Beyond the Chatbot: The Next Trillion-Dollar Phase of AI Growth
The initial hype cycle of Generative AI is settling. As Large Language Models (LLMs) commoditize, the smart money is moving toward systems that do rather than just say. Here is the roadmap for the next decade of machine intelligence.
The next phase of AI growth moves beyond text generation into Agentic AI and Embodied Intelligence. While the first phase focused on Large Language Models (LLMs) creating content, the next cycle prioritizes Large Action Models (LAMs) that can autonomously execute complex workflows, physical robotics that understand real-world physics, and Edge AI, where processing occurs locally on devices rather than the cloud to reduce latency and energy costs.
Key Takeaways
- From Chat to Action: The market values software that executes tasks (booking flights, coding apps) over software that merely summarizes text.
- The Hardware Bottleneck: Growth is currently constrained by energy availability and inference compute, not just training data.
- Embodied AI: Robotics is the physical manifestation of AGI; Figure and Tesla are leading the convergence of vision and actuation.
- Sovereign & Edge AI: Data privacy and latency requirements are pushing small, specialized models (SLMs) onto phones and laptops.
The End of the “Magic Trick” Era
For the last two years, the world has been mesmerized by the magic trick. You type a prompt, and the machine writes a poem, generates code, or paints an image. It was impressive, accessible, and largely performative.
But enterprise CFOs are done paying for magic tricks. They want ROI. We are witnessing a hard pivot from Generative AI—which creates digital artifacts—to Agentic AI, which performs labor. The distinction is critical. A generative model writes an email; an agentic model researches the recipient, drafts the email, sends it, updates the CRM, and schedules a follow-up meeting based on the reply.
This shift from probability to utility is where the next $10 trillion in value lies.
1. Agentic AI: The Rise of Large Action Models (LAMs)
Current LLMs are passive. They wait for a user to hit “Enter.” The next growth vector is autonomy. We are moving toward systems capable of multi-step reasoning and tool use without human hand-holding.
The Loop of Agency
To understand where the technology is going, we must look at the OODA loop of AI agents:
- Observe: The AI reads the screen, accesses APIs, or parses a document.
- Orient: It understands the context and the goal (e.g., “Book the cheapest flight”).
- Decide: It formulates a plan using reasoning models (like OpenAI’s o1 or Q* concepts).
- Act: It clicks the button, executes the SQL query, or sends the JSON payload.
Figure 1: The Agentic Workflow moves beyond simple input/output to include reasoning and self-correction.
Companies like Salesforce (Agentforce) and Microsoft (Copilot Studio) are betting the farm on this. The growth metric here isn’t “users,” it’s “tasks completed.”
2. Embodied AI: Intelligence Meets Physics
Software is scaling fast, but the physical world has lagged. That is changing. We are seeing the convergence of computer vision, motor control, and language understanding. This is Embodied AI.
Until recently, robots were “dumb” machines repeating hard-coded paths. If a box moved one inch to the left, the robot failed. With Visual Language Models (VLMs), robots can now “see” and adapt.
Key Players to Watch
| Company | Focus | The “Moat” |
|---|---|---|
| Tesla (Optimus) | General Purpose Humanoid | Mass manufacturing capability & real-world training data. |
| Figure AI | Commercial Humanoid | Partnership with OpenAI for high-level reasoning. |
| Boston Dynamics | Industrial Mobility | Decades of hydraulic and electric motor control IP. |
The growth here is exponential because the labor market for physical tasks (warehousing, elder care, hazardous manufacturing) is massive and underserved.
3. The Compute & Energy Wall
You cannot discuss AI growth without discussing the plumbing. The limiting factor for the next phase isn’t code—it’s megawatts.
Training a frontier model costs hundreds of millions of dollars in energy. Running agentic workflows (inference) costs even more. This demand is triggering a renaissance in nuclear energy (SMRs) and grid modernization. Hyperscalers (Google, Azure, AWS) are effectively becoming energy companies.
The Chip Divergence: While NVIDIA dominates training, the inference market—running the models—is fragmenting. Expect massive growth in ASICs (Application-Specific Integrated Circuits) designed solely to run specific models efficiently. If you are looking for investment vectors, look at the cooling solutions and custom silicon providers.
4. The Edge: Small Language Models (SLMs)
Not everything needs to go to the cloud. In fact, for privacy and latency reasons, it shouldn’t. The next growth surge is On-Device AI.
Apple’s “Apple Intelligence” and Microsoft’s “Copilot+ PCs” are the bellwethers. By running quantized models (compressed 3B or 7B parameter models) locally on your phone’s NPU (Neural Processing Unit), you get:
- Zero Latency: No round-trip to a server farm in Virginia.
- Privacy: Your health data or financial documents never leave the device.
- Cost Reduction: Companies stop paying per-token API costs.
This “Small Model” revolution is arguably more important for mass adoption than the massive “God Models” being built in research labs.
Conclusion: The Maturity Phase
We are leaving the “Wild West” phase of AI. The next growth phase is about integration, reliability, and physical manifestation. The companies that win won’t just be the ones with the biggest chatbots; they will be the ones that build the infrastructure, the agents that execute work, and the robots that build our world.
Final thought: Don’t look at what AI can write. Look at what AI can move.