How to lose at Generative AI!

Value capture in Generative AI is going to be a disappointment for most startups!

Some ‘platform shifts’ favour the disruptors, some favour the incumbents.

Generative AI looks overwhelmingly poised to favour incumbents. And, of course, why not – you’d say – incumbents have all the data advantage.

Now, that’s a good quick argument win the startup-incumbent debate at a dinner conversation. But the real reason incumbents win and startups lose with Gen AI is a lot more nuanced.

So where exactly do today’s tons of AI startups lose? And what are the few places where they win?

Let’s dive right in and unpack this further.

Getting Gen AI wrong!

The Generative AI hype is just about subsiding.

Dozens of newsletters helping you prompt better mushroomed over the past 6 months. Dozens others were set up for the sole purpose of recommending products with .ai domains, some of which were mere prompt engineering wrappers while others were rule-based automations hosted on .ai URLs.

But once we’re past this hype, where exactly is that pot of gold at the end of the GenAI hype rainbow?

To understand value capture in Gen AI, let’s look at a simple framework for value creation in Gen AI.

Value creation in Generative AI

There are largely 3 key value drivers across the Generative AI value stack.

1. Compute: The massive resources needed to run large language models (LLMs)
2. Model: The learning and the memory powering GenAI
3. Workflow: The context into which GenAI is served

 

 

The workflow vs application distinction is important. A lot of folks think about the model layer and the application layer as two distinct layers, but that distinction is not very precise.

An application could be workflow-only i.e. working with a third party model and serving AI integration into the workflow. Or an application could be model+workflow, meaning you back your AI integration into workflow with a proprietary contextually fine-tuned model.

A tale of two platform shifts

VC dollars are pouring into the GenAI gold rush.

Most Gen AI startups today are jumping in with the ‘cloud/saas’ hat on. There’s a new ‘platform shift’. Time to make hay building new applications.

Most value in the shift to Cloud was captured by new SaaS startups, not by on-premise incumbents.

Incumbents needed a full-stack rebuild:

1) There was no clear path from single-tenant DBs and desktop UX to multi-tenant DBs and browser-based Saas UX.
2) Cloud required the full software stack to be rearchitected.
3) Most importantly, a shift to subscriptions needed a completely different GTM model with low CAC and higher focus on churn management (vs. high-touch large upfront contracts).
4) Your CFO needed to understand all this.

As a result, incumbents lost out on the transition to cloud. A whole generation of Saas startups emerged to capture value at the application layer.

Today’s GenAI startups apply the same logic when they look to bundle some cute prompt engineering with UX improvements to create an AI-aided workflow, running on one of the LLMs.

But a lot of the four factors above DO NOT apply to Gen AI.

In fact, Generative AI overwhelmingly favors the incumbents:
1) The model API distribution approach jives well with cloud incumbents, who already have primacy of relationship with an installed base (especially in B2B) and, hence, access to workflow. Further, the proliferation of integrators like HuggingFace, means quick plug-and-play deployment without rearchitecting the stack.
2) In most cases, AI can be embedded into existing workflows, making incumbents’ workflow ownership a huge advantage.

Today’s incumbents are yesterday’s upstarts – Hubspot, Salesforce, ServiceNow etc. In fact, the very Saas players that scaled handsomely during the shift to Cloud are now best positioned to leverage their installed base and workflow dominance to capture value with Generative AI.