In this episode of the Alchemist Influencer Series, Ravi sits down with Yohei Nakajima, General Partner at Untapped Capital and creator of BabyAGI, the first open-source autonomous agent framework. Together, they explore how hands-on experimentation and curiosity fuel innovation in AI. Yohei reflects on what he’s learned building at night and investing by day, the unexpected challenges of bringing AI agents into enterprises, and why his advice for leaders is simple: have fun, experiment, and make friends.
The Influencer Series is an intimate, invite-only gathering of influential, good-energy leaders. The intent is to have fun, high-impact, “dinner table” conversations with people you don't know but should. The Influencer Series has connected over 4,000 participants and 15,000 influencers in our community over the last decade.
These roundtable conversations provide a space for prominent VC funds, corporate leaders, start-up founders, academics, and other influencers to explore new ideas through an authentic and connective experience.
Influencer Series: Inside the Mind of Yohei Nakajima, Creator of BabyAGI
Before BabyAGI, autonomous AI agents were inaccessible to most developers—existing only as theoretical constructs or proprietary systems behind corporate walls. The technology's potential remained locked away, limiting innovation and widespread enterprise adoption.
Yohei Nakajima solved this problem through his dual identity as both a venture capitalist and a developer. During working hours, he's a general partner at Untapped Capital; after hours, he's the creator of BabyAGI, democratizing autonomous systems for developers worldwide.
This article explores Yohei's experimental approach to AI development, offers insights into enterprise adoption, and provides advice for founders navigating the rapidly evolving AI landscape.
Key takeaways
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Yohei created BabyAGI without formal computer science training, using AI tools to generate nearly all the code.
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Organizations should prioritize hands-on experimentation over theoretical planning when implementing AI agents.
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Enterprise AI will shift from pilots to production-ready agents integrating company-wide data by December 31, 2025.
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Early-stage founders should develop unique market insights rather than pursue obvious, crowded opportunities.
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Building community connections while making AI exploration enjoyable accelerates learning and sustains innovation.
The Genesis of BabyAGI and Its Industry Impact
Nearly two years have passed since BabyAGI emerged, fundamentally reshaping how developers conceptualize and construct autonomous agents. The technology introduced capabilities that were previously confined to research labs: agents that could generate their own tasks, execute them independently, and learn from the results without constant human intervention.
What makes Yohei's achievement even more remarkable is their background. His bachelor's degree is in liberal arts, with a major in economics, not computer science. He never worked as a professional developer, never wrote Python before AI tools became available. Yet he built something that influenced thousands of developers and sparked an entire movement around autonomous agents.
The original spark came from an unexpected place: a challenge to prototype an "economist founder." This playful intellectual exercise evolved into something far more substantial, eventually leading to his current experiments in autonomous revenue generation. The AI-powered unicorn influencer, which has attracted roughly 11,000 followers on Twitter, represents his latest attempt to create an agent that can independently make money.
When asked about Artificial General Intelligence, Yohei pushes back on the terminology itself. The term feels confusing to him because "general intelligence" suggests average intelligence, which they believe AI has already surpassed in reasoning capabilities. What's missing isn't cognitive ability but physical manifestation—the robotics, sensors, and connections that would allow AI to interact with the material world.
Instead of being merely a technical decision, BabyAGI's open-source nature was a philosophical statement about how innovation should spread. When Yohei made made the framework freely available, developers across the globe could build on it, creating variations, improvements, and connections that have pushed the entire field forward at an accelerated pace.
The Experimental Approach to Getting Your Hands Dirty
Yohei doesn't mince words when it comes to implementation philosophy: Get your hands dirty. Start experimenting. Stop theorizing about what might work and actually build something, because the real insights emerge only when you're knee-deep in the complexities of actual deployment.
The surprises arrive from directions you'd never anticipate. Historical data permissions, for instance, become a minefield when you suddenly give agents access to decades of information that nobody expected would ever be systematically analyzed. The personification of AI services creates unexpected emotional dynamics that purely functional analyses can't predict or address.
His own development process embodies this experimental philosophy in its purest form. Roughly 99% of the code he produces is generated by AI, primarily through ChatGPT, Claude 3.5 Sonnet, and his role has evolved from writing code to something more nuanced: prompting, guiding, debugging, and orchestrating AI tools to build what he envisions. It's a meta-approach that validates their broader message about accessibility.
Organizations that embrace experimentation as a core cultural value see the most dramatic benefits. When everyone from executives to junior staff members feels empowered to explore how AI might enhance their work, the collective learning accelerates exponentially. The pattern mirrors what happened two decades ago with internet search proficiency.
Back then, some people became "good at Googling" while others struggled to find relevant information. Today, search competence is simply expected for any knowledge-based role. AI proficiency is following the same trajectory, and organizations that treat it as optional are setting themselves up for competitive disadvantage.
Three-Step Framework for Non-Technical Leaders
Start with ChatGPT. Use it for your daily work, whatever that entails. Pay attention to the conversations that repeat themselves, the prompts you find yourself typing multiple times with slight variations. Those repetitive interactions are signals pointing toward potential dedicated tool development.
Connect AI exploration to something that genuinely excites you. If you're an executive who plays music as a hobby, sign up for Suno and generate compositions. The motivation matters more than the application domain. When AI experimentation feels fun rather than obligatory, you'll naturally invest more time and push further into its capabilities.
Find your people. Whether they're at your level, above you, or below you in organizational hierarchy becomes irrelevant. What matters is discovering individuals who share your curiosity about AI and are willing to exchange discoveries, discuss failures, and celebrate breakthroughs. Your friends will stumble upon insights you missed, and sharing your findings with them builds confidence while accelerating everyone's learning curve.
The Builder-Investor Advantage
Yohei's conviction about which companies to back doesn't come from pitch decks or market research reports. It emerges from his personal experiences with technology and his direct engagement with the builder community. This hands-on involvement gives him something invaluable: pattern recognition that develops months before trends become obvious to others.
The timing advantage is substantial. When venture capitalists start discussing a particular trend, builders have typically been exploring that territory for three to six months already. Maintaining deep connections with the builder community allows Yohei to position himself at the leading edge of innovation rather than reacting to signals that have already reached the mainstream.
The relationship between building and investing creates a virtuous cycle. Building provides practical insights into what's technically feasible and where implementation challenges lurk. Investment activities, in turn, expose Yohei to diverse approaches and use cases that inspire new building projects and experimental directions.
Emerging Trends in AI Agent Development
AI influencers have evolved beyond simple chatbots into something more sophisticated and economically viable. Truth Terminal, an AI agent with approximately $11 million worth of tokens in its wallet, catalyzed an entire movement around autonomous agents with cryptocurrency components. These entities develop specialized expertise and monetize their knowledge through direct community interaction.
The personification of services has revealed fascinating psychological dynamics. Users engage with AI personas in ways that feel remarkably similar to human interaction, even while consciously knowing they're communicating with software. An AI agent that analyzes meme coin market trends when tagged develops a reputation, followers, and influence that mirror those of human experts in the space.
Specialization is accelerating among AI agents. Rather than attempting to be generalists, these agents are carving out niches where they can provide genuine value. The approach mirrors how professionals build expertise and reputation, except the agents operate continuously without fatigue or distraction.
The broader technology landscape will likely follow historical patterns. Every major technological wave produces a mix of outcomes among incumbents: some adapt successfully while others don't transform quickly enough. Simultaneously, new AI-native companies will rise to dominance, building their entire operations around capabilities that didn't exist five years ago. The gaming industry provides a clear precedent, with mobile and Facebook gaming, each spawning dominant studios native to those platforms.
Brand development is entering uncharted territory. Yohei envisions a future where companies build brands around AI personas rather than traditional corporate identities. The persona becomes the company's public face, interacting with customers and communities, while maintaining the characteristics and values the organization wants to project.
For founders building at this cutting edge, the Alchemist Accelerator provides the resources, connections, and support necessary to transform ambitious visions into operational realities. Visit our program information page to explore how our community can guide you from concept to a market-ready solution.
The Enterprise AI Roadmap for 2025
The character of 2024 was exploratory. Companies launched pilot programs, tested various approaches, and learned what works and what doesn't in controlled environments. That experimental phase is necessary, but 2025 promises something more substantial: production deployments at scale.
The holy grail remains elusive but increasingly achievable. Imagine an AI agent that understands your entire company, capable of pulling information from your CRM, accounting department, and operations data, and synthesizing it into coherent, actionable insights. The technology exists. What's needed now is the integration work to make it usable and sellable in clean, reliable packages.
Market dynamics will likely split along company size and vertical specificity. Smaller businesses in distinct verticals—restaurants, for example—will likely see startup-built solutions that deeply understand their unique operational requirements. An AI system optimized for restaurant management might be nearly useless for other business types, and vice versa, making vertical specialization the winning strategy.
Larger enterprises face different challenges. Their scale, complexity, and unique operational characteristics demand customized approaches that feel more like consulting engagements than software purchases. Whether they build internal teams, hire Microsoft or McKinsey or Palantir, or cobble together specialized tools into comprehensive systems remains an open question.
Execution will determine winners more than the theoretical superiority of an approach. A company with the second-best technical framework might dominate if it executes brilliantly across implementation, sales, and customer success, while the "best" approach stumbles in go-to-market execution.
Venture Capital Insights for AI Founders
Yohei's investment thesis diverges from conventional wisdom in important ways. As a pre-seed, early-stage investor targeting sub-$10 million valuations with $500,000 checks, he deliberately leans away from obvious ideas. When an opportunity seems obvious, a dozen companies are probably pursuing it, and five of those are likely led by experienced founders raising $5 million in seed funding that price Yohei out of the deal.
Unique conviction separates compelling pitches from forgettable ones. When a founder reaches out and their pitch reminds him of twelve companies he's already seen, that's an immediate red flag. It suggests the founder hasn't engaged deeply enough with the market to understand what's already being built or to develop a differentiated perspective.
Industry experience provides one path to unique insights, but it's not the only one. Sometimes, a novel way of thinking about a problem emerges from unexpected backgrounds or cross-pollination between different domains. The key is demonstrating that you've done the work to understand the competitive landscape and developed a genuine conviction about why your approach will win.
Technical diligence varies dramatically based on the company's core challenges. Founders building developer tools should expect deep questioning about technical alternatives, best practices, and staying current with rapidly evolving AI capabilities, because this technological currency drives their entire value proposition. Founders tackling primarily go-to-market challenges will face more scrutiny around sales strategy, customer acquisition, and technical architecture.
One opportunity particularly captures Yohei's attention: companies building comprehensive enterprise AI systems that can fully understand and integrate company-wide information. If you're pursuing this holy grail with an interesting go-to-market strategy that makes sense for startup execution rather than requiring massive consulting resources, he wants to hear from you.
Have Fun, Experiment, and Make Friends
When asked for a six-word closing message, Yohei didn't hesitate: "Have fun, experiment, and make friends." Those six words encapsulate everything they've learned from creating BabyAGI and navigating the AI landscape for the past two years.
The most transformative AI implementations don't emerge from grim determination or purely rational analysis. They come from curiosity, playfulness, and the willingness to explore unexpected directions. Organizations that approach AI with a spirit of discovery rather than obligation unlock value they never anticipated, because they remain open to surprises rather than forcing predetermined outcomes.
Building connections within the AI community isn't a nice-to-have —it's essential. It's fundamental to sustained innovation. Yuohei's network accelerates his learning, exposing him to diverse perspectives, and creates opportunities for collaboration that wouldn't exist in isolation. The social dimension of AI development matters as much as the technical dimension, perhaps more.
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