Building a pure technology moat has become a challenge with the emergence of large language models (LLMs). But fear not, there’s a new opportunity to establish a different kind of moat, one based on a much wider product offering that solves multiple pain points for customers and automates large workflows from start to finish.The AI explosion has been mind-blowing, and businesses in the space are struggling to create a defendable product with substantial entry barriers for new competitors or incumbents. But with open-source models becoming available for commercial use, businesses can listen to their customers on a much broader scale and deliver wide products that solve multiple pains that seemed unrelated only a year ago. When combined with integrations that fully automate customers’ workflows, businesses can truly achieve a sustainable competitive advantage.
Put yourself in your customers’ place
Customers would much rather have one “AI partner” that updates its offerings with the latest technology rather than multiple small vendors. To stand out, businesses will need to connect the dots between problems, find solutions that no one else has considered, then find additional dots to connect.
Executing this strategy requires setting a broad vision and much shorter, targeted cycles across the organization in product development and company-wide synchronization. For instance, ML/AI teams should be part of weekly sprints. This will allow them to add new AI features more efficiently and make decisions regarding adding new LLMs or open-source models within the same time frames to improve or enrich offerings.
Building wider AI products
By building a wide product instead of one focused on a single feature, startups can achieve this mythical moat since it simplifies product adoption, creates further barriers to entry, and safeguards against new open-source models that could be released and tear down a business overnight.
Let’s look at the AI transcription market (ASR) as an example: Several providers were in this market with similar price levels and relatively nuanced product differentiations. Suddenly, this seemingly sleepy market was rattled when OpenAI released Whisper, an open-source ASR, which showed immediate potential to disrupt the market but with some substantial gaps. The “incumbents” in the market, who faced the above dilemma, decided to each launch a new proprietary model and focused some of their messages on the problems of Whisper.
At the same time, others found ways to close these gaps and market a superior product with limited R&D efforts that are receiving incredible enterprise customer feedback and an entry point with happy customers.
Returning to the original question, can one build a moat in the AI space? I believe that with the right product vision, agility, and execution, businesses can build rich offerings and, in time, compete head-to-head with market leaders. Many of the core principles needed to identify great startups are already inherent in the minds of VCs who understand what it takes to recognize opportunities and grow them accordingly. It’s critical to recognize that today’s castles look different than they did years ago. What you protect is no longer the crown jewels, but the whole kingdom.
