Enterprise sales in high gear

It’s easier than ever to grow fast, but harder than ever to grow fast enough for long enough

Beyond TTDD. Over the past year, the scale of the AI-powered application software opportunity has become increasingly clear. We continue to meet companies with impressive early growth tracks, and we continue to hear stories of software companies — particularly in the US but in Europe and Israel as well — that are posting very impressive early revenue numbers. As Ed Sim pointed out recently, the “triple-triple-double-double” (TTDD) revenue trajectory that defined successful enterprise startup revenue trajectories for the past decade is being re-evaluated in the face of AI application companies that seem to be growing much faster. The new upper bound seems to be two years of 10x revenue growth (10x10x), but even somewhat more modest pathways such as Ed’s proposed “quintuple-quadruple-triple-double” (QQTD) are more frequently observed than previously. TTDD gets you from $1M to $36M in four years. QQTD gets you from $1M to $120M in four years. 10x10x gets you to $100M in just two years. The list of companies that are apparently achieving results on the upper end of this scale is longer than ever before: Bolt, Cursor, Harvey, Together.ai, 11x, Eleven Labs, Character.ai, Sublime, Wiz, and several more.

Observations and questions. These types of growth rates are absolutely happening, and they have impacted the startup ecosystem already. They also raise some questions for which we do not yet have good answers.

• The bar is being raised for VC. Many venture investors we talk to are just not interested in stories that are showing less than 5–10x revenue growth rates when revenues are at the $1M level. Fair enough, but this does create an unattainably high bar for many companies that are good companies with strong fundamentals. This is particularly challenging for companies with a longer sales cycle (or any sales cycle, see below.). Regardless of how frequent these hyper-growers are, founders must be realistic about the fundraising climate and what VCs are hoping to see. The idea that $1M ARR guarantees a Series A round is pretty much dead right now. VCs need to see a path to $10M ARR in 12–18 months and a growth rate of 4–5X for a Series A round fundraising to be anything other than very hard.
• AI excitement is supercharging adoption. Enterprise buyers began 2024 with a tremendous appetite for adopting AI-first products across their organizations. Many of them were experimenting with this in 2023 as well. There is widespread understanding of the potential impact of AI on nearly all aspects of work. Everyone (including our parents and children) has played with ChatGPT or Gemini or StableDiffusion. We are all aware of this, and — if anything — our collective expectations of what AI can do for our companies in the immediate term are probably over-inflated. But this is leading to a very willing buying environment.
• Employee replacement has led to greater enterprise sales surface area than ever before. It’s very hard to generalize, but the way many of these products are priced and bought appears to be as a replacement for employees. AI products are increasingly speaking in the language of “agentic augmentation” of employees. If the tool makes employees in a given function 10–90% more effective, it can easily be argued that it allows the customer to avoid hiring and training at least one employee. The starting price for a lot of these tools begins around $100K and is seen as the equivalent of an entry-level employee. The economic buyer (which can be anyone with hiring authority in the organization) faces a simple choice: they can hire yet another human employee or they can buy an AI product which will enhance the entire team, reduce their management and training burden, and put them on the cutting edge of technology making them a hero in the organization to senior management which is eager to talk about AI. The choice is an easy one for most — and thus the sales surface area for these AI-first tools is simply enormous.
• These tools are bought not sold. Consequently, it can be challenging to talk in terms of “sales,” “sales process,” and “sales efficiency” in relation to many of these companies. Many of these products are being ripped out of the hands of the companies that are building them. The sales force is largely taking orders and trying to ensure customer success — but they are not forced (yet) to crack complex sales processes, overcome objections, and deal with competitive pressures. That will come, but its not a big factor right now.
• AI has eliminated technical integration challenges. One reason revenue growth is in high gear is that LLMs are particularly good at ingesting unstructured data and contributing to unstructured processes. That removed a critical friction and speeds things up massively.
• Sales durability is a massive question mark. Undoubtedly, many of these hyper-growers will evolve into successful and sustainable businesses with durable revenues and customer stickiness. But undoubtedly, many will not. It is still quite early in this wave, and we don’t yet know how revenue durability will break for many of these companies. I speak with a lot of people with pretty good information, and opinions are sharply divided even on specific companies. One person will tell me that Company X is the fastest growing company in their category; another will point out that churn is massively high; a third will point out that there is no product; and a fourth will insist that users love the product. Time will tell. The next two years are going to be really interesting.
• Good conditions for the top 20 Series A firms; seed is (as usual) harder. At Angular, we invest first (in inception rounds) when there is rarely revenue. In the 10x10x/QQTD world, this is a particularly dangerous place to invest in most categories. The Series A firms have the luxury of waiting to see the revenue trajectory around the $1–2M mark, and can snap up the 10x10x/QQTD hyper-growers. For them, there is little incentive to fund before that trajectory is clear — and zero incentive to invest in 2–3x growers at the $1M level if they believe they will find others that will grow faster. These conditions are actually optimal for the top 20 Series A VCs in the world who can reliably access and win these opportunities. For any Series A firm not in the top 20 (and this would include most of the Europe or Israeli series A firms), the situation is darker. As a seed fund, we need to focus on opportunities where we believe we can predict large outcomes at the inception stage: sometimes that’s going to be 10x10x/TTDD-style growth, but not always. We will continue to focus on backing deep technical defensibility that will drive long-term growth, breakeven potential, and breakout revenue growth down the line.
• Product appears to be the missing ingredient. Across the range of early-stage companies we are meeting — including those with very impressive early revenue traction — the missing ingredient appears to be robust product design. If I had to bet on what will separate winners from losers over the long-run, it would be a range of things all related to product rigor and depth. The double-edge sword of LLMs and GenAI is that they make it very easy to build a product that is 80% of the way to great, but getting to truly great remains very difficult — maybe more difficult than previously. In a world where nearly anyone can deliver an 80% good solution to customers, I believe the best strategy for a seed fund is to identify situations where founders are obsessed with delivering a product that can get to 100% — even if it takes longer and even if the initial growth looks a bit shakier.

Put it in the right gear. For a lot of reasons outlined above, enterprise sales for many AI-first vertical application companies appear to be happening in high-gear. Like a car speeding downhill on a smooth highway, the transmission is in high gear and everything is spinning super fast. Customers are adopting so fast that even high churn is not a problem. It’s easy to overlook competition, product problems, or sales inefficiencies — because all of these just disappear into the blur caused by the overwhelming speed of the growth engine. But when a car in very high gear encounters any disturbances, the transmission can begin to slip — sometimes with catastrophic or unexpected results. I am not at all arguing that all this growth is fake. It’s very very real — but it’s an entirely new world of revenue growth and sales that founders and VCs are now navigating. Knowing when to downshift — and knowing to avoid upshifting too early — may be the key to survival and scale for some of us. Some companies were born to run in high gear from day one. Some companies should start out in first gear because they need to climb uphill on the dirt road for a while before they get on the highway to scale. Knowing which is which will make all the difference.