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👇 Heartcore Insights
Welcome to the 105th edition of Heartcore Insights. Curated with 🖤 by the Heartcore Team.
If you missed the past newsletters, you can catch up here. Now, let’s dive in!
How to escape competition - Building enduring application-level value with LLMs - Sarah Tavel (GP @Benchmark)
Copywriting was the first visible category of work that startups leveraging LLMs went after, (Jasper and Copy.ai). It’s very clear that if you are able to build a product that uses LLMs to automate a work product that has previously required hiring someone for that job, the demand will be there.
But that doesn't mean it's been all sunshine and rainbows for these companies. If anyone with access to any of OpenAI’s APIs could essentially get to the same output, you are always vulnerable to customers moving their business to whoever offers the product at a cheaper price. As an example, both Jasper and Copy.ai got caught on their back foot when OpenAI released ChatGPT.
Additionally, we've seen Notion, Hubspot, Canva, quickly announce GPT-driven features in their products. It becomes the classic race of either the startup figures distribution or the incumbent figures out innovation. Here, the "incumbents" aren't sleepy companies but innovative tech companies.
How do you build enduring value if you are a startup looking to leverage LLMs to create a new application?
Narrowness in initial focus: in a world with multiple competitors seemingly focused on the big horizontal opportunities, and still rapidly evolving underlying technology, a focused competitor can win and then can expand from that position of strength. These will often involve tuning a model to a specific use case, hooking into if not replacing existing workflows (oftentimes leveraging other ML techniques to do so), and therefore means that there will be more to the execution than a simple API call to a foundation model.
Feedback loops: if you build an application that can leverage user engagement to improve the accuracy of your model, there will be advantages to scale and thus the ingredient to escape competition. Having a human in the loop that acts as a bit of a power user to provide feedback, in the beginning, is another mechanism companies use that is also effective in providing advantages to scale, provided you are able to leverage that feedback to fine-tune.
Accruing data asset: an interesting dynamic happens for companies where a positive externality of users leveraging their LLM-driven application leads to the creation of a new, useful data asset that wouldn’t have been possible before at scale. In a way, this externalizes the moat outside of what’s possible with LLMs themselves and creates an even more differentiated offering that can escape competition at scale.
In a bull market, high growth and high burn are fine because if you need to spend a lot of money to get there, whether through paid marketing or partnerships, you do it… after all, you can just raise more money, right? But in a bear market, the answer changes. This means the strategy for user growth just went from “as much as possible” to “efficient, profitable, productive” in just a few quarters.
What are some ways you should be rethinking your growth strategy?
Embrace the new normal: although there is a floor for how fast a product has to grow to be interesting, there’s now a much bigger emphasis on efficiency. One metric that’s been recently popularized is the Burn Multiple = Net Burn / Net New ARR. In other words, how much is the startup burning in order to generate each incremental dollar of ARR?
Cut your marketing spend: in particular (a) keep the high ROI channels, cut the low ROI ones, even if they provide volume, (b) focus on accountable spending, and reduce ones that have a long/fluffy payback, (c) rethink brand marketing spend. On the first point, every growth effort is built from layers of channels built on top of each other. Usually, these layers are built over time by growth teams who keep arbitraging 10:1 LTV/CAC ratios down to 3:1, then 1.5:1, before they slow down. It’s time to unwind that. Instead, go back to the core.
Laser focus on your engaged, high LTV users: at Uber, it was often noted that it was much faster to get drivers to spend 10% more time on the platform than to acquire 10% more drivers in a market. The reason why this dynamic exists is that there’s often a central segment of where the product is really working, and then an “Adjacent User” where it only kind of works. This can become a tradeoff between Marketing versus Product-Led Growth, where the former drives CAC, whereas the latter is built on product development costs.
Live to fight another day: you now need much more revenue to justify the same valuation - what used to be a 15x multiple is now 7x. This is causing a domino effect in the industry. When you see a $2B public company cut down to $1B, then a $500M privately held startup is cut down to $250M, and so on. That means for much of the industry, the next round of a startup just became much, much harder, but we potentially won’t know for a while how much the bar has moved.
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