Winter may have arrived early for AI and machine learning startups. After years of rosy projections, growth and investor enthusiasm, a new report from PitchBook shows that VC activity in the AI sector declined precipitously over the past few months.
Deal value growth in AI startups was down 27.8% quarter over quarter in Q2 2022, with overall investments reaching $20.2 billion across 1,340 deals. Year to date, VCs have funneled $48.2 billion into AI startups across over 3,000 deals — which sounds healthy but actually represents a 20.9% year-over-year dip.
AI funding declined across all deal stages, per PitchBook data. Excluding angel and seed rounds, early-stage investments hit $4.2 billion, a drop from $5.6 billion in Q1 and down 35% from the same quarter last year. Meanwhile, later-stage funding declined from $18.3 billion in Q1 to $13.4 billion in Q2 — a 48% decrease compared to last year.
As VC deal value and deal count in AI startups reach their lowest levels since Q4 2020, it’s not just investors that are pulling back.
According to a recent survey from The Harris Poll and Appen, fewer companies are devoting budgets between $500,000 and $5 million to AI, opting instead to up capital commitments to general cloud computing and edge infrastructure.
“In Q2, investors nearly halted funding for prominent horizontal categories including AI platforms and semiconductors,” PitchBook senior analyst Brendan Burke, who authored the PitchBook report, told TechCrunch in an email interview. “In AI specifically, enterprises have eased their appetite for specialized AI infrastructure, opting to reuse existing infrastructure to run AI workloads for models that have already proven to be successful. This pressure limits startups’ ability to transform their customers’ data stacks. Further, specialized chips are not seeing high adoption in this environment after successful pilots in 2021, with enterprises being content to leverage their existing compute clusters.”
Burke wasn’t quick to write off every category of AI startup, pointing out that certain use cases — particularly accounting automation and wealth management — have proven robust amid the present downturn. But in the same breath, he noted that many AI platform companies have struggled to reach their revenue forecasts and that they’re competing against incumbents like Google, Amazon and Microsoft for a relatively small addressable market. Burke pegged the market size for AI software platforms at $7 billion — a modest figure compared to, say, the $17 billion HR software market
“The IPO wave of 2021 came and went without significant outcomes for horizontal AI startups, leaving questions as to the market size for AI software and opportunities for AI chip companies,” Burke said. “Chip companies offer exciting innovations yet a high-risk path to scale that is difficult for mainstream investors to underwrite.”
Inside the top-level AI funding figures there’s more bad news. AI-as-a-service investment is on pace to decrease 87.7% in 2022, the PitchBook report noted. In terms of deal count, VC exits in AI fell 21.8% as merger and acquisition activity remained low outside of IBM’s acquisition of database management startup Databand and Meta’s purchase of clothing size recommendation company Presize.AI.
But there’s reason for some optimism around specific segments within AI, Burke said — like data prep, computer vision, robotic process automation, natural language processing and big data analytics tech.
“Generally speaking, investors are currently biased against AI startups’ resource-intensive capital plans due to fiscal discipline, but we expect them to restore their confidence given the successes in the field. A widespread shift to AI-specific infrastructure remains a long-term trend,” he continued. “Database management [also] continues to grow rapidly as enterprises shift to non-relational databases and data lakes. Spending in those areas is now expected to exceed relational database management by 2026, which we believe will be a boon for AI decision engines given their ability to analyze unstructured data from a variety of sources.”
So what does this mean for AI startups? Burke recommended that they aim to leverage existing infrastructure and integrate with systems of record to find their place in the data science stack. Generally, he said, vendors building analytics on top of leading cloud database systems and industry-specific systems of record can expect to see more rapid adoption.
“The goal to be an end-to-end platform requires outsized investment and has limited product-market fit,” Burke added. “Vertical applications like sales and marketing, drug discovery and information security build on top of existing systems of record and continue to drive high growth. Horizontal platforms will have to demonstrate their pathways to exit in 2023 to restore confidence.”
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