AI is one of the fastest-growing startup sectors globally: in May alone, large-scale language models raised $12.4 billion, according to Crunchbase, which represents 40% of the total funding raised worldwide in May.
But beneath the surface lurks a disturbing truth. While the initial hype was deafening, there are signs it’s starting to fade. Now the big question is: Can promising AI startups translate their early promise into long-term success?
Valley of Disillusionment
As a venture capitalist, AI-native startups are some of the fastest-growing startups I’ve come across, scaling to $1M in annual recurring revenue in record time. According to Insight Partners, nearly half (44%) of the new unicorns in 2023 will be generative AI companies. While the early-stage growth is undeniable, the rapid growth often hides a significant challenge. In this case, it’s the lack of a post-demo product plan. Demo value does not equate to long-term business value.
The recent excitement around AI bears a striking resemblance to the “Peak Inflated Expectations” stage of Gartner’s Hype Cycle, where early hype around an emerging technology produces a few success stories accompanied by many failures.
The AI hype cycle. Source: Provided.
As the AI hype begins to die down, we stand on the brink of the next phase, the “Disillusionment Phase,” when inflated expectations from both customers and investors face reality. Customer and investor mindshare declines. AI startups will be under more pressure than ever to show results and revenue.
Now is the time to deliver on what the founders promised and meet customer expectations. From there, you can continue to ship your product so that your company’s growth doesn’t stagnate or, worse, collapse. For an AI startup to be successful, there are three key stages to consider: the initial launch, the flight mode, and the descent into the world of growth and expansion. Take more ownership of your workflows and data to drive your intelligence systems.
Course Charter
Compared to traditional startups, AI application layer companies have a lower barrier of entry in terms of development time. AI startups require less initial capital investment because the underlying models are available through APIs and open source models. AI’s disruptive potential and high profitability are also attracting significant investment from venture capitalists. This means more customer value can be generated in a shorter period of time, but it also means more competitors vying for customer attention. So founders need to think beyond the initial barrier.
There are four key questions AI founders need to ask themselves to show they can think beyond the launch point and grow their business sustainably.
Working backwards, what is the most ambitious version of your company? And what experiments can you run to maximize your learnings to get there next year?
Why is this the right wedge product/market and how do we build from there – can we build it to address more workflows and provide more value to our customers over time?
What logical steps can you demonstrate or mitigate risks over time to show you are moving in the right direction?
What unfair advantage does this give you in the long run? Why does it make customers more likely to stick around?
If founders can answer the questions above, they can build a more enduring business over time and accurately track and measure the resources needed to get to the next stage. Quick wins are great for a business’s dopamine rush, like a sugar rush, but without a roadmap of what happens next, all of your early efforts will be for naught.
Space Control Center
Now comes the real test. To get on a sustainable trajectory, founders must shift focus. Capital is important for startups, but it alone rarely leads to success. Capital is a tool, not a strategy. Success is achieved by properly executing a strategic vision.
First, prepare your team. Founders need to assemble the right team and talent to navigate the complexities of AI development and business growth. AI and machine learning talent is in short supply, but a good developer can bridge the gap between regular development and working with an LLM. Hiring good systems engineers and architects can eliminate a lot of headaches and help you scale. Team structures can be small, so having people who not only understand the technology but also the business side and customer value helps you build the right thing and move fast. While there is excitement, many industries are stuck at the starting line and not fully exploring how AI can improve their operations and workflows. This paves the way for startups with niche solutions tailored to specific sectors. When you engage with your customers, you know them so well that you can read their minds and know their needs. That deep understanding goes a long way.
Next, refine your product. Remember, your first release is just your first iteration. Now it’s time to gather data and user feedback to optimize your offering. Focus on showing a clear return on investment to your target users. This is what will keep them interested and coming back for more.
From Rise to Expansion
The third stage is a transition to a world of sustainable growth and continued expansion.
Always understand your cost structure relative to new revenue. You can use the “magic number” – your net new ARR for a period divided by your S&M expenses for the previous period. Ideally, this ratio is greater than 1.
Next is identifying opportunities for growth. Can you extend your solution to new geographic markets or user segments? Are there strategic acquisitions that can complement your core offering and accelerate your expansion? A multi-stage plan shouldn’t be static; it should be a dynamic compass that constantly adapts to changing conditions.
This includes understanding the repeatability of your existing business and anticipating industry trends like technological advancements and shifts in consumer preferences. Evaluate your existing assets and resources (the tools and talent that make your startup unique) and keenly identify adjacent opportunities that can add value and drive further growth. One exercise to do is one that Airbnb used from their Snow White example: plot all the user journey interactions that led to and go beyond your current product. You’ll be surprised at what broader experiences you can build to help your customers.
As you refuel, remember that fundraising at this stage is not about filling a hole, but injecting fuel into an efficient flywheel with reasonable predictability. Unfortunately, injecting fuel into a rocket that is moving in the wrong direction will only move you further away from where you want to go. Focus on demonstrating metrics that quantify success, not just initial hype. It is important to align with the right investors and partners who can provide strategic direction and a network to help keep founders on the path to success.
Like rockets, AI startups have experienced exhilarating launches and have the potential to reach the stars, but landing requires precision and a clear roadmap.
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