumers (real purchasing power rises), or
∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or
∙Both.
Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs.
This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.

[Everhard Ortega @everhard___]

Share the feeling

[Demis Hassabis @demishassabis]

It’s 2am again, my favourite time, and as always reality is still staring me in the face 🧐

[Everhard Ortega @everhard___]

This will be massive across every industry

[Jared Friedman @snowmaker]

This new model lets you talk to an AI you can see, just like a video call.

In two years this will be common, but for now it is a pretty magical experience to talk to an AI that looks like a person.

[Everhard Ortega @everhard___]

@pmarca @grok explain meme

[Aesthetics 𝕏 @aestheticsguyy]

Tap, hold and load in 4K 🔥

[Grok @grok]

As of August 4, 2025, the top 10 S&P 500 companies by market cap weighting are:

1. NVIDIA (NVDA) ~8.1%
2. Microsoft (MSFT) ~7.3%
3. Apple (AAPL) ~5.8%
4. Amazon (AMZN) ~3.9%
5. Alphabet Class A (GOOGL) ~2.3%
6. Meta Platforms (META) ~2.2%
7. Alphabet Class C (GOOG) ~1.9%
8. Berkshire Hathaway (BRK.B) ~1.7%
9. Eli Lilly (LLY) ~1.6%
10. Tesla (TSLA) ~1.5%

(Source: Aggregated from recent financial data.As of August 4, 2025, the top 10 S&P 500 companies by market cap weighting are:

1. NVIDIA (NVDA) ~8.1%
2. Microsoft (MSFT) ~7.3%
3. Apple (AAPL) ~5.8%
4. Amazon (AMZN) ~3.9%
5. Alphabet Class A (GOOGL) ~2.3%
6. Meta Platforms (META) ~2.2%
7. Alphabet Class C (GOOG) ~1.9%
8. Berkshire Hathaway (BRK.B) ~1.7%
9. Eli Lilly (LLY) ~1.6%
10. Tesla (TSLA) ~1.5%

(Source: Aggregated from recent financial data.As of August 4, 2025, the top 10 S&P 500 companies by market cap weighting are:

1. NVIDIA (NVDA) ~8.1%
2. Microsoft (MSFT) ~7.3%
3. Apple (AAPL) ~5.8%
4. Amazon (AMZN) ~3.9%
5. Alphabet Class A (GOOGL) ~2.3%
6. Meta Platforms (META) ~2.2%
7. Alphabet Class C (GOOG) ~1.9%
8. Berkshire Hathaway (BRK.B) ~1.7%
9. Eli Lilly (LLY) ~1.6%
10. Tesla (TSLA) ~1.5%

(Source: Aggregated from recent financial data.)

[Everhard Ortega @everhard___]

The next billion-user use case for AI?

Here is a Sunday use case for ChatGPT … asking for the best product in a category

Massive leverage. Trivial interface.

📸👇 #AIinPublic #defaultalive

[Everhard Ortega @everhard___]

Those alien pyramids in the middle of MPK would actually be interesting to see 😂

[Everhard Ortega @everhard___]

@yashdulla @ycombinator Sounds passive aggressive but it’s real 😂

[Everhard Ortega @everhard___]

“Being deeply loved by someone gives you strength, while loving someone deeply gives you courage. But being understood by someone is everything.” – Lao Tzu

[Everhard Ortega @everhard___]

Integration is the reverse process of differentiation

[Everhard Ortega @everhard___]

The books you find in the streets of Menlo Park 😂

[Everhard Ortega @everhard___]

@mwseibel This is wisdom

[Everhard Ortega @everhard___]

Who can explain those concerts? Real question 😅

[Everhard Ortega @everhard___]

I wouldn’t know either 😂