How value pools in Gen AI will evolve- lessons from last 4 platform shifts
Introduction
AI powerhouses like OpenAI and Anthropic have built massive AI infrastructure (called foundational models), offering models through APIs, UI and licenses. At this stage, they primarily earn revenue by charging for API calls or licensing deals, similar to how cloud providers rent out compute resources. So far, most model provider companies have stayed away from going deep into the application layer- hence we don’t seen Open AI compete in CRM market, or Anthropic build contact center applications.
However this may soon change, and the model providers are likely to move up the value chain in the AI stack. This trend has played out numerous times in other technology eras: personal computing, the internet, cloud migration and finally the mobile revolution. In all of these, infrastructure was initially king, but eventually, applications and platforms claimed most of the profits.
Why AI Companies Will Shift to Applications
Source - Apoorv Agarwal
Tech Reasons: AI Models Are Becoming Commodities
A few years ago, GPT-3 was a breakthrough few could match. Today, multiple research labs and open-source communities have produced similar models—some even rivaling proprietary offerings. The result? Capabilities once unique to GPT-4o are becoming broadly available. As the core model layer turns into a commodity, unique user experiences, proprietary data, and specialized workflows become the true differentiators.
Economic Reasons: Infrastructure Prices Are Collapsing
This mirrors the fate of cloud computing infrastructure. Amazon Web Services, Microsoft Azure, and Google Cloud once enjoyed high margins by selling raw compute. Over time, however, competition intensified, leading to lower prices. Meanwhile, software-as-a-service (SaaS) vendors built massive businesses on top of that cheaper infrastructure, enjoying robust margins and subscription revenue. AI model providers are now at risk of the same outcome if they remain solely at the API layer.
Business Reasons: Applications Capture More Value
When you only provide an API, you lack direct control over the end-user experience. In contrast, companies that build applications:
Own the Customer Relationship: This fosters strong brand recognition and higher margins.
Collect User Data & Feedback: Improves product quality and creates a virtuous cycle.
Create Workflow Integration: Embedding AI deeply into user workflows encourages loyalty and locks out competitors.
OpenAI’s ChatGPT illustrates this shift. It’s widely known by users worldwide—far more than any underlying model name (GPT-4). This brand recognition and direct channel to users highlight how an application can outshine the infrastructure behind it.
History Repeats: Lessons from Past Tech Shifts
1. Computing Era: From Hardware to Software Dominance
Personal computing provides an even earlier example of this dynamic. Companies like Intel and Dell led the hardware side, benefiting from the initial PC boom. Intel’s processors and Dell’s “build-to-order” PCs were foundational to computing in the 1980s and 1990s, capturing strong margins for a while. However, as hardware designs became more standardized and competition increased, profit margins on hardware dwindled.
In contrast, Microsoft emerged as a software giant by providing the Windows operating system and Office productivity suite, both of which became essential in homes and offices worldwide. While Intel and Dell still found success, Microsoft’s software focus yielded better long-term returns. Owning the operating system meant owning the platform where all the applications ran, resulting in huge network effects and near-monopoly conditions.
The Big Takeaway: Infrastructure—whether it’s cables, servers, hardware, or raw AI models—eventually faces commoditization. Over time, the highest profits, brand loyalty, and user engagement shift to those controlling applications, platforms, and ecosystems.
2. Internet Era: From Network Providers to Web Giants
In the 1990s, internet service providers (ISPs) seemed like the future of tech. They owned the “pipes” that carried data. At first, these ISPs profited handsomely, charging premium prices for internet connectivity. However, as the internet expanded, true value shifted up the stack to content and services. Google, Facebook, and Amazon became household names by delivering search, social networking, and e-commerce on top of the raw connectivity. ISPs were relegated to a “dumb pipe” role, leaving them with thinner margins.
A major contributing factor was that once the infrastructure was built, the barriers to entry for new online services were relatively low. Telecom companies found it harder to differentiate their pipes, while web companies innovated rapidly in areas like search, advertising, social media, and retail. Ultimately, the web giants captured more user attention and monetized through ads or sales. The ISPs, despite handling all the data, did not see comparable profit growth.
3. Cloud Computing: From IaaS to PaaS to SaaS
The cloud revolution further showcases how infrastructure eventually yields to applications.
IaaS (Infrastructure-as-a-Service): In the early 2010s, AWS, Azure, and GCP dominated by offering virtual machines, storage, and basic computing resources. Margins were initially high, thanks to on-demand pricing and a lack of direct competition.
PaaS (Platform-as-a-Service): To stand out, providers began bundling more advanced services like managed databases, serverless computing, and developer tools. This allowed them to differentiate from basic IaaS, providing a simpler way for developers to build and deploy applications.
SaaS (Software-as-a-Service): Eventually, true value accrued to companies that delivered complete software solutions. Providers like Salesforce, Zoom, and Shopify built on top of cloud infrastructure, capturing recurring subscription revenue and forging strong customer relationships. AWS responded by moving further into the application space with services like Amazon Connect (call center solutions) and AI-driven offerings, trying to claim more of the higher-level value.
Over time, PaaS and SaaS layers witnessed faster growth and better margins than raw infrastructure. The same pattern threatens pure-play AI infrastructure providers. If they do not move upward—offering end-user applications or integrated solutions—they risk being undercut on price.
4. Mobile internet Era: Semiconductors vs Hardware vs. Apps
Source- Morgan Stanley report
Initially, it looked like hardware companies—particularly Nokia, Motorola, and chip manufacturers like Qualcomm—would dominate mobile. Their devices and components were essential. However, with the launch of the iPhone (2007) and the Android ecosystem, Apple and Google shifted the locus of power to operating systems and app stores. Users cared more about the software experience and the availability of apps than the specific chip inside their phone.
Apple’s App Store and Google’s Play Store quickly became massive marketplaces, each taking a significant cut of all app transactions.
Device makers (except for Apple, which controlled both hardware and software) saw margins shrink, often competing in a race to the bottom on hardware prices.
By controlling the app platform, Apple and Google could leverage network effects, data, and developer ecosystems—capturing more profit than the companies merely building phones or chips.
Real-World Case Studies
Telcos vs. Over-the-Top (OTT) Services
Telecom companies once owned internet access, so they assumed they would dominate in the long run. But OTT services like WhatsApp, Skype, and YouTube eroded telecom revenues by handling voice, messaging, and video over the top of basic internet connectivity. By 2018, telcos had lost $386 billion in revenue to OTT apps, operating at about 10% margin, while the app companies often hovered around 30% margins.
AWS vs. SaaS Growth
Amazon Web Services remains a leader in IaaS, generating huge revenues. But software companies built on top of AWS—such as Snowflake and Shopify—have managed to capture large market caps and enjoy high profit margins. Snowflake’s specialized data cloud solution, for instance, boasts margins near 77%. Meanwhile, AWS invests heavily in new data centers and competes on price, leading to shrinking margins.
Apple vs. Nokia: The Mobile Battle
In the smartphone arena, Apple consistently captures over 80% of the industry’s profits, despite having a fraction of the market share in terms of device shipments. This staggering profitability is largely due to Apple’s control of both hardware and software, plus its thriving App Store ecosystem. Meanwhile, Nokia, once the world’s leading phone manufacturer, lost its dominance when it failed to pivot effectively to a software-centric business model.
The Bottom Line
If you don’t move up the stack, you risk being left behind. Raw infrastructure inevitably faces price competition, eroding margins and forcing providers to scale just to stay afloat. The real wins occur at the application layer, where brand loyalty, recurring revenue, and direct user engagement create sustainable competitive advantages.
What This Means for AI’s Future
The AI Industry Shake-Up
Model providers will likely expand into end-user applications, likely building applications for deeper use cases such as customer support, accounting, CRM, etc. Startups relying solely on external APIs may suddenly find themselves competing with the same providers they once depended on. Simultaneously, enterprise giants like Microsoft and Google are embedding AI deeply into their software suites—Office, Gmail, Google Workspace—tightening their grip on user experience.
Who Wins & Who Loses?
Winners will be organizations that build distinctive applications, own their user data, and create sticky user experiences. Losers will be the API-only businesses that fail to keep pace with open-source models and integrated AI solutions.
Advice for Businesses
Go Beyond APIs: Deliver solutions that solve real user problems in a seamless way. Focus on user experience, proprietary data, and ecosystem building.
Build AI-Powered Applications: The future isn’t in mere access to a language model. It’s in software that incorporates AI to provide tangible business or consumer value.
Embrace Open-Source: Open-source AI tools and models are growing fast. They can reduce costs and give you flexibility—allowing your business to differentiate at the application layer rather than just re-selling someone else’s API.