Remember 2024? Every week brought a new AI developer tool. Vector databases, prompt engineering platforms, fine-tuning frameworks, evaluation suites, agent builders, RAG orchestrators — the landscape was expanding so fast that keeping track of it all became a full-time job.

That era is over. The consolidation wave that many predicted is now visibly underway, and it’s happening faster than most expected.

The Numbers Tell the Story

According to data from CB Insights, funding for AI infrastructure startups dropped 35% between Q3 2025 and Q1 2026 — not because investors lost interest in AI, but because they stopped funding tools that compete directly with features being absorbed by major platforms.

The pattern is clear: large platform companies are eating the AI tooling stack. LangChain went from framework to platform, absorbing functionality that a dozen smaller startups had been building independently. AWS, Azure, and GCP have all launched managed agent services that replace standalone agent frameworks. Databricks acquired two MLOps companies in four months. Snowflake’s AI features now cover what three or four separate tools used to handle.

For startups that built single-purpose AI tools, this is an existential moment. Your product either becomes a feature of a larger platform, or you need to find a niche specific enough that the big players won’t bother building it.

Who’s Winning

The survivors tend to fall into a few categories.

Platform plays that moved fast enough. LangChain, Weights & Biases, and Hugging Face all managed to expand from single tools into broader platforms before the consolidation wave hit. They’ve got enough surface area and user lock-in to compete with the cloud giants, at least for now.

Deep vertical specialists. Companies building AI tooling for specific industries — medical imaging pipelines, financial model validation, legal document processing — are relatively safe because the big platforms don’t go deep enough into domain-specific workflows.

Infrastructure that sits below the model layer. Compute orchestration (Modal, Anyscale), GPU scheduling, and inference optimisation tools are still differentiated because they solve problems the model providers don’t want to solve themselves.

Open source projects with strong communities. Tools like Ollama and vLLM have become infrastructure that even the big platforms build on rather than replace.

What This Means for Enterprise Buyers

If you’re an enterprise evaluating AI tools right now, the consolidation wave actually simplifies your decision-making, but it also introduces new risks.

The simplification is obvious: fewer tools to evaluate, clearer market leaders, and more integrated platforms that reduce the duct-tape integration work that characterised early AI deployments.

The risk is vendor lock-in. As platforms absorb more of the AI stack, switching costs increase. The vector database that was a standalone service becomes a feature of your cloud provider’s AI suite — which is convenient until you want to move to a different cloud.

My advice: pick your platform bets carefully, and insist on data portability and open standards wherever possible. The companies that built on proprietary, tightly-coupled AI stacks in 2024 are the ones struggling most with migration costs today.

The Second Wave

Consolidation doesn’t mean innovation stops. What usually happens after a consolidation wave is that a new generation of startups emerges, building on top of the now-established platforms rather than competing with them.

I’m already seeing early signs of this second wave. Startups building industry-specific agent workflows on top of the consolidated platforms. Companies creating compliance and governance layers. Teams building specialised evaluation frameworks for specific use cases like customer service quality, code review accuracy, or document processing reliability.

The AI tooling market isn’t shrinking — it’s reorganising. The chaotic, overlapping landscape of 2024 is giving way to a more structured ecosystem with clear layers: foundation models at the base, platforms in the middle, and specialised applications on top.

For anyone building or buying AI systems, understanding where you sit in this stack — and which layers are commoditising — is the most important strategic question of the year.