Home News 3 Strategies for AI Startups to Win Against Big Tech

3 Strategies for AI Startups to Win Against Big Tech

3 Strategies for AI Startups to Win Against Big Tech

Constructing defensible firms has grow to be tougher than ever, especially with the emergence of generative AI. Big tech has inherent benefits over startups in each distribution and competitive pricing. Any startup founder knows the nightmare scenario: waking as much as a giant company in your space offering a competitive recent feature or product. And it’s free. And they’ve bundled it with their already widely distributed offerings.

But AI startups who make just a few key decisions early can insulate themselves from this threat, and grow to be true disruptors by leveraging the benefits they’ve over Big Tech.

Compete in a product category that’s AI-native

One strategy for AI startups to win against Big Tech is to deal with product categories which can be 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.

The truth is, this is precisely the dynamic that brought lots of today’s fundamental players in tech to prominence, as identified by Clayton Christensen in his landmark book, The Innovator’s Dilemma. This time around, nevertheless, they’re the incumbents.

Let’s take the instance of search. It’s clear that LLMs will change the way in which users seek for  answers to their questions. When someone goes to go looking for something, they usually are not actually on the lookout for a listing of weblinks. They’re on the lookout for answers to questions, or specific products, places or people. This is the reason LLMs stand out as potential search engine killers.

For a search engine company to switch the core flows of its experience is to risk losing users and billions of dollars in revenue. Nonetheless, in the event that they opt to not transition to a chat style interface, they open themselves up entirely to recent competitors. In each cases, they’re at a drawback to your startup’s AI-native product.

Product categories that may truly embrace generative AI-native innovation are data-driven, and cater to a wide selection of specialised use cases. A couple of examples of categories that appear to be  AI-native include search, advice engines, or legal and medical technology.

Feature density as a differentiator

Traditionally, startups and small teams would deal with a distinct segment and develop just a few very worthwhile features that service a well-defined audience. Larger firms with larger 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 wealthy set of features that provide value for purchasers. Even small innovations at this stage yield massive value for users. And in contrast to a big, established tech company, they usually are not slowed down by compliance constraints and bureaucratic red tape. This permits them to ascertain a foothold and gain momentum before Big Tech can catch up.

Perhaps the largest advantage of specializing in feature density and time to market is the rapidly evolving nature of AI technology. Recent models, faster models, more use cases. Just up to now few months, we’ve seen OpenAI, for instance, speed through their GPT3, GPT3.5 and GPT4 models, while releasing DALL-E 2, ChatGPT, and opening up API access, enabling one other order of magnitude of innovation. By January of 2023 we saw Microsoft running as fast as they may to take a position in, not compete with, OpenAI.

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

Find and own a brand new product category

AI solves a variety of problems. This, in turn, creates recent, unexpected ones. Discovering one in every of these recent problems leading to a shift in technology or customer behavior isn’t easy, but when done right, can put an organization in pole position – ahead of any larger player.

How AI works and functions as a component in peoples’ day-to-day lives remains to be an open query. We’re all in AI kindergarten. Startups who’re near their market, keenly listening for the issues that arise consistently from the initial implementation of their technology, can quickly assess and construct solutions for these emerging challenges.

As an illustration, as AI-powered chatbots grow to be 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 brand new standard within 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 brand new problem: knowing which content to publish. Solving this recent problem was key for Anyword to construct, not only a feature, but a whole offering centered around generating effective content, and providing tools to investigate and manage copy that support marketers’ workflows and goals.

By identifying these emerging problems and offering progressive solutions, startups can establish themselves as pioneers in recent AI categories, cementing their position as disruptors out there.


Please enter your comment!
Please enter your name here