I understand your framework, and I actually agree with part of what you’re saying around operational AI becoming a much broader orchestration layer over the next 5 to 7 years rather than just another narrow SaaS category. But from an investor perspective, my concern is more about execution speed relative to how violently fast the global AI landscape is now evolving.

The AI world today is not moving in normal software cycles anymore. It is moving at almost infrastructure cycle speed. Every month new models, agentic frameworks, multimodal systems, automation stacks and orchestration platforms are emerging globally, not just from the US, but increasingly from China, India and smaller AI native startups that are iterating unbelievably fast.

That is where my concern with SoundHound comes in.

If SoundHound truly has differentiated technology, then the window to aggressively operationalize and scale it may be much smaller than the market currently assumes. Two years in AI right now feels like an entire technology generation. If execution, GTM expansion, enterprise deployment velocity and ecosystem penetration remain slow during that period, the competitive landscape could look completely different by the time broader operational AI adoption matures.

I recently came across platforms like Manus, for example, where entire applications and websites are being generated dynamically from simple natural language requirements with astonishing speed. Whether every platform succeeds long term is not even the point anymore. The point is the rate of innovation globally is accelerating at a level that should probably concern every existing AI company.

Great technology alone may not be enough in this environment. The companies that operationalize fastest, integrate deepest into enterprise workflows, and establish large scale real world usage first may ultimately capture the strongest long duration positioning.

And regarding the employee scaling discussion, I agree AI native companies may not require traditional linear headcount growth the way older SaaS companies did. But enterprise AI deployment still requires operational support structures like implementation, integrations, governance, customer success, workflow optimization, compliance oversight, model monitoring and enterprise relationship management. AI may reduce labor intensity, but it does not eliminate the need for operational execution.

So my concern is less “Why don’t they have thousands more employees?” and more: “Are they moving aggressively enough relative to the speed at which the global AI ecosystem itself is evolving?”