Trends in Platforms, AI and BigTech
Michael Spencer
A.I. Writer, researcher and curator - full-time Newsletter publication manager.
Hey Everyone,
Today we get to read a Platform expert, Sangeet Paul Choudary who is based in Singapore and has an excellent Newsletter: Platforms, AI and the Economics of BigTech. He’s a professional CXO Advisor on Platform Strategy and a best-selling author among other accolades.
Sangeet is a frequent keynote speaker at leading global forums including the G20 Summit, the World50, the United Nations, and the WEF.
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Platforms, AI and the Economics of BigTech
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Platforms, AI, and the Economics of BigTech
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So without further adieu, let’s dive into our headline topic.
By Sangeet Paul Choudary , February-March, 2024. This article was first published here .
Winners and losers in the age of Generative AI
How Generative AI transforms competitiveness of solution providers
The Gen AI hype of 2023 is giving way to serious discussions on ROI and competitive advantage.?
Boards and investors are taking note and asking “We get the opportunity, where’s the money?”
In other words, which plays and positions are going to be profitable and defensible in the Gen AI space??
This post deconstructs the Gen AI value chain, calling out opportunities for startups and incumbents across this space.?
The Gen AI value chain
To understand value capture in Gen AI, let’s look at a simple framework for value creation in Gen AI.
In a model I shared in How to lose at Generative AI , I propose a simplified view of the Gen AI value chain:?
There are largely 3 key value drivers across the Generative AI value stack.
Most of the performance advancements that have brought us to the current state of Gen AI in early 2024 have been driven by performance improvements at the model layer and at the compute layer. The value of these performance improvements hasn’t fully translated to massive value creation at the workflow layer.?
1. Incumbents are well-positioned to win at the workflow layer
Some 'platform shifts' favour the disruptors, some favour the incumbents.
Generative AI looks overwhelmingly poised to favour incumbents, and not just because they have access to data.?
Today’s incumbents are yesterday’s upstarts - SAAS (Software-as-a-service) leaders like Hubspot, Salesforce, ServiceNow etc. Today’s dominant Saas providers are best positioned to leverage their installed base and workflow dominance to capture value with Generative AI.
There are two other factors that overwhelmingly favor an incumbent win in Gen AI.?
First, technological shifts work against incumbent advantages if they require a re-architecture of the tech stack and/or distribution channel.?
For instance, startups displace incumbents in the shift from on-premise enterprise software to cloud-hosted SAAS because cloud required the full software stack to be rearchitected. There was no clear path from single-tenant DBs and desktop UX to multi-tenant DBs and browser-based Saas UX.
Even more so, a shift to subscriptions needed a completely different GTM (go-to-market) model with low CAC (customer-acquisition-cost) and higher focus on churn management (vs. high-touch large upfront contracts).
The shift to Generative AI involves none of these displacements.
As I go on to explain in How to lose at Generative AI ,?
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.
Second, incumbents also have a business model advantage.?
As explain in How to lose at Generative AI ,?
Incumbents can subsidize AI by bundling it with an existing already-working profit pool. Startups, instead, need to charge for AI.?
Workflow-only startups that gain traction but don’t own a proprietary model may get stuck with a huge LLM API bill, making AI subsidization untenable even with heavy VC funding.
This is similar to what happened during the rise of IoT (Internet of Things).?
Most incumbents - companies like Schneider, Johnson, and Honeywell - who sold industrial devices bundled connected IoT services with their existing profit pools in devices and effectively subsidized IoT services.
Startups who tried to subsidize their hardware and monetize data were left footing a huge manufacturing bill for subsidizing low quality hardware.
Incumbents, instead, increased the value of their devices by bundling in subsidized connected services - a huge business model advantage.
We’re seeing something similar play out in Gen AI as incumbents leverage their business model advantage in charging for software and merely bundle AI advantages into it without raising prices, thereby reducing churn.?
?? Editor’s Note: Peer Newsletters you might Like:
2. Verticalization is key to winning as a Generative AI challenger/startup
Foundation models are horizontal. They are not focused on deeply solving a narrow use case.?
Challengers and startups can gain initial adoption around verticalization. To verticalize, startups need to unbundle a horizontal foundational model into a vertical use case.
As I explain in How to win at Generative AI :?
领英推荐
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.
Here’s a simple flywheel explaining th
s:
Prompt engineering + well-crafted UX help drive initial adoption.?
However, prompt engineering alone isn’t sufficient. You also need to develop proprietary vertical advantage through model fine-tuning.?
As I explain further in How to win at Generative AI :?
Model fine-tuning helps build proprietary vertical advantage 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.
UX needs to follow in step. 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.
So this is what the flywheel looks like over tim
:?
Verticalization is critical to success at the workflow layer.?
The solutions that will truly differentiate through vertical advantage are the ones which will deeply couple model improvements into UX innovation.
However, verticalization also poses a unique challenge:?
Organizationally, model developers 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.
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.
3. Verticalization is necessary but not sufficient
Verticalization helps gain a competitive position. But vertical players always risk being absorbed into a player that can win horizontally. When a vertical e-commerce player comes up, they always risk competition from a horizontally dominant firm like Amazon. Gen AI startups need to manage similar risk.?
In order to create a defensible competitive position, a vertically dominant player should find a path towards creating a horizontal position.?
This path typically follows four steps through a cycle of unbundling and rebundling.?
Firms that win vertically will need to create a control point that allows them to establish themselves as a workflow hub.?
We already see the advantage of workflow hubs in SAAS. Consider Salesforce, which established a workflow hub position, as explained by a16z here :
Salesforce is a system of record for sales, customer service, and marketing, but it also helps power and enable an entire ecosystem of software applications that serve its end-customers. This is because Salesforce creates and owns the Salesforce Object, which becomes the foundational customer unit of the business and functions as the unique customer identifier for other software applications.?
Imagine a company using Salesforce. The foundational customer unit is the customer ID, or SFID, which is created when a lead enters the marketing system. The SFID is then used by all systems that optimize funnel behavior (e.g. Fullstory, Optimizely, etc.). Once a prospect becomes a customer, Salesforce-owned (Service Cloud) or Salesforce-integrated (e.g. Zendesk) customer-support tools all leverage this common SFID in whatever workflow tool is used to handle queries.??
Gen AI winners will similarly need to establish an AI-native hub position.
4. Winners will need to develop a hub position with AI
How do you develop a hub position with AI?
Taking a framework from How to win at Generative AI :?
There are three key sources of advantage for a hub position. Most players vying for the AI-enabled hub position will need a combination of one or more sources of such advantage.
Focusing on the core decision is key. Whether Predictive AI or Generative AI, AI collapses the cost of decision making either by improving predictions or generating solutions. In order to establish a hub position, you need to build at least one of the above advantages, and ideally all three.
Let’s take Autodesk as an illustrative example of how a company could build these three advantages using AI.
Workflow advantage: In the construction industry, Autodesk has a right to establish a workflow hub position across the Design-Plan-Build-Operate lifecycle of a building. Autodesk has an advantageous position in the Design phase of the lifecycle and could leverage that workflow advantage to act as a lifecycle hub.
A hub position leverages a component of the workflow that other workflows need to plug-in to, because they lie either upstream or downstream from this core decision/creation 'bottleneck' and feed into or feed from it.
Owing to this, the decision hub establishes superior integration value and the right to cross-sell (and possibly even bundle cross-subsidize) upstream and downstream activities and the tools that enable them.
Intelligence advantage: autodesk’s acquisitions of companies operating across the building lifecycle (to create the Autodesk Construction Cloud) provides access to datasets across the lifecycle, further improving its bid for a hub position through building an Intelligence Advantage, in addition to a Workflow Advantage.
As a hub position is established, other data flows from other tools start integrating with it (especially if the position also has a workflow and/or relationship advantage). As more data flows plug in, the hub position’s intelligence advantage improves and its control point over the core decision or core creative action becomes ever stronger.
Relationship advantage: Autodesk owns the relationship with key creators (building designers in the design phase) and to a lesser but still significant extent with the key decision makers (building planners in the plan phase).
Owning the core user relationship (the core decision maker) is key in enterprise software.?
5. Vertical integration across the value chain delivers strongest advantages to the BigTech?
Of all incumbents, the BigTech are best positioned to extend their dominance further.
Revisiting the value chain above, the BigTech play across all layers of the value chain.
At the compute layer, Microsoft, Amazon, and Google benefit from undeniable scale advantages with their cloud computing capabilities.
At the workflow layer, Google’s search dominance and Microsoft’s Office suite are examples of large installed bases that become more sticky when AI-aid is bundled into the workflow.
Finally, all these players are leading investors at the model layer. Google with its in-house efforts and Microsoft with its investment in OpenAI.
Here’s a representative overview from The Strategy Deck:?(by Alex Irina Sandu )
By vertically integrating across all three layers of the value chain, these players get significant advantages both in terms of economics (lowering the costs of inference) and negotiating power (by squeezing out profits from players sandwiched in between their dominant layers).
Most importantly, they gain unenviable control of the end-to-end value chain, giving them the ability to choke vertical players who start gaining a hub position.?
A builders guide to Gen AI
If you’re building in Gen AI, there’s much to learn from previous technological cycles while looking at what’s unique about the current one.?
A few key ideas, in summary:
Biography
CXO Advisor on Platform Strategy to 40+ Fortune 500s | Co-Author, Platform Revolution | 4x HBR Top10 | WEF YGL | Thinkers50 | IIM Distinguished Alumnus Awardee.
Sangeet’s international best-seller Platform Revolution is a Forbes “must-read”, with 300,000+ copies sold globally.
Sangeet Paul Choudary, is the founder of Platformation Labs and the best-selling author of Platform Revolution and Platform Scale. He has advised the leadership of more than 35 of the Fortune 500 firms and has been selected as a Young Global Leader by the World Economic Forum. Sangeet’s work on platforms has been featured on four occasions in the HBR Top 10 Must Reads compilations.
If you like easy to read articles with deep thinking and world-class infographics:
Platforms, AI, and the Economics of BigTech
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Founder & Chief Strategist at Group 8 | Brand Consultant | Public Speaker | Podcast Host | Empowering Global Brands with Strategy, AI & Innovation | #UnboxedWithAJ
8 个月Generative AI is no longer just a buzzword; it's a strategic game-changer. Big companies already in the game have a head start. They can use their existing ways of doing things and relationships with customers to make Generative AI work for them. Unlike in the past, they don't have to completely change how they operate to win here. For smaller players, focusing on specific areas or "verticals" is key. But just getting into one niche isn't enough. They need to build a strong position using AI that's tailored to their customers' needs, their workflows, and their understanding of the market. And then there's the big tech giants. They're already way ahead because they control every step of the process. From the technology itself to how it's used and who gets to use it, they're in charge. That gives them a huge advantage. Whether you're a big player or a smaller one, knowing these dynamics is crucial in today's Generative AI world. It's not just about having the latest tech; it's about using it smartly to stay ahead. #GenerativeAI #BusinessStrategy #TechTrends
A.I. Writer, researcher and curator - full-time Newsletter publication manager.
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