However, the real game-changing potential lies less in the ability to generate new content and more in the fact that in LLMs we now have the ability to understand and analyse content in a much more accurate, generalisable and scalable way than has ever been possible, which means many tasks that could only be done by humans can cost effectively be automated.
Over the next few years, we’ll see more and more processes automated using LLMs which will likely improve efficiency across a whole range of verticals, particularly those like insurance, law and medicine where, due to the volume of unstructured data, automation has been difficult. The potential to boost productivity in huge sectors such as these justifies investors’ excitement.
That said, the ease with which LLMs can be leveraged to build simple automations, often in minutes or hours, has led to lots of hype with an explosion of startups leveraging LLMs to deliver solutions such as copywriting (Jasper, Copy.ai, Rytr), chatbots (Character.ai, Heyday, Chai) or sales assistants (Outplay, Regie.ai, Exceed.ai). Here, the ease of building with LLMs, often through providers such as OpenAI or Anthropic, means that products can be built and launched very quickly, which will lead to a plethora of startups and lots of noise. We’re already seeing that in some spaces like sales assistants, where there are a lot of companies building similar solutions and it’s difficult to choose between them.
There are three problems with this wave of startups:
- While the analysis and automation provided by LLMs might be key functionality, it likely won’t be sufficient on its own without workflow, UX, integrations etc. and building that functionality will be harder than integrating output from someone else’s LLM. This means that incumbents, who have already built those things, will be better placed than new entrants.
- Just incorporating an LLM does nothing to provide the defensibility that is needed to build a venture scale business. If you can quickly automate a process using an off the shelf LLM, so can your competitors.
- The technology is developing so rapidly that there is a risk of building capabilities that are quickly superseded or rendered obsolete as technology improves.
This means that although generative AI will likely significantly impact the way many jobs are done with big productivity gains, many of the first-generation generative AI startups will fail. The challenge for VCs is to find companies that are leveraging generative AI in a way that allows them to build a defensible business.