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AI & TECH

AI Evolution: Self-Improvement and Open Source Surge

Tuesday, March 10, 2026 · from 1 podcast, 3 episodes
This Week in StartupsThis Week in StartupsThis Week in Startups
  • Andrej Karpathy's Auto Research shows AI can improve its own code in simple training loops, democratizing AI experimentation.
  • Open-source AI projects like OpenClaw gain massive traction, contrasted by negative U.S. public sentiment.
  • Bit Tensor leverages crypto incentives to decentralize AI development, challenging Silicon Valley norms.

AI isn't just for labs anymore. It's transforming in public hands.

Andrej Karpathy's Auto Research, a streamlined AI training loop, lets models improve their own code in five-minute cycles. It isn't theory, it's practice - used by Shopify CEO Tobi Lütke to achieve a 19% performance gain over a weekend. As Jason Calacanis discussed, this expands AI research participation from a select few to anyone with coding chops.

Meanwhile, open-source projects like OpenClaw are exploding. OpenClaw amassed more GitHub stars than React in record time, stealing the show from AI incumbents focused elsewhere. As Logan Allen noted, it's a classic disruption pattern driven by adaptability and grassroots enthusiasm.

Bit Tensor takes a different route, using crypto incentives to decentralize AI innovation. Mark Jeffrey explained that it turns global talent into a competitive market, rewarding developers directly for model improvements. This contrasts with Silicon Valley’s capital-heavy approach, creating comparable AI products on a fraction of the budget.

These shifts reveal a democratization of AI amidst mixed public sentiment, especially in the U.S., where many remain skeptical. Globally, however, enthusiasm surges - developers aren't waiting for traditional paths.

Andrej Karpathy, This Week in Startups:

- It's a really stripped down LLM training loop and it runs in fiveminute increments.

- So you bring your own AI model to be an agent essentially and then you give it a prompt and then what the system does is try to improve its own code over a fivem minute training period.

AgentsOpen SourceRegulationBig Tech

Source Intelligence

What each podcast actually said

This Week in Startups
This Week in Startups

How agents will change banking forever | E2260 • Mar 10

  • Andrej Karpathy's Auto-Research tool enables an AI model to iteratively test and improve its own code in five-minute cycles, demonstrating a basic mechanic of self-improvement.
  • Shopify CEO Tobi Lütke used Auto-Research to run 37 experiments over eight hours, boosting a model's performance score by 19%, despite having no machine learning research background.
  • Calacanis argues that elite AI labs are likely advancing similar self-improvement techniques at a pace twice as fast as the public tools indicate.
  • A recent NBC poll found only 26% of Americans view AI positively, with 46% opposed, indicating lagging public enthusiasm compared to technical progress.

Also from this episode:

Models (1)
  • Jason Calacanis predicts AI tool democratization will expand the pool of people capable of improving models from roughly 3,000 highly-paid PhDs to hundreds of thousands of tinkerers.
Society (1)
  • The hosts contrast US skepticism with Chinese AI enthusiasm, where OpenClaw meetups draw crowds and local governments offer adoption incentives, driven by aspirational culture and tangible career utility.
Enterprise (1)
  • The barrier for non-technical executives to directly tinker with AI training loops has collapsed, foreshadowing tension with developers who prefer keeping management away from the codebase.
AgentsModelsBig TechStartups

Wisdom of the $TAO: the future is decentralized AI • Mar 6

  • The system monetizes open-source contribution in a way traditional development cannot, according to Jeffrey.

Also from this episode:

Mining (2)
  • Bit Tensor uses a crypto incentive layer with token emissions akin to Bitcoin mining rewards to subsidize AI development, according to guest Mark Jeffrey.
  • Jeffrey describes the model as Bitcoin's incentive structure applied to stranded talent instead of stranded energy.
Startups (7)
  • The network operates 128 specialized AI subnets that compete to produce the best models.
  • Ridges costs 29 dollars per month while centralized competitors raised funding at valuations in the billions.
  • The Ridges project was built on roughly 10 million dollars in chain emissions, compared to traditional startups requiring billion-dollar valuations.
  • Developers anywhere can earn subnet tokens daily by outperforming centralized teams, turning the stranded talent problem into a market.
  • A developer in Turkey can earn subnet tokens daily by improving the model, effectively owning a slice of the product's success.
  • The market bypasses traditional startup machinery including HR, payroll, and fundraising.
  • The network pays for progress directly, turning AI development into a performance-based contest.
Coding (2)
  • Subnet 62 launched Ridges, a coding assistant that scores 73 to 88 percent on benchmark tests measuring vibe coder effectiveness, according to Jeffrey.
  • Ridges scores competitively with Claude and Cursor on performance tests.
AI & TechMarkets

Is Anthropic Making the Biggest Mistake in AI History | E2258 • Mar 5

  • OpenClaw accumulated more GitHub stars than React in 39 days, becoming the most-followed open source project in history.
  • OpenClaw, an open-source coding agent, dethroned React as the most-followed project on GitHub in just over a month.
  • AI incumbents focused on 'agent' features and co-work tools, while OpenClaw captured developer mindshare by shipping code, according to the summary.
  • OpenClaw briefly partnered with Venice AI, an uncensored chat platform founded by crypto veteran Eric Vorhees.
  • The pending Clarity Act is expected to allow banks to adopt stablecoin rails without regulatory uncertainty, according to Logan Allen.

Also from this episode:

Startups (1)
  • Logan Allen of Finn Capital described OpenClaw's rise as an outsider project capturing developer attention while established players looked elsewhere.
AI & Tech (3)
  • Eric Vorhees applied blockchain-era principles, including user sovereignty, privacy, and censorship resistance, to the AI landscape via Venice AI.
  • Eric Vorhees, from the crypto world, observed that principles like user sovereignty, privacy, free speech, and lack of censorship were absent in AI.
  • Vorhees founded Venice AI to bring user sovereignty, privacy, free speech, and censorship resistance to the AI landscape.
Culture (1)
  • Jason Calacanis described a tech adoption curve starting with criminals, moving to discreet uses like sports wagering, then to mainstream users seeking efficiency.
Stablecoins (1)
  • Calacanis noted stablecoins are entering their final adoption phase, with users like gardeners asking for USDC payments to avoid 3% fees.
VC (2)
  • Logan Allen made a pre-IPO investment in Circle based on the trajectory of stablecoin adoption for TradFi efficiency.
  • Allen's investment in Circle has doubled since its IPO, but he believes the adoption of stablecoins is still in its early stages.
AI & TechCoding

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