At the end of 2022, like many, I made some predictions about what 2023 would bring to the technology investing ecosystem. Namely, the Fed would tame inflation, and the fundraising market would thaw, but overall, claiming that 2023 would be the first year of a “new normal” era for the markets and that machine learning would permeate most of the workflow software operators use today.
Fast-forward a year later, and we’re entering our second year of the post-pandemic economy. The state of venture capital investing and technology battle several factors, some of which are 2023 repeats and some brand new, including but not limited to higher interest rates, a more challenging market that demands better product-market fit and, of course, the rapidly evolving state of AI.
Will we end 2024 in the same market we started 2023 in? Through exploring the sluggish first half of 2023 to how AI exploded in the second half, I have several predictions about what we can expect to see in the year ahead.
The IPO market remains closed through the first six months of the year, but a few significant issuances in the middle part of the year reopen it for others, with seven venture-backed software IPOs completed.
It was widely speculated that Klaviyo’s exit would open the public markets and cause a ripple effect for others, but that wasn’t the case. The 2024 market will imitate 2023’s in some ways as the higher rates environment and geopolitical tensions continue to weigh on valuations and defer IPOs, resulting in a quiet landscape. Despite this, there will likely be a few outlier companies that spur momentum for others, primarily in core software, with companies that demonstrate spectacular unit economics and cash flow.
M&A picks up throughout the year as either the anticipation or the reality of a rate change accelerates it for the feared increase in valuations. In the last two years, on average, M&A has totaled about $49 billion. It will surge to above $60 billion, primarily driven by AI acquisitions. Private Equity becomes an essential buyer of companies growing 10-25%, just as it did in 2023.
M&A acquisition value plummeted in Q4 2022, rivaling the dot-com bust and Global Financial Crisis for its lack of activity. We’ll see a slight change in the market and, subsequently, increased M&A activity as the public technology markets begin to demonstrate strength and valuations creep back up. Take-privates totaled $50.2B in 2023, with Qualtrics and Coupa topping the list.
AI and data continue to dominate the funding landscape.
Much like mobile technology became a de facto part of every startup, AI is no longer a category but the core or a component of every product. It’s still early days with LLMs, and there’s a lot of work to do; however, LLMs have already wholly transformed data in many ways, and innovations with data will continue to command VC investment. Likewise, venture dollars will still funnel into startups in the space. LLMs have driven an increased demand for data, caused a complete architecture change inside companies and changed how data is manipulated. As the technology evolves, we’ll continue to see an increase in new data products and data teams.
The bitcoin ETF drives a resurged interest in web3 financing. The crypto winter has forced many companies to become revenue-generating and we will see the first broadly successful tokens with dividends (likely outside the U.S.). We also see more ARR-based web3 businesses achieving scale.
Last year, I predicted 2023 would see a continued hangover from the high level of 2022 activity in web3. This year, we’ll see a resurgence following the industry’s setbacks as U.S. regulators decide to move forward with bitcoin ETFs. This would remove the industry from recovery, mark a significant shift for web3 and cement the digital assets space as part of traditional finance.
U.S. VC deals fall from $275 billion in 2022 to $200 billion in 2023, and sustain at about $200-$220 billion next year. Valuations will remain relatively steady, except for AI businesses, which will command a premium of about 10-15% to the market.
While VC deals fell dramatically between 2022 and 2023, 2024 won’t see as sharp of a decline. LP reallocation to other asset classes continues, still smarting from the precipitous collapse in valuations in 2023 and the need for liquidity.
The discussion around AI regulation has become a critical topic in the U.S. due to the rapid European regulation. It becomes a vital part of the election conversation, particularly as deepfakes and machine-generated content sow increasing distrust of media.
The EU AI Act will have trickling effects on U.S. businesses and on the U.S. AI conversation writ large as the administration rushes to develop operational processes and frameworks. Biden’s executive order on AI will be hotly contested during the presidential election debates and will likely serve as a point of more divisiveness among parties as it asks to what extent privately held companies can, or should, be regulated.
The share of AI-enabled searches approaches 40% of all consumer searches as consumer behavior patterns, especially on mobile, drive innovation in this direction.
AI made significant waves this year for its use cases on the consumer side through chatbots, personalized content, AI-enabled search and more. Consumer behavior patterns, specifically around e-commerce, will continue to drive the rise in AI-enabled searches as consumers leverage the technology for more personalized experiences.
Companies and startups, in particular, report meaningful productivity improvements from AI, reducing their headcount growth but growing revenue just as much as projected. ARR per employee increases by 10%, twice the decade-long average.
In 2013, these companies’ average revenue per employee totaled $200,000. Today, that number is $470,000 for a group of successfully publicly traded software and infrastructure companies — a 135% improvement. While most companies do not grow revenue-per-employee per year linearly, AI will drive this growth to happen more steadily and with greater rigor as adoption and applications of AI drive efficiencies within companies.
Comment