THE AI STACK DEBATE || Why the Question Isn't How Many Tools You Use, It's Which Ones Are Winning

Most business owners building their AI stack are asking the wrong question: it isn't how many tools you're running, it's whether the categories those tools compete in already have clear winners.

The pressure to consolidate is real. Subscription fatigue is setting in, and the idea of owning one AI platform per business function—one for customer service, one for email marketing, one for content creation—is an appealing simplicity. But if you consolidate too early in categories that haven't matured yet, you risk locking into the wrong tool before the best one has even emerged.

David Shim, co-founder and CEO of Read AI, puts it plainly. The companies winning right now aren't the ones with the leanest stacks; they're the ones experimenting broadly and making deliberate choices about where to commit. "You don't want to use Claude because it's the name brand," he says. "You want to use the solution that will make your team more efficient." The bar for ROI, he adds, has never been higher. Tools that don't deliver value immediately don't get a second chance.

The practical reality is that AI consolidation is happening on three tracks simultaneously, and conflating them leads to poor decisions.

Mature categories are already consolidating

The first track is the mature categories, where winners are already visible. Meeting intelligence is a clear example. Loom records. Gemini transcribes. But Read AI integrates, automates follow-ups, tracks action items across platforms, and sends a digital twin to meetings on your behalf. The gap between a recording tool and a work process platform is significant enough that the category has effectively found its leader. The same logic applies to CRM, where most SMBs have already landed on HubSpot or Salesforce and aren't moving.

Frontier tools are still in the experimentation phase

The second track is the frontier, where the Cambrian explosion Shim describes is very much underway. The large language models are a prime example. ChatGPT, Claude, and Gemini are not interchangeable; each has distinct strengths that reward deliberate use rather than brand loyalty. Using ChatGPT for social media and casual strategy conversations, Claude for research and long-form writing, and Gemini for cross-checking claims and generating graphics isn't inefficient. It's precision. Consolidating to a single LLM today would mean accepting meaningful trade-offs in output quality. There are no clear winners yet, and the race will continue as a new model is released each week that claims to topple the incumbent leader.

Specialized platforms are converging

The third track is convergence, in which specialized platforms combine capabilities because neither is complete without the other. ElevenLabs and HeyGen are moving closer together because a convincing AI video clone requires a convincing AI voice; one without the other is a half-product. HeyGen's integration with Gamma, which allows users to generate narrated presentation decks voiced by their own AI clone, is another signal that the boundaries between content creation categories are dissolving. Consolidation here isn't a business choosing one tool over another; it's the tools themselves deciding that together they provide a better solution.

The strategic takeaway for SMBs is straightforward. Audit your stack by category maturity, not by headcount. In categories with clear leaders, commit and integrate deeply. In frontier categories, stay in experimentation mode and hold the ROI bar high. And watch the convergence plays closely, because the most important consolidation decisions of the next two years may not be ones you make at all.

Cover photo by Florence Leung