AI capabilities are becoming ubiquitous and cheap. Like computing power before it, AI is following its own Moore's law. The question is no longer whether AI can do something. It's who controls how AI works for you.
We're watching the same curve that defined the PC revolution play out again. Models that cost millions to train last year are open-source today. Capabilities that were exclusive to the biggest labs six months ago now run on a laptop.
This isn't a bubble. It's a fundamental shift in the cost structure of intelligence. Every month, the baseline gets higher and the price gets lower. The moat isn't having AI. Everyone has AI. The moat is knowing what to do with it.
[1] Microsoft WorkLab [2] Substack
There's a fundamental tension in the AI industry. On one side, open-source models and falling costs should be democratizing AI, giving every organization the power to run, fine-tune, and govern their own models.
On the other side, most companies are more dependent than ever on a handful of centralized API providers. They've traded one kind of vendor lock-in for another. Their data flows through infrastructure they don't control, governed by terms they didn't write.
We believe the next decade belongs to organizations that own their AI stack. Not because it's trendy. Because it's the only way to build a durable competitive advantage.
The race to build the biggest model is over. The current phase of AI is about making it more efficient, more specialized, more practical.
A 7-billion-parameter model fine-tuned on your domain outperforms a general-purpose model with 100x the parameters, and at a fraction of the cost. The future isn't bigger. It's smarter.
The pace is staggering. Every few months, a new generation of models resets the baseline. A 3-billion-parameter model released today outperforms 30-billion-parameter architectures from six months ago. What required a cluster of GPUs last year runs on a single card.
And the gap between open-source and proprietary is closing fast. Open models now match frontier commercial systems on reasoning, coding, and agentic tasks, all without the billions in training capex. The technology is commoditizing in real time.
[1] Towards AI [2] Kaitchup [3] Kaitchup [4] Towards AI [5] Towards AI [6] Towards AI
When AI capabilities become a commodity, thin software wrappers around language models have zero defensibility. If your entire product is a UI on top of someone else's API, you're one pricing change away from irrelevance.
What survives is white-glove service. Deep domain expertise. Custom integration. Real partnership. The solutions that win are the ones that understand your business, not the ones with the prettiest ChatGPT skin.
There are broadly three ways companies approach AI today. Our focus is on the third: delivering tailored capability without the cost and risk of building from scratch, blending technical expertise with an understanding of your business.
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