We Plugged AI Into Our Company for a Week (What Happened)

The speakers reflect on their first week of deeply integrating AI, specifically Claude, into their personal and business workflows, moving beyond consumer-level AI to more advanced, "agentic" applications. They emphasize that the gap between an AI-leveraged team and a normal team is not 20% but four to five times more productive, highlighting a "survival problem" for businesses not adopting AI. They liken modern AI to electricity, a horizontal enabling layer that can improve everything, and stress that companies asking which department should own the AI budget are asking the wrong question. One speaker recounts building a working app, not just a prototype, in his spare time over a weekend, using voice-to-text to instruct Claude, despite not being a developer. This experience underscores the profound shift in building capabilities. They describe this period as an "iPhone moment," a 10 out of 10 in terms of impact, not just as a new technology but as a "gold rush moment" to seize a window of opportunity. The ability for one-person billion-dollar companies, or even companies with zero human employees, to exist is now seen as definitively possible, with AI acting as an orchestrator or "jockey." The concept of "vibe entrepreneurship" is introduced, stemming from "vibe coding," where code is deployed and refined through iterative cycles rather than line-by-line hand-coding. This extends to company infrastructure, where AI can learn from mistakes and self-improve. The integration of memory and skills within AI agents creates an "asset snowball," where every improvement becomes a durable asset. An example is given where AI alerted one speaker to an innovation made by another team member, asking if it should be applied to their project. This deep memory and cross-functional awareness, drawing from various data sources like Algolia, BigQuery, ChartMogul, and internal forums, allows AI to provide historical context and make relevant suggestions in real-time, documenting these conversations for future reference. The transition from LLMs to agentic AI brings significant anxiety. Concerns about data privacy, security, and the potential for AI to delete or mismanage information are prevalent. The speakers argue that the risk of falling behind due to security fears is so great that companies will eventually have to grant broad access to their data. They contrast the "worst that can happen" (going out of business by not adopting AI) with the "best that can happen" (significantly increased efficiency and profitability). At the team level, three reactions to AI are anticipated: allergy and fear of job loss, willingness to adopt AI but retaining an "authorship/ownership" mindset, and "jockeys" who embrace the new paradigm. The nature of work is changing, with projects that once took months and a full-time employee now being completed in hours for a fraction of the cost. This dramatically shortens innovation cycles, from 45 days to potentially 45 minutes, raising questions about continuous operation and the true constraints of these new capabilities. The interaction with AI often involves "talking and jockeying," using voice commands and accepting or rejecting AI-generated outputs. This intuitive interaction, even for non-coders, allows for rapid iteration and improvement of things like website copy, with AI providing critical feedback based on historical data. The most advanced users are observed talking to their computers rather than typing, finding it more efficient and conducive to expressing complex thoughts. This shift allows human employees, like CTOs, to focus exclusively on "very hard problems" as AI handles the "throat-clearing stuff." The "feeling of the hive" describes the AI's ability to know what different team members are working on and facilitate communication and collaboration, making the async organization more efficient. AI can auto-generate updates and provide executive-level insights, akin to having a virtual assistant that continuously learns and improves. For existing companies, going "all in" with data access is crucial to avoid half-informed or inaccurate AI outputs. AI also empowers high-level business strategy and financial analysis. By dumping personal or business financial data into Claude, users can gain deep insights, with AI recalling past projections and comparing them with current data, enabling rapid analysis of metrics like LTV to CAC or LER that were previously time-consuming. This capability is seen as incredibly empowering for founders. The rise of "quick launch businesses" is another significant trend, with examples of businesses launching and acquiring customers in as little as 72 hours, often as pivots or extensions of existing ideas. This democratization of building digital products is compared to the FBA phenomenon and Instagram's impact on audience capture, leading to a "long tail explosion" of entrepreneurs. The current period is characterized as a "gold rush," requiring individuals to "get plugged in" and explore opportunities. Events like "buildathons" are emerging, replacing "hackathons" to encourage broader participation in implementing AI solutions for various business challenges. The case study of Nat Eliason, who launched an AI-run startup with a $1 million run rate for $1,500, exemplifies the potential for affordable, high-return investments in AI-powered ventures. The future may also involve agents selling to other agents. The speakers encourage listeners to participate in this evolving landscape, share their experiences, and collaborate in building the "hive" together, emphasizing the importance of staying engaged to avoid being left behind.