AI & TECH

Democratized AI development outpaces public trust

Thursday, March 12, 2026 · 6 sources

This Week in Startups This Week in Startups This Week in Startups Presidio Bitcoin Jam TFTC: A Bitcoin Podcast TFTC: A Bitcoin Podcast

AI self-improvement is no longer theoretical; public tools like Karpathy's Auto Research enable non-experts to drive progress.
Crypto-incentivized networks like Bit Tensor monetize open-source contributions, creating a global, performance-based development market.
A massive adoption gap is forming: grassroots experimentation booms globally while US public sentiment remains deeply skeptical.

The ability to improve artificial intelligence is escaping the lab.

Andrej Karpathy’s Auto Research tool is a simple public loop. An AI agent rewrites its own code in five-minute cycles. Shopify CEO Tobi Lütke, not an AI researcher, used it over a weekend. His 37 experiments yielded a 19% performance gain on a small model. On This Week in Startups, Jason Calacanis argued this moves the field from a few thousand elite PhDs to potentially hundreds of thousands of practitioners.

Crypto networks are formalizing this shift into an economic model. Bit Tensor uses token emissions to subsidize 128 specialized AI subnets, paying developers globally to compete. Mark Jeffrey explained its coding assistant, Ridges, scores competitively with Claude but costs $29 per month. The project was built on roughly $10 million in chain emissions, while centralized competitors raised billions. This system monetizes open-source contribution, turning AI development into a performance-based contest.

Simultaneously, AI tools are lowering the barrier to build, not just research. On TFTC: A Bitcoin Podcast, Matt Corallo noted that tools like Claude 3.5 enable users to construct applications without deep coding knowledge. This democratization extends Bitcoin’s reach. Developers are also sorting tools into specialized roles. On Presidio Bitcoin Jam, DK described a triad: Gemini for code review, Claude for brainstorming, and OpenAI’s Codex as the relentless executor.

The global enthusiasm for these tools is not uniform. According to a poll cited by Calacanis, only 26% of Americans are pro-AI, with 46% opposed. This contrasts with places like China, where government-backed meetups for tools like OpenClaw draw massive grassroots interest. The technology is being built faster than public trust can form.

The dam has cracked. The question is who will control the flow.

Jason Calacanis, This Week in Startups:
- That 3,000 will turn into 300,000 people who understand how LLMs work and who can make meaningful progress on them.
- This is the dam cracking from the developers owning the world to everybody building the future.

Agents Big Tech Open Source

Source Intelligence

What each podcast actually said

This Week in Startups

How agents will change banking forever | E2260 • Mar 10

Andrej Karpathy released Auto Research, a tool that demonstrates AI agents can self-improve through simple, public loops.
Auto Research uses a stripped-down loop where an AI agent tries to improve its own code in 5-minute training sprints.
Shopify CEO Tobi Lutke, a business leader without an ML research background, successfully used Auto Research over a weekend.
Public tools and open-source models are democratizing the ability to understand and build on AI technology.
Jason Calacanis predicts that the pool of experts will expand from 3,000 to 300,000 people who can make meaningful progress on LLMs.
Calacanis framed this democratization as the shift from developers owning the world to everybody building the future.
Public grassroots experimentation with AI is thriving globally, especially in China with government-backed OpenClaw meetups.
The real bullish signal from public progress is what it implies for the pace of improvement inside private labs like OpenAI and xAI.

Also discussed (4)

Tobi Lutke reported achieving a 19% performance improvement on a small 800-million-parameter model using Auto Research.
Jason Calacanis notes that the era where only a few thousand elite researchers can meaningfully improve AI models is ending.
A recent poll cited by Jason Calacanis shows only 26% of Americans are pro-AI, while 46% are opposed.
A growing public capability to tinker with AI clashes with broad public skepticism in the US, setting up a complex adoption battle.

Agents Models Open Source

How agents will change banking forever | E2260 • Mar 10

Andrej Karpathy released Auto Research, a bare-bones training loop that lets a small AI model iteratively rewrite its own code in five-minute cycles.
The project proves the core mechanism of AI self-improvement is simple and already works, not just theoretical.
High-level tinkering with tools like Auto Research will massively expand the pool of people capable of meaningful AI research.
In China, local governments host incentives and massive grassroots meetups for open-source AI tools like OpenClaw.
An NBC poll showed only 26% of people in the U.S. are pro-AI, with 46% opposed, revealing a net negative public perception.
Karpathy's project is a small proof of concept, suggesting closed-door AI labs are advancing far faster.

Also discussed (4)

Shopify CEO Tobi Lütke, a self-described non-researcher, used Auto Research over a weekend to run 37 experiments.
Lütke achieved a 19% performance gain on an 800-million-parameter model using the tool.
Jason Calacanis argued this moves AI experimentation from a few thousand PhDs to potentially hundreds of thousands of curious practitioners.
A massive enthusiasm gap is emerging, with explosive grassroots AI adoption in places like China contrasting with U.S. public skepticism.

Agents Models Open Source

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 discussed (11)

Bit Tensor uses a crypto incentive layer with token emissions akin to Bitcoin mining rewards to subsidize AI development, according to guest Mark Jeffrey.
The network operates 128 specialized AI subnets that compete to produce the best models.
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.
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.
Jeffrey describes the model as Bitcoin's incentive structure applied to stranded talent instead of stranded energy.
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.

AI & Tech Markets

TFTC: A Bitcoin Podcast

#723: The Battle for the Agentic Economy with Matt Corallo • Mar 8

Also discussed (15)

Matt Corallo argued that Bitcoin thrives as a safe haven when central banks devalue their currencies through aggressive monetary policies.
Corallo stated the philosophical belief that in a world where central bankers devalue currency, Bitcoin wins.
Corallo emphasized a crucial shift: AI tools have recently evolved to enhance software development, making it accessible to non-expert programmers.
He noted that latest AI advancements allow individuals to construct meaningful applications by merely typing out ideas, bypassing extensive coding knowledge.
This democratization of technology opens pathways for Bitcoin enthusiasts to actively contribute to the development of Bitcoin-based applications.
Corallo pointed out that the surge in AI tools enables Bitcoiners to create applications easily.
The current environment favors Bitcoin as central bank currencies weaken.
With many conversations around 'agentic payments' rising, there's a real opportunity for Bitcoin to become the backbone of commercial transactions in a new digital economy.
Current payment methods struggle to accommodate the shift to agent-driven transactions, and new protocols must emerge.
Corallo sees this as a chance to start from scratch, where everyone is on equal footing without entrenched players like Visa dominating the landscape.
He called for Bitcoiners to step up and innovate as various companies begin to push out competing protocols.
The race is on to shape the future of payments, and this effort could represent a genuine collective push.
Corallo argued this collective push could allow for merchant adoption that hasn't occurred thus far.
If Bitcoin involvement in this race persists, it may emerge as a viable option for agentic spending.
Corallo warned that if Bitcoin doesn't succeed, it will be because people lack the willpower and agency to make it so.

Adoption Markets

#723: The Battle for the Agentic Economy with Matt Corallo • Mar 7

Also discussed (15)

Matt Corallo says recent AI model advancements like Claude 3.5/3.6 have dramatically lowered the barrier to software development.
He explains these AI tools now enable users to build robust frontend, web, and mobile applications without deep coding knowledge.
This marks a unique opportunity for the Bitcoin community, which thrives on experimentation and diverse builders.
Corallo says AI tools have eliminated excuses for Bitcoiners to build applications.
The other major shift is the rise of 'agentic payments' where AI agents autonomously purchase goods and services.
Corallo states this isn't a distant future and will soon comprise a non-trivial portion of consumer spending.
Existing payment rails like traditional credit card sites are not equipped for agentic payments, as they employ anti-bot measures.
Traditional systems also struggle with chargeback structures designed for humans, not autonomous agents.
Stablecoins face a similar hurdle, lacking widespread merchant integration for agent-to-merchant transactions.
For agentic payments, Corallo argues everyone is starting from zero, creating a greenfield opportunity.
Bitcoin, which often struggled to be 10x better for domestic payments, now has a unique shot in this space.
While many competing protocols from Visa, Stripe, Google, and L402 are emerging, Corallo argues the underlying payment rail is what matters.
He specifically highlights Bitcoin's Lightning Network as a payment rail that could be foundational for agentic payments.
Corallo states Bitcoiners must enter this race now to gain material merchant adoption.
He says the tools exist for building, and now willpower and a clear concept are the only requirements.

AI & Tech Adoption

Presidio Bitcoin Jam

Codex vs Claude Vibe Coding, Study Shows AI Agents Prefer Bitcoin, Kazakhstan to Add BTC? • Mar 7

Also discussed (10)

Developer DK claims OpenAI's Codex CLI has overtaken Claude Code for execution-heavy tasks, describing Codex as the relentless "builder" and Claude as the "brainstormer".
DK advocates for a three-tier AI coding workflow using Google's Gemini for code review, Anthropic's Claude for architecture exploration, and OpenAI's Codex for persistent execution.
DK previously relied on Claude Code for months but found it gets stuck in rabbit holes when exploring ideas like an artist, whereas Codex focuses like "a dog on a bone" through refactoring tasks.
Developer Callie characterized Claude as working like an "American" and Codex like a "German" in their respective approaches to software development.
DK conducted a "vibe coding" session at 70 miles per hour through the Nevada desert using Tesla's Full Self-Driving to handle highway driving while simultaneously using OpenAI's Codex CLI for software architecture.
The desert coding setup involved speaking commands to the terminal, letting the AI process for ten-minute intervals, and checking the screen periodically over a five-hour period.
Tesla's Full Self-Driving capability enables "vibe coding at 70mph," which raises safety concerns about using AI to write code while AI operates a vehicle at highway speeds.
Grok has stagnated as a competitive coding assistant over the past six months despite its integration with Tesla vehicles, according to DK.
Tesla's Grok integration allows drivers to hold the steering wheel button to speak commands and later receive code on their laptop, functioning as a car convenience rather than a serious coding contender.
DK described Codex as "like your autistic friend who just keeps going" and stated it is "insanely better than the alternatives right now at this moment."

AI & Tech Coding