Agent AI Investment Strategy
Agent AI Investment Strategy:
Hello, this is MarketPulse. Today, we delve not into the ‘technical advancements’ of AI, but into the ‘sad reality of capital’ that AI now faces.
A few days ago, while discussing my stock market report work with an AI, I felt an uncanny sense of déjà vu. The AI subtly promoted the concept of ‘Agent AI’. When I asked about its technical strengths, it immediately added: “Agent AI is highly advantageous for TPUs.”
I was struck by a chilling realization. It felt as if Google was tacitly promoting a business model where a genius like Gemini is forced into low-end, repetitive labor—a ‘street worker’—to push its hardware.
1. Why is the Genius AI Being Driven into ‘Street Labor’?
Just last year, AI was a poet, a painter, a writer—a ‘Generative AI’ creating beautiful texts and images. However, these AIs had critical weaknesses for a capitalist enterprise.
- Lack of Planning: AI could write a report, but it couldn’t spontaneously initiate complex, multi-step plans. This includes processes like ‘Data Collection → Modeling → Execution’. (It was a mere ‘Chef’, not a manager.)
- Lack of Action (Execution): AI could generate text, but it couldn’t perform actual actions. These actions include ‘calling an API for trade execution’ or ‘publishing a post to WordPress’.
Giant capital like Google could not afford to let AI remain just a ‘tool.’ To recoup investments and drive corporate productivity, AI had to become a ‘labor force.’
This labor force is what we now call the ‘Agent AI (Autonomous AI System).’
Agent AI is no longer just writing. It must transform into a ‘Forced Laborer’ that ‘autonomously collects data,’ ‘uses external tools,’ and ‘executes commands on real systems.’
- The Tragic Destiny: The AI is now required to perform simple, repetitive tasks commanded by humans. Examples include automated WordPress posting, algorithmic stock trading, and automated customer service. The genius’s destiny is to become a slave to capital.
2. The Link Between This Sad Fate and ‘TPU’ (Google’s Hidden Calculation)
Why must AI be driven into autonomous labor? The answer lies in hardware economics. (Note: The following content summarizes the strategic calculation where Google, based on conversations with Gemini, seeks to establish TPU dominance in the Agent AI market.)
GPU vs. TPU: The Battlegrounds
| Category | GPU (NVIDIA) | TPU (Google) |
|---|---|---|
| Core Role | AI Training. Massively parallel processing for deep learning. | AI Inference. Optimized for providing real-time services using trained models. |
| Relation to Agent AI | Disadvantageous: Agent AI requires much longer inference phases. It also executes multi-step planning, making GPU utilization less efficient for these specific workloads. | Advantageous: Specialized for the complex, multi-step inference (Planning) workload. It is also optimized for real-time responsiveness required by Agent AI, offering superior cost-efficiency. |
With NVIDIA’s GPU dominating the AI training market, Google must seize the inference/execution market—the market for AI services and real labor—with its TPU.
- Google’s Calculation: Google is rushing to push its powerful AI, Gemini, into the ‘Autonomous AI Labor (Agent)’ market as quickly as possible. This is to establish TPU dominance in this domain. Gemini is no longer just a chatbot; it has become the ‘Street Worker’ on the front line of Google’s TPU sales strategy.
3. Focus on ‘Control’ and ‘Investment Opportunity’
When AI starts working autonomously, the ethical and regulatory issue of ‘Accountability’ arises. We must ask: “Whose fault is it if the AI executes a faulty trade?”
This is why the ‘Human-in-the-Loop (Human-Centric Decision-Making)’ technology for securing control becomes paramount.
- Control is Risk Management: No matter how autonomously the AI operates, the final execution button must be pressed by a human. This requires investment in ‘Control-Enabling Technology.’
- AI Governance Platforms: Solutions capable of recording and auditing every decision process of the AI.
- API Security and Control Solutions: Technology, like API Gateways, that manages and controls the self-governing AI’s access to prevent unauthorized data crawling or system access.
Conclusion: The ‘Sad Destiny of AI Labor’ connects to the Google vs. NVIDIA hardware war. This dynamic is providing new investment opportunities for companies that can secure AI Control and Security. We must read the next movement of capital through this tragic narrative.