The 2026 World Humanoid Robot Games have begun. 666 teams from around the world are competing with more than 2,000 humanoid robots https://t.co/8GwBuNnNn1 https://t.co/KMF1kkBiI6
Since the launch of ChatGPT, the Philippines offshore industry (8% of country’s GDP) has actually grown much bigger.
Employment in IT and business outsourcing is up by +20% to 1.9m workers and industry revenue is up +30% to $42B.
“AI is helping offshore workers in the Philippines do new, more complex jobs. They are picking up work training AI models or supervising AI agents. Hospitals in America are increasingly outsourcing the checking of insurance eligibility and filing of medical records to Filipinos packing AI tools. Mr Gallimore says more “high-value” work, such as accountancy or engineering, is going offshore in part because “AI is a leveller. You can now teach someone complex stuff quickly” and still get it done more cheaply than in America or Europe.”
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More here: https://t.co/WTVAUuuxBd
Possible scenarios (ranked in order):
1/ Your country makes the best cheapest products. You export, and your citizens also buy the best.
2/ Your country doesn’t make the best/cheapest; but your consumers enjoy them (Brazil below).
3/ Your country doesn’t make the cheapest/best AND your country blocks access to your market. So you pay more for worst products, and you don’t export. Plus your local companies become less globally competitive as they are protected.
#3 is the worst. It’s what Europe did when America thrived. And it’s what many in Washington recommend today.
One danger of working just hard enough to get by is that you tend not to leave much margin for error when you do that. It's the effort equivalent of doing things at the last moment.
"So You Want to be a VC"
Im enjoying this week in Boston visiting students promoting my new book - Runnin Down a Dream. Not surprisingly, many ask me about trying to break into venture capital. I wrote a letter answering this question 15 years ago. I would send it out when people inquired. I'm making it public for the first time - with zero modifications.
1) I think it holds up well
2) make sure and read my new book also
3) I probably can't help with followups (as suggested in the letter)
Hope you find it useful. Good luck!
Tried a ripe durian today. I have such a bad sense of smell that it didn't bother me at all. The rest of the family was revolted though. None of them would try it, but Jessica let me kiss her afterward. She said it was "like kissing a rubbish bin".
MIT nuclear scientist Charles Forsberg on visiting China’s molten-salt reactor program:
“The thing that struck me the first time we went to China, in particular, is they assigned an awful lot of people to the problem…And if you assign several hundred engineers to the problem you will learn very, very rapidly.”
All the analysts forever writing about OpenAI vs Anthropic vs Google are missing the real story that already happened.
80% of startups pitching Andreessen Horowitz are running on Chinese open-source models. Not OpenAI. Not Anthropic. Chinese models like DeepSeek that cost 214x less per token.
The math here breaks everything. DeepSeek trained its model for $5 million. OpenAI spent $500 million per six-month training cycle for GPT-5. That gap translates directly to API pricing where startups pay $0.14 per million tokens versus $30 for GPT-4.
For a startup burning through 100 million tokens monthly, that’s $1,400 versus $300,000. The difference between 18 months of runway and 3 months.
This tells you the real constraint in AI was never capability. Chinese models are matching GPT-4 on coding benchmarks while costing 2% as much. The constraint was always burn rate, and China solved it first by optimizing for efficiency instead of chasing AGI.
The second-order effect gets interesting. When your infrastructure costs drop 98%, you can actually afford to fine-tune models for your specific use case. American startups paying OpenAI’s API rates are stuck with generic models. Chinese open-source users are building specialized variants.
Silicon Valley thought the moat was model quality. Turns out the moat was cost structure, and they built it backwards. When a16z partner Anjney Midha says “it’s really China’s game right now” in open-source, he’s not talking about benchmarks. He’s talking about who controls the default foundation layer.
Now look at where this goes. American AI labs are optimizing for AGI and superintelligence. Raising billions to chase the theoretical ceiling. China optimized for distribution and adoption. Making AI cheap enough to become infrastructure.
All 16 top-ranked open-source models are Chinese. DeepSeek, Qwen, Yi. The models actually being deployed at scale. While OpenAI charges premium rates for exclusive access, Chinese labs are flooding the zone with free alternatives that work.
The third-order cascade is what changes everything. Every startup that survives the next funding winter will have optimized around Chinese open-source as default infrastructure. Not as a China strategy. As a survival strategy.
That 80% number at a16z only goes one direction. When you’re a seed-stage founder choosing between 18 months of runway or 3 months, economics beats nationalism every time.
America is still competing to build the best model. China already won the race to build the one everyone uses.
A group of Western VCs goes to China, not to invest but to see what the competition is like. They realize that China is too far ahead on clean tech and decide the only way forward is to work with Chinese companies.
One VC describing a visit to Chinese battery giant CATL: