The moat paradox: rediscovering competitive advantage for AI success

Building a pure technology ditch has become a challenge since the emergence of large language models (LLMs). Due to the lower barriers to entry for introducing new products to the market and the constant fear of becoming obsolete overnight, existing companies, startups and investors are all trying to find a path to sustainable competitive advantage.

However, this new landscape also offers the opportunity to create a different kind of moat, one based on a much broader product that solves multiple customer pain points and automates major workflows from start to finish.

The AI ​​explosion, whose blast radius has continued to grow since the public launch of GPT3.5/ChatGPT, is astonishing. In addition to the debates about efficiency and risk, companies in the space have been relentlessly confronted with the question of whether building a technology ditch is still possible.

Companies grapple with the reality of creating a defensible product with substantial barriers to entry for new competitors or incumbents. As in the past, this will continue to be a necessary component for a new company to grow and become a centaur or unicorn.

Open-source models are the real revolution

The real revolution is not just ChatGPT. The real revolution involves making open source models available for commercial use – for free. In addition, solutions such as LoRA enable anyone to quickly and economically retrain open-source models on specific datasets.

The reality is that while OpenAI ushered in the “democratization of AI” era, the open source community ushered in the “democratization of software” era.

What this means for companies is that now, instead of defining narrow, “single-feature” products that solve niche problems that competitors haven’t yet met, they can now listen to their customers on a much broader scale and develop broad products. being able to deliver solutions that solve multiple problems that seemed unrelated a year ago. When combined with integrations that fully automate customer workflows, companies can truly gain a sustainable competitive advantage.

Put yourself in the place of your customers

Simply put, to stand out, companies need to connect the dots between problems, find solutions no one else has considered, and then find additional dots to connect.

Put yourself in the place of your customers. How do you understand and evaluate the differences when you are presented with dozens of solutions at the same time? How can you make long-term decisions if you think more solutions will be available next month?

Customers prefer to have one “AI partner” updating their offerings with the latest technology, rather than multiple small vendors.

Executing this strategy requires setting a broad vision and much shorter, focused cycles across the organization in product development and company-wide synchronization. For example, ML/AI teams should be part of weekly sprints. This allows them to add new AI features more efficiently and make decisions about adding new LLMs or open source models to improve or enrich the offering within the same time frame.

Building broader AI products

Building a broad product, rather than one focused on a single function, helps startups achieve this mythical moat, as it simplifies product adoption, creates further barriers to entry (against both new entrants and market leaders), and protects against new open doors. source models that could be released and take down a company overnight.

Let’s look at the AI ​​transcription market (ASR) as an example: there were several providers in this market with similar price points and relatively nuanced product differentiations. Suddenly this seemingly sleepy market was rocked when OpenAI released Whisper, an open-source ASR, which showed immediate potential to disrupt the market, but with some substantial gaps. Faced with the above dilemma, the incumbents in the market each decided to launch a new proprietary model and focused some of their messages on Whisper’s problems.

At the same time, others found ways to close these gaps and bring a superior product to market with limited R&D effort receiving incredible feedback from business customers and a starting point with satisfied customers.

Going back to the original question, can one build a moat in the AI ​​space? I believe that with the right product vision, agility and execution, companies can build a rich offering and, over time, compete with industry leaders. Many of the core principles needed to identify great startups are already inherent in VC heads who understand what it takes to spot opportunities and grow accordingly. It is 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 entire kingdom.

Ofer Familier is co-founder and CEO of ShineAI.