Weekly Briefing Note for Founders

13th June 2024

This week on the startup to scaleup journey:
  • VC deal flow awash with AI startups

VC deal flow awash with AI startups

AI & ML represented the largest area of startup investment in 1Q24. Not only is this vertical driving a whole category of 'AI first' players but AI & ML is now a key feature of non-AI first companies receiving investment from top VC firms.

One of the key reasons: Generative AI adoption in the enterprise is taking off. The short term opportunity is so big that in the land grab, startups are already vying with the big tech companies for market positioning.

In turn, investors are being hit with unprecedented amounts of AI deal flow. Much of this is now in the application space. Some are developing new approaches to categorise these opportunities and set investment priorities.

Founders creating AI businesses must be cognisant of how the early market is playing out. The dynamics are quite unlike any tech boom that has gone before.

Investing trends

Pitchbook's Emerging Tech Indicator (ETI) provides a quarterly review of pre-seed, seed, and early-stage investment activity involving a limited subset of the world’s most successful VC firms. Even though this is a small grouping it accounts for roughly 10% of total VC investment. The analysis provides a unique perspective into the types of technologies top investors view as the most promising, while also tracking how aggressively they are making capital allocation decisions.

In 1Q24, the ETI tracked 132 seed and early-stage VC deals across the top 15 VC firms. These firms are determined each quarter based on the success of their investments over time in terms of exits and valuations. ETI startups identified via this top-15 methodology have strongly outperformed the broader VC industry, exhibiting higher exit rates and higher valuations.

As highlighted in the report, AI & ML dealmaking in the top 15 surged in 1Q24, with a peak in deal activity of $1.2 billion across 29 deals. Series A rounds attracted the most funding, but Seed-stage companies comprised almost half of the deal count.

AI & ML is also a critical component of companies outside of the AI & ML segment. Of the 103 non-AI & ML companies covered in this report, 47 have integrated AI & ML as a key aspect of their product offerings.

A transformational moment

There is a real sense amongst VCs that the generative AI boom is a transformational moment for many industries. Sarah Tavel, General Partner at leading VC Benchmark Capital, in a recent interview, says we have to start changing the mental model of startups. For decades the industry has seen application software as a productivity improvement tool. But what AI enables is not just a productivity enhancement but "a tool that actually does the work".

AI is thus hugely disruptive and in many cases it is a service that no longer fits the 'per seat' business model. In the enterprise, Tavel talks about the "unbundling of the employee". Today, it's a spectrum of capability (as AI is still in its infancy) but there are already dozens of use cases where whole functions are being automated in such areas as HR, recruiting, sales, language translation and many others.

Many of the big rounds to date have been in the infrastructure layer, especially for training LLMs. But Benchmark's contention is that the application layer will now drive most of the value. There is already a second wave of startups coming into the market with much more sophisticated middle-layer capabilities, not just a simple API into an LLM.

Tavel also claims that the go-to-market strategy becomes easier: "It's not about an employee adopting something new but selling a complete work package" to the enterprise. The workflow is not enhanced, it is replaced.

The role of the Big Tech in AI

Not only is AI a transformational moment but it is unlike prior events of such scale. The market dynamics are notably different. In the early days of the internet and mobile markets, much of the technology innovation was driven by startups. Microsoft and others were simply asleep at the wheel. In the AI era, this has all changed.

The big established players - Microsoft, Google, Amazon, Meta - have been actively shaping the evolving AI landscape for years. The reason: AI is an enabler for both sustaining innovation as well as disruptive innovation. This means that it is strategically important to incumbents not just startups looking to create new markets.

This makes the competitive picture harder to discern for investors. In a recent interview, VC Bill Gurley, GP at Benchmark Capital, commented on the role of the 'incumbents': "..this appears highly choreographed. The windows that open up huge doors for innovators and start-ups are usually not choreographed. You don't have the whole world saying, "Look, this is where we're going." And yet, here you do."

Gurley's other observation is that foundational model companies are now driving such massive investments (with equally outsize valuations) that this can't be considered a startup market anymore. As a new wave of highly contextual LLMs, tuned for individual-level use cases ("small local models") appears on the horizon, this may once again open up opportunity for new players. But the incumbents are already moving fast here too.

Interviewed alongside Gurley, Michael Mauboussin, Head of Consilient Research at Morgan Stanley, underlines the importance of financial resources. For example, AI infrastructure players must build out the enormous compute capability required to train LLMs. This heavily favours the incumbents (as well as semiconductor suppliers like NVIDIA). For example, Microsoft's R&D budget was a lot bigger than their CapEx not so long ago. Now it's flipped.

Mauboussin provides a remarkable datapoint. "If you just take the top five energy companies in the world and the top five technology companies, the technology companies are spending 2x the CapEx as the energy companies. And if you told me 25 years ago or 30 years ago that technology companies would be spending twice as much on CapEx as core energy companies, I think I would have questioned that potential. The amount of money these guys are spending is staggering and very difficult for new companies to come along and keep up with."

Generative AI adoption

The surge in investor interest is in part driven by just how fast AI is unlocking new value streams in the enterprise. McKinsey's latest global survey of AI adoption makes compelling reading. Organizations are already seeing material benefits from Gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology.

The authors claim: "If 2023 was the year the world discovered generative AI, 2024 is the year organizations truly began using — and deriving business value from — this new technology." In the latest survey, 65% of respondents report that their organizations are regularly using Gen AI, nearly double the percentage from the previous survey just ten months ago. 75% predict that Gen AI will lead to significant or disruptive change in their industries in the years ahead.

The average organization using Gen AI is doing so in two functions, most often in marketing and sales and in product and service development, as well as in IT. The biggest increase from 2023 is found in marketing and sales, where reported adoption more than doubled. Most respondents - 67% - expect their organizations to invest more in AI over the next three years.

McKinsey's research also provides insight into deployment strategies, finding three archetypes for implementing Gen AI solutions:

Takers - use off-the-shelf, publicly available solutions.
Shapers - customize those tools with proprietary data and systems; and
Makers - develop their own foundation models from scratch.

About half of reported Gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Investors are monitoring these adoption trends closely. As the focus shifts from core infrastructure to applications, VCs are now facilitating a land grab for hot application spaces. Huge funding rounds are being justified on the basis that the opportunity size is 10x or 50x bigger than simply selling software as a productivity tool.

The eight flavours of AI application startups.

VC deal flow is awash with AI startups building across a wide array of application spaces. The role of AI is many and varied. To try and make sense of this burgeoning landscape, some investors are creating a set of 'paradigms' for how AI application companies actually leverage AI.

Angular Ventures has gone as far as producing a preliminary taxonomy of AI application companies. They have proposed eight categories depending on how startups use AI to create value for customers. These can be grouped into three broad categories:

  1. AI as a value delivery model. These companies leverage AI directly to deliver novel value to customers. There are 4 categories here: (i) Agentic subsystem - uses AI to perform a relatively straightforward (if difficult) service that is consumed as an input to some other service or operation. (ii) Functional copilot - acts as a companion to a human user to help him or her perform tasks more efficiently. (iii) Functional autopilot - acts as a replacement for a human user with the intention of completely replacing that function. (iv) Service/work delivery - here AI enables a software company to completely replace an entire organization in the delivery of a service to a customer (per Sarah Tavel's description above).
  2. AI as a penetration vector. These companies are leveraging AI to enable them to compete in difficult markets and displace incumbents or stubborn internal processes at customers. The 2 categories here are: (i) Data ingest and organization and (ii) Data fusion / complex query handling.
  3. AI as software displacement. These are companies that create value principally by using AI to reduce the need for software development. The 2 categories are (i) Custom code displacement and (ii) Application displacement.

In an increasingly crowded space, these categories will help founders and investors create clearer market positioning. They also help customers determine where in their evolving AI tech stack this particular solution fits.

In summary

AI & ML represented the largest area of startup investment in 1Q24 for top VC firms. Not only is this vertical driving a whole category of 'AI first' players but AI & ML is now a key feature of non-AI first companies receiving investment.

Investment to date has been dominated by the infrastructure layer, especially for training the big LLMs. The degree of capital intensity has seen many outsize rounds, but it is also favouring incumbents. These dynamics present new hurdles for startups that must find their own unique positioning.

As the application space now takes off, investors are being hit with increasing AI deal flow. Some are developing new approaches to categorise these opportunities and set investment priorities.

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