3 Strategies for AI Startups to Win Against Big Tech

3 Strategies for AI Startups to Win Against Big Tech

Building defensible companies has become tougher than ever, especially with the emergence of generative AI. Big tech has inherent advantages over startups in both distribution and competitive pricing. Any startup founder knows the nightmare scenario: waking up to a big company in your space offering a competitive new feature or product. And it’s free. And they’ve bundled it with their already widely distributed offerings.

But AI startups who make a few key decisions early can insulate themselves from this threat, and become true disruptors by leveraging the advantages they have over Big Tech.

Compete in a product category that is AI-native

One strategy for AI startups to win against Big Tech is to focus on product categories that are AI-native. What does this mean? While Big Tech may add some AI functionality to their existing products, their users, their developers, and their product roadmaps are all focused on servicing these existing user flows. Modifying these flows comes with inherent risks.

In fact, this is exactly the dynamic that brought many of today’s main players in tech to prominence, as identified by Clayton Christensen in his landmark book, The Innovator's Dilemma. This time around, however, they are the incumbents.

Let’s take the example of search. It's clear that LLMs will change the way users search for  answers to their questions. When someone goes to search for something, they are not actually looking for a list of weblinks. They are looking for answers to questions, or specific products, places or people. This is why LLMs stand out as potential search engine killers.

For a search engine company to modify the core flows of its experience is to risk losing users and billions of dollars in revenue. However, if they opt not to transition to a chat style interface, they open themselves up entirely to new competitors. In both cases, they are at a disadvantage to your startup’s AI-native product.

Product categories that can truly embrace generative AI-native innovation are data-driven, and cater to a wide range of specialized use cases. A few examples of categories that seem to be  AI-native include search, recommendation engines, or legal and medical technology.

Feature density as a differentiator

Traditionally, startups and small teams would focus on a niche and develop a few very valuable features that service a well-defined audience. Larger companies with bigger dev teams could bring more features to market, faster.

With Generative AI, the bottleneck of development has moved from coding to product and UX. An agile startup can move faster to bring to market a rich set of features that provide value for customers. Even small innovations at this stage yield massive value for users. And unlike a large, established tech company, they are not slowed down by compliance constraints and bureaucratic red tape. This allows them to establish a foothold and gain momentum before Big Tech can catch up.

Perhaps the biggest advantage of focusing on feature density and time to market is the rapidly evolving nature of AI technology. New models, faster models, more use cases. Just in the past few months, we’ve seen OpenAI, for example, speed through their GPT3, GPT3.5 and GPT4 models, while releasing DALL-E 2, ChatGPT, and opening up API access, enabling another order of magnitude of innovation. By January of 2023 we saw Microsoft running as fast as they could to invest in, not compete with, OpenAI.

As the field continues to evolve and mature, startups that can differentiate and innovate will have a leg up over larger competitors who may struggle to adapt to the changing tech landscape.

Find and own a new product category

AI solves a lot of problems. This, in turn, creates new, unexpected ones. Discovering one of these new problems resulting in a shift in technology or customer behavior isn’t easy, but if done right, can put a company in pole position – ahead of any bigger player.

How AI works and functions as an element in peoples’ day-to-day lives is still an open question. We are all in AI kindergarten. Startups who are close to their market, keenly listening for the problems that arise consistently from the initial implementation of their technology, can quickly assess and build solutions for these emerging challenges.

For instance, as AI-powered chatbots become popular, some users voice concerns about privacy and data security. A forward-thinking startup could tackle this emerging problem and develop an AI solution that implements advanced encryption and data anonymization techniques, assuaging users' fears and setting a new standard in the industry.

In my company’s case, it was noticing that, though marketers were overjoyed to have the nearly limitless copy variations AI makes available to them, there was a new problem: knowing which content to publish. Solving this new problem was key for Anyword to build, not just a feature, but an entire offering centered around generating effective content, and providing tools to analyze and manage copy that support marketers’ workflows and goals.

By identifying these emerging problems and offering innovative solutions, startups can establish themselves as pioneers in new AI categories, cementing their position as disruptors in the market.

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