Image Credit: Created with Midjourney
Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
Building a pure technology moat has become challenging since the emergence of large language models (LLMs). Due to the lower barriers of entry for introducing new products to the market and the continuous fear of becoming outdated overnight, existing businesses, startups and investors are all trying to find a path to sustainable competitive advantage.
However, this new landscape also presents an opportunity to establish a different kind of moat, one based on a much wider product offering solving multiple pain points for customers and automating large workflows from start to finish.
The AI explosion, whose blast radius has kept growing since the public launch of GPT3.5/ChatGPT, has been mind-blowing. In addition to the discussions around efficiencies and risks, businesses in the space found themselves dealing relentlessly with the question of whether building a technology moat is still possible.
Companies are struggling with the realities of creating a defendable product with substantial entry barriers for new competitors or incumbents. Just as in the past, this will continue to be a necessary component for a new business to be able to grow and become a centaur or unicorn.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Open-source models the real revolution
The real revolution isn’t just ChatGPT. The real revolution includes open-source models becoming available for commercial use — for free. Additionally, solutions such as LoRA are allowing anyone to retrain open-source models on specific datasets quickly and economically.
The reality is that while OpenAI kicked off the era of the “democratization of AI,” the open-source community kicked off the era of the “democratization of Software.”
What this means for businesses is that now, instead of defining narrow, “single-feature” products that solve niche pains that have remained unmet by competitors, they 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
Simply put, 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.
Put yourself in your customers’ place. When you’re presented with dozens of solutions simultaneously, how do you understand and evaluate the differences? How can you make long-term decisions if you feel more solutions might be available next month?
Customers would much rather have one “AI partner” that updates its offerings with the latest technology rather than multiple small vendors.
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 (against both new entrants and market leaders) 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.
Ofer Familier is cofounder and CEO at GlossAI.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read More From DataDecisionMakers