A couple of weeks back, I wrote a post on How to lose at Generative AI.
Which raises the counter-question, how do you win at Generative AI?
Of the many GenAI copycats emerging, which are the few that win?
This post is about winning at Generative AI.
It’s long.
Let’s grab that skinny latte before we get into this one.
…
Let’s do this!
The devil lies in the details.
But let’s soar at 30,000 feet for a bit before we sky-dive into the details.
Every shift in technology creates an opportunity for value migration.
The current hype in Gen AI – fuelled largely by the all-knowingness of ChatGPT – is largely focused on the foundation model layer. Foundation models, however, are context-agnostic – a feature, and not a bug, of the all-knowingness that they come with.
I believe that we will see new value plays in Gen AI as value migrates away from the foundational model layer.
Firms that exploit this value migration to their advantage will need to follow a four-step process:
Here’s how this evolution plays out across the value chain.
A foundational model takes the market by storm and establishes horizontal dominance at the model layer.
At this stage, a range of ‘startups’ emerge at the workflow layer to verticalize this horizontal model. We’ve seen this play out over the past year as a whole host of AI ‘wrapper’ copycats emerge. The value continues to sit entirely at the model ‘horizontal’ during this step.
Unbundling starts:At this point, these ‘startups’ have started making their first feeble attempts at unbundling a horizontal foundational model into a vertical use case.
A few of these ‘startups’ may develop some sort of vertical advantage (a proprietary fine-tuned model, access to vertical data sets, a vertical-specific UX advantage, or some combination of all the above) and leverage that advantage to pull value into a vertical play.
Successful unbundling: At this point, a few successful unbundlers emerge with the right to play and win vertically. The other AI ‘wrapper’ unbundlers rapidly get commoditized if they rely largely on the foundational model, without any proprietary advantage of their own.
This is the key step.
Having developed vertical advantage, a very small number of the firms that succeed in Step 2 will find themselves owning a key control point through their vertical specialization – an advantage that gives them primacy of user relationship in that vertical. I’ll get into the nature of these control points and how to build one.
These few firms will leverage this newly developed control point to start rebundling ‘over the top’.
Rebundling starts: At this point, a select few ‘startups’ emerge laying claims to the right to rebundle value at a higher (workflow) layer.
As rebundling progresses around the control point, this ‘startup’ now emerges as the hub into which other players connect. It successfully creates an ‘over-the-top’ layer coordinating across multiple capabilities.
Successful rebundling: And with that, we have the new winner. Value is successfully rebundled and migrates to this new layer (and player).
This inevitable cycle of unbundling and rebundling is key to value migration when a new tech like Gen AI comes in. I’ll explain shortly how this cycle determines winners and losers and how to use it to your advantage.
As you’d have figured, this post is about going from wrapper to winner; we’re not talking about value at other layers of GenAI tooling. There will be winners in LLMOps and Compute, determined largely by engineering, scale, and talent advantages. But this post is about winning while delivering GenAI into a product.
Let’s now dive into the juicy details.
What exactly do wrappers do?
They try to unbundle. And they’re pretty mediocre at it!
Foundational models are horizontal. User needs are vertical.
Foundational models claim to have a probabilistic answer to just about everything.
They’re fascinating to the casual user as they seem to have an opinion on everything. This partly explains why ChatGPT was the fastest ever to get to 100M users and why lazy conversations at weekend dinner parties have shifted from discussing Sam Bankman-Fried’s wardrobe choices to ‘Is ChatGPT going to eat your job?”.
Yet, foundational models are not as useful to the specialized user. Getting them to produce the specific output that is most useful to you requires a very specific set of prompts.
Enter the world of prompt engineering – a fancy term to stumble your way (often through trial and error) to a prompt that works best for delivering the most useful and usable output.
A well-structured prompt unbundles a foundational model into an end user use case.
A wrapper essentially packages this unbundling in a user-friendly interface.
So wrappers verticalize a horizontal foundational model.
But this verticalization doesn’t really amount to successful unbundling unless the wrapper creates a proprietary vertical advantage.
So how do wrappers actually develop proprietary vertical advantage?
Here’s the simple GenAI wrapper playbook for developing vertical advantage:
Here’s a simple flywheel explaining this:
Prompt engineering + well-crafted UX help drive initial adoption. A wrapper turns the end-user’s non-sophisticated input into a well-defined prompt (largely collapsing end user costs of iterating to the right prompt through trial and error), which helps generate a highly useful and usable output.
Vertical solutions like Harvey, Ironclad, and Bloomberg GPT largely follow this playbook to build vertical advantage around their initial ‘wrapper’ unbundling.
Model fine-tuning helps build proprietary vertical advantage around what started as a basic wrapper by:
Smaller models, trained on domain-specific data deliver better performance on latency, accuracy, and cost than larger foundational models. This verticalization has its own reinforcing feedback effect. The more you develop vertical advantage, the more competitive you get on all parameters.
To deliver the most compelling vertical solution over time, the more the model is fine-tuned, the more deeply coupled future UX changes should be with the model in order to deliver the benefits of that model into the user workflow.
And this brings us to an interesting conundrum in developing vertical advantage with Gen AI.
The solutions that will truly differentiate through vertical advantage are the ones which will deeply couple model improvements into UX innovation.
Late followers that lack model fine-tuning will struggle to deliver the same UX innovation without a fine-tuned model backing it.
This coupling sounds easy but can be very difficult to deliver. And therein lies a hidden source of advantage that separates the losers from the winners in vertical solutions.
But why will companies struggle to deliver model improvements into UX? Why is this a source of advantage at all?
Organizationally, model wizards are fairly removed from UX designers. The vast majority of really good model engineers and data scientists are horizontally oriented. They focus on building models that deliver on scale and scope. This is an advantage while building foundational models. But this is orthogonal to developing deep empathy for a customer pain point, which is required to deliver model fine-tuning advantages into the UX.
This is what this orthogonality looks like.
What’s more – most model engineers have cut their teeth building foundational models, far removed from use cases that require deep customer empathy to win. These model engineers are increasingly going to be hired into companies looking to develop vertical advantage. A minority will succeed at engaging deeply with UX designers to create a tight coupling between the two layers.
In order to develop strong vertical advantage in Gen AI (and all forms of AI in general), you will need to deliver an org model that ensures rapid development cycles in teams comprising model engineers and UX designers.
So that’s how you win as a Gen AI wrapper… by developing true vertical advantage.
Developing vertical advantage as a point solution is only the second step towards establishing a winning position with Gen AI.
The magic lies in what follows an advantageous vertical position…
To understand that, we first need to look at rebundling
As a vertical, point solution, you’ve now delivered successful unbundling.
Most ‘disruption’ of the status quo happens through unbundling. But most venture returns are realized through rebundling.
Fintechs specializing in one activity unbundle banks, Healthtech firms specializing in one aspect unbundle healthcare providers, and specialized energy startups unbundle utilities.
Yet, there is no sustainable value capture in unbundling. Unbundling unseats incumbents but doesn’t create scalable and defensible value pools.
That is achieved through rebundling. Rebundling involves bundling multiple unbundled capabilities into a cohesive customer-centric offering.
Most important, the successful ‘rebundlers’ establish a hub position and gain primacy of user relationship.
Venture capital chases unbundling because unbundlers hold the promise of rebundling and capturing value. Yet, most venture money is lost because a tiny handful rebundle.
Square, Plaid, Stripe are a few of the unbundling fintechs that successfully rebundled and dominated one or more horizontal layers in the value chain. Most others fell by the wayside.
The winners in Gen AI will likewise need a path to successful rebundling.
So how exactly does rebundling work in Gen AI?
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