Last week, Wing launched its 7th annual Enterprise Tech 30 list and report. For those unfamiliar, at a high level the list identifies the most promising enterprise tech startups by surveying ~100+ VCs, who pick 10 companies each by category (early, mid, late, giga), with the stage cut-offs determined based on funding amounts raised at the time of voting (detailed methodology). The 15 companies that receive the most votes by stage make the list. This year was the 7th edition of the list, which is presented below:
Now, onto a few observations.
1/ The Rise of AI Applications
In the last few years, AI has been a central theme around companies that make the list. However, most of the companies were building models and infrastructure with a lot fewer of what we would consider AI-native applications. This was evident in the category breakdown — only 25% of companies on the list the past few years were application companies (and only a handful were AI-native), much lower than the historical 35-40% range.
This year however, AI-powered applications began to take center stage, with application companies rising back to 35% of the list. Moreover, application companies made up 43% of the early and mid stages of the list — perhaps a sign of things to come.
Additionally, over 50% of the application companies can be considered AI-native (vs only 2 out of 10 last year), highlighting that we’re now at the stage where companies are turning these raw AI capabilities into transformative products and innovating at the application layer.
Popular workflows and use cases that AI-native and AI-forward companies are tackling include search and knowledge retrieval (Glean, Perplexity), support (Decagon, Pylon), sales (Clay, Copy.ai, Unify), workflow automation and verticalized copilots (Harvey, Rogo).
2/ The Emergence of Agents and Enablers
Alongside the surge in AI apps, there’s an exciting trend toward agentic technology. While agents are still early, and you’d be hard pressed to get three people to agree on a definition of them, its clear that founders across the infra and application layers are pushing beyond simple chatbots and copilots to ones that can complete entire workflows leveraging planning, memory, reasoning and tool use.
Looking at the list, we see that enablers of agents such as Browserbase, CrewAI, LlamaIndex make up three of the five early-stage spots. Similarly, early poster examples of agentic applications such as Decagon also were high up on the list.
I expect to continue to see more agentic companies in future lists, as we see the continued rise of the agentic workforce.
3/ The AI Fundraising Frenzy
The current AI buzz is reflected in the fundraising landscape, with deal sizes and frequency reaching record levels. The median last deal size of companies that made the list was the highest ever across every stage other than mid, indicating the increased size of rounds these companies are raising.
Additionally, companies are raising rounds very frequently as highlighted below. The median time since the previous fundraise had steadily decreased after increasing post the 21/22 correction.
This is perhaps quite justified given the impressive revenue ramps we’ve seen from some of the companies such as Cursor and Mercor which have raced to $100M in a very short period of time. Cursor took the #1 spot on the late stage list after skipping the early and mid stages completely. Given that later stages are difficult to stand out in, it’s quite an impressive feat which illustrates how meteoric their rise has been.
4/ The Typical Category Distribution Trend
There’s been a lot of movement in the category distribution of companies that made the last the last few years, with the emergence of AI models and associated infrastructure dominating. This year saw continued changes, with applications rising and taking share from data platforms and AI models/tools.
But looking at the historical distributions, it does seem that where we are at now is close to somewhat of a “typical” state, although it will obviously ebb and flow. Based on the rough historicals, it seems like a typical breakdown of the list tends to look like:
- ~35-45% horizontal + vertical applications
- ~30-40% infrastructure (data, developer tools, AI models and infra)
- ~10% security
- ~10% fintech
- ~5% defence and aero
