This week on the startup to scaleup journey:
European exit values collapse
In 2021, exit value in VC markets peaked at an astonishing €135B across Europe. To put this into perspective, the second highest value ever recorded was $48B in 2018. Of this $135B, IPOs made up the lion's share at €104B, followed by corporate acquisitions (M&A) at €25B, with PE Buyouts coming a poor third at €5B. In 2022, exits dropped significantly to €39B, largely as a result of IPOs collapsing to €13B. But acquisitions maintained momentum at €24B, whilst buyouts halved to just over €2B - mainly due to the rising cost of debt for LBOs. Now, 1H23 figures confirm that exit activity has all but collapsed over the past 6 months. As Pitchbook has reported: In 1H23, exit value in Europe amounted to €3.5B, a run rate that implies full-year 2023 will come in 82% below 2022’s total exit value. Similarly, exit count is currently 10.4% below 2022, as both value and volume signal more caution amongst companies and investors. At the beginning of 2023, as funding markets receded and investors sought liquidity, many startup boards began investigating M&A options. But for the majority the outcome has simply been a 'fire sale': A year ago in 1H22, 431 European venture-backed companies were acquired for a total of $17B (an average of $39M). A year later in 1H23, 299 acquisitions have so far been recorded for a total exit value of $1.6B (an average of just $5.4M!).
This collapse in exits has led some investors to push for 'secondaries' - where investors trade equities amongst themselves - to generate liquidity. As recently reported by Sifted, amid a cooldown in funding, some companies have marked down their valuations by as much as 85% from their peak. That ought to mean great deals can be had by buying shares from early investors, founders and employees who want to cash out. But investors say that secondary activity is still sluggish as startup founders remain hesitant to reprice their shares, and investors stay reluctant to invest in anything but the best companies. Secondaries usually trade at a discount on the last valuation, as sellers pay a premium to get liquidity early. But substantial share repricing, aka a 'downround', can send negative signals to the market. And as secondary transactions don't bring any new money into the business to fund operations, it's not surprising that founders are very wary of such a move. But with exit paths currently all but blocked, it seems inevitable that there will be an increase in downrounds to ensure survival for many. This in turn should boost activity in secondaries over the next 12 months or so.
This combination of very difficult funding and exit markets is forcing startups to develop ever more innovative financing strategies. Multi-step funding plans have almost become the norm, especially when more time may be needed to hit elevated investment criteria. It's not unusual to see a company that had been planning for a Series A in early 2024 now inserting a bridge round to provide an immediate runway extension, with a 'late Seed round' replacing the 'A', which has then been pushed out to later in 2024. If the bridge round is in the form of a SAFE note or convertible loan, the awkward issue of pricing can then be deferred. But these smaller, more 'digestible' rounds, can still be very time consuming to manage: Many VCs are now viewing a follow-on funding event as if it were a new investment, with founders having to pitch the whole story afresh. This is one of the reasons why investors and advisors are recommending that when planning a campaign, capital-raising timelines should now be extended from 6 months (the norm in 2022) to 9 months. In sum, founders are having to navigate some of the most challenging market conditions for years. Every option is requiring more creativity, more time, and greater doggedness than ever before.
What every CEO should know about generative AI
Generative AI is poised to unleash the next wave of value creation. The impact on the startup community has already been profound as AI startups defy the venture funding decline. On top of a rapidly growing cluster of 'foundational models', some startups are already building value-added products and services, whilst others are hastily adopting generative AI internally. But the majority are still investigating how to grasp the potential of AI. For this latter group, moving up the learning curve quickly is now critical; not least as every founder is suddenly being put on the spot by investors to describe their AI strategy. To accelerate learning, we are continuing to share discoveries from our own recent investigations, this time from the excellent McKinsey library. 'What every CEO should know about generative AI' is a great starting point. This extensive article (17 pages), provides 3 major insights: 1. A primer that sets out what generative AI is and how the underlying large language models (LLMs) have evolved. For startups looking for entry points it provides an excellent overview of the emerging generative AI value chain. (For those that want an even more in-depth analysis look here). 2. How to quickly put generative AI to work (to avoid falling behind the competition), and 3. Considerations for getting started with a generative AI offering, such as the organisational model, potential use cases, and the tech stack.
For any company looking to insert itself into the AI food chain, the next step is understanding where business value could accrue. McKinsey's 68-page report on the economic potential of generative AI provides an in-depth reference. There are some key takeaways for CEOs trying to scale the potential: The productivity impact will add trillions of dollars to the global economy. Even though it will have significant impact across ALL industry sectors, there will be unequal value dispersion across functional applications. About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. Current generative AI and other technologies have the potential to automate work activities that absorb a staggering 60 to 70 percent of employees’ time today. As a result, the pace of workforce transformation is likely to accelerate: Half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in McKinsey's previous estimates. Corporates are already investing $B's in new technologies, services, risk management, skills development, and many other areas to ensure they don't get left behind. Corporate budgets are being heavily prioritised around AI and this is exciting investors looking for the 'next wave' to exploit. Boards are racing to provide their companies with the right guidance for the generative AI era. Many are scrambling to understand and respond to the opportunity, which could quickly turn into a threat if not addressed with urgency.
McKinsey's research has identified the 4 key questions that boards must query management about: 1. How will generative AI affect our industry and company in the short and longer term? 2. Are we balancing value creation with adequate risk management? 3. How should we organize for generative AI? But it's number 4 that really opens the door for startups; 4. Do we have the necessary capabilities? The 2 key components here are technology and talent. The core technology concern is that only by incorporating their corporate data into the foundational model will companies be able to fully unlock AI's transformative power. This will require a scalable data architecture with new data governance and security procedures, as well as dramatically upgrading compute resources. This is already very fertile ground for dozens of recently funded startups, especially those building foundational models, model hubs, and 'MLOps' solutions. Above that there are already hundreds of startups layering on new applications for specific use cases. The core talent concern is the desperate need to reskill, hire, or, where necessary, outsource specialist tasks. Again, this is where significant opportunity lies for new entrants that are quickly building out world class teams themselves. There is currently a land grab for AI talent and we are seeing many high-profile generative AI startups across Europe, such as Mistral, Stability AI and Sana, vacuuming up many of the best and brightest, some on the back of huge rounds - even before they have generated a single $ in revenue.