as CEO affinityRay’s goal is to bring relational intelligence to the world because all opportunities start with relationships.
This is a difficult time to raise startup capital. Total investment in startups has fallen significantly—80% drop in the first quarter of this year Almost one-fifth of founders who successfully raise funding have to take a down round.
However, there is one industry that is bucking this trend. It’s AI. According to Pitchbook– Even excluding OpenAI’s most recent $10 billion round – AI venture funding in 2023 is far higher than in 2022 and well over half of the $9.1 billion that peaked in 2021.
Looking at the broader market, investors across the board have tightened their investment criteria after the most active era in venture funding and deals in history. Record funding was achieved at the beginning of the decade, reaching $128.3 billion in 2021, 1.5 times more than the previous year. Much of that capital remains unutilized.
AI has emerged as the latest in a series of high-profile technologies to capture investor demand. For the past few years, it has been cryptocurrencies and the Metaverse (of questionable value to both consumers and investors). But with LLM and other generative models exploding, enabling new apps like ChatGPT and Midjourney to become some of the fastest-growing apps in history, AI has demonstrated its staying power as a transformative technology. did.
These tools fundamentally changed what is possible with software and spawned a new wave of ideas, startups, and talent coming together to build in this space.
As an investor, it’s nearly impossible to ignore the hype cycle. The fear of missing out is real even in times of uncertainty, when risky opportunities are typically avoided. AI has brought new possibilities for disruption to every industry. This is the psychology that is driving the disparity we see in AI investing compared to other private markets.
These are three observations about how the AI opportunity is evolving.
1. The ability to accumulate unique datasets is becoming a defensible moat.
LLM makes it easy, in some cases, to build a huge number of new features and products that would otherwise be very difficult to build and train in-house from scratch. I did. However, there are clear benefits and defensible opportunities for companies whose LLM-powered capabilities are built on proprietary datasets that they own.
Consider a company like Box. The company’s Box AI service is powered by the files and content that enterprise customers already store on the base platform. By feeding that data into a language model, Box AI enables features like file/content Q&A chatbots that only Box can build uniquely, given that other companies don’t have access to the same underlying data. We can provide it.
In general, companies that provide systems of record (such as file storage or CRM) have a huge opportunity to use generative AI to deliver defensible value. Investors should look for and understand data moats when considering investing in new AI companies.
2. How companies implement AI internally can be just as important as what capabilities they use AI to build.
There is a lot of discussion among investors about what new capabilities and businesses can (and will) be built using AI. However, less discussed is how portfolio companies can leverage AI internally to improve internal operational efficiency. In my view, this is as central to the company’s AI strategy as the product roadmap.
Companies that successfully implement AI productivity tools will be able to scale more efficiently because humans and technology work together to improve results. Tools like ChatGPT process large amounts of unstructured data (for example, user feedback from platforms like Discord) to understand what people care about and what you, as a builder, should know. you can understand. But ultimately humans still have to make the decisions.
Investors need to learn how startups are leveraging AI to improve efficiency metrics, team productivity, and reduce costs, both as part of supporting and due diligence of portfolio companies. there is. Especially in an uncertain macro environment like the current one, AI can make a huge difference to your bottom line.
3. There are untapped opportunities for AI-powered services.
Currently, much of the conversation around AI focuses on AI-powered software features and products. But I think there’s an equally huge opportunity in creating and reinventing the entire service sector using AI.
Industries such as banking, law, and accounting have traditionally delivered value through the provision of services, which are highly human-intensive operations. Most of the value in these industries is generated by the largest service companies themselves.
But how many of these human roles can be automated by AI? And what will an AI-first law or accounting firm look like? This will be an interesting question for both founders and founders.
What about this whole hype cycle? It won’t last. they never last long. The nature of startups is that there will be more failures than successes. However, AI is lowering the barrier to entry in many industries. Opportunities are being created at a faster pace, emerging in areas such as services reinvented by AI, and companies with unique data sets have the potential to achieve truly transformative outcomes. Masu.
Ultimately, it’s up to investors to identify and fund these opportunities, and it’s important to consider a company’s AI strategy in all future deals.