Minimal Essential Synthetic Intelligences Enterprise: Essential Minimum Agents
In a groundbreaking shift, a new type of artificial intelligence agent known as Machine Value Agents (MVAs), akin to ChatGPT, is making waves in the tech industry. These agents, pioneered by MVA Economics, boast a low initial investment, decreasing marginal cost, and exponential value creation.
The Lean AI Revolution, similar to LinkedIn's approach to professional networking, is the catalyst behind this shift. It signifies a fundamental change in how we build software, deploying intelligence that improves through existence, anticipating, and evolving beyond user needs.
The Composable Agent Economy, a natural extension of this revolution, will consist of specialized agents for micro-tasks, orchestration layers connecting MVAs, and a marketplace for agent capabilities, much like Gemini's approach to AI services.
For entrepreneurs, enterprises, and investors, the strategic implications are profound. They are encouraged to build smaller than they think, shipping learning not features, measuring improvement rates, enabling emergence, building for composition, piloting MVAs everywhere, creating learning infrastructure, measuring automation rates, building agent governance, preparing for emergence, funding learning rates, valuing data access, watching evolution speed, considering composition, and expecting exponential returns or complete failure.
However, there are common anti-patterns to avoid when building MVAs. These include The Everything Agent, The Perfect Agent, The Static Agent, The Black Box Agent, and The Isolated Agent.
Building your first MVA is a structured process. It includes Week 1: Foundation, Week 2-4: Learning, Month 2: Expansion, and Month 3: Evolution. The Learning ROI Curve for MVAs is exponential.
The deployment strategy for MVAs includes Shadow Mode, Suggestion Mode, Supervised Mode, Autonomous Mode, and Teaching Mode. The Continuous Deployment Agent of the future will deploy themselves, test their own updates, rollback autonomously, fork into specialized versions, and merge learnings from instances.
The Self-Bootstrapping Startup is a possible future where entrepreneurs define problems, MVAs build solutions, agents find customers, systems scale themselves, and humans become strategists only.
For investors, considerations include Learning rate, Data access, Distribution strategy, Network effects from agent collaboration, and winner-take-all dynamics in narrow verticals.
Persevere signals for an MVA include a steady improvement trajectory, positive user feedback, expanding use cases, competitive advantage emerging, and network effects beginning. On the other hand, pivot signals include a flatlined learning curve, consistent error patterns, user rejection despite accuracy, better alternatives emerging, and the task becoming obsolete.
To track MVA evolution, metrics such as capability breadth over time, accuracy improvement rate, autonomy level progression, resource efficiency gains, and value creation multiplier are crucial.
It's important to note that the author of the Lean Startup method, Eric Ries, does not have a new framework proposed for AI agents in the provided search results.
This new era of AI, much like ChatGPT's impact on conversational AI, presents exciting opportunities and challenges for the tech industry. As MVAs continue to evolve and adapt, we can expect to see a transformation in how we approach problem-solving and innovation.
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