GenAI’s Platform Play: How the New ‘Engine’ is Built on Yesterday’s Cloud

We are witnessing another shift in computing platforms — after the client-server era, the web, and mobile/cloud. Each previous shift created trillion-dollar opportunities (think Google for web, iOS and Android for mobile), redefining business landscapes entirely.

• Web shifted control to browsers and search engines.

• Mobile shifted control to app ecosystems and app stores.

• Cloud shifted control to infrastructure-as-a-service providers.

Today, the generative AI shift isn’t just another evolution — it’s fundamentally altering how value is created, captured, and delivered. Unlike previous platforms designed primarily for consumption, Generative AI platforms are built for creation, enabling anyone, from tech giants to small startups, to rapidly generate high-quality content, code, media, and more.

But what truly differentiates this shift is the emergence of a stable, universal model layer. For the first time, foundational models (like GPT-4, Gemini, LLaMA) offer a consistent, reusable “platform engine,” akin to what SQL databases once provided for structured data.

Think back to when databases first appeared. Before SQL databases, every company stored data in a different, messy way. SQL standardised how data could be organised, queried, and used — so you didn’t have to rebuild everything from scratch. This made it cheap and fast to build applications on top.

Generative AI is doing something similar for intelligence and content creation.

Before, every application needed its specific logic, templates, or algorithms. Now, a single foundation model (like GPT-4 or Gemini) can do many tasks — writing, summarising, reasoning, generating code — with consistent quality. You just prompt it, instead of building the intelligence piece by piece.

Is this Platform Shift changing previous platforms?

This platform shift is built directly on top of the previous platforms.

Generative AI would not be possible without massive cloud data centres, specialised GPUs/TPUs, and global network capacity — all provided by the last wave of cloud and hyperscaler platforms (like AWS, Azure, Google Cloud). GenAI models are trained and deployed on these foundations.

The way GenAI features are delivered — via API calls, integrations, and SaaS apps — relies on the plumbing built in the previous platform shift. The whole “prompt-to-output” flow works because of robust cloud-based APIs and modular software stacks.

GenAI isn’t replacing the old stack; it’s amplifying it. Adoption is faster because the groundwork is already done.

This platform shift is fundamentally changing previous platforms

SaaS apps were built around fixed workflows, manual input, and static dashboards. Now: GenAI is baked in — think copilots that generate content, automate tasks, and answer questions contextually inside your CRM, docs, or analytics platform.

User Interfaces Are Being Redefined

• Then, UI/UX was click-based, menu-driven, and rigid.

• Now: Prompt-based, conversational, and adaptive — users talk to software, describe problems in plain language, and expect instant, personalised results.

Designers are rethinking interfaces for natural language, multimodal input, and real-time AI feedback.

Where will this go?

When SQL databases became standard, companies faced a choice: Do we run our database (for control/privacy), or do we use a managed/cloud service (for speed and scale)? Do we use open-source (flexibility, community), or go with a vendor (support, premium features)?

The same choices are coming with GenAI:

1. Some will want AI running privately for security.

2. Some will use cloud AI for convenience.

3. Some will pick open models to customise.

4. Some will pay for proprietary models for the best results.

AI running privately

For businesses where data privacy, regulatory compliance, or intellectual property protection is non-negotiable, running GenAI models within a private environment (on-premises, virtual private cloud, or even on-device) becomes a strategic moat. For example, Hospitals using private LLMs for medical records summarisation, patient chat, or diagnosis support

Cloud AI for convenience

Companies prioritise speed, scalability, and lower operational overhead by leveraging GenAI as a managed service in the cloud. This approach suits fast-moving teams and broad deployment needs. For example, E-commerce: AI-powered customer support chatbots, real-time translation, or personalised product descriptions delivered at a global scale.

Open Models for Customisation

Organisations or innovators seeking maximum flexibility, transparency, or cost savings choose open-source GenAI models. This unlocks solutions and fosters community-driven advancement. This we have seen over the years.

Just as most companies today use both local and cloud databases, the GenAI stack will be mixed

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About Me

Hey, I’m Shailesh Sharma! I help PMs and business leaders excel in Product, Strategy, and AI using First Principles Thinking. For more, check out my Live cohort course, PM Interview Mastery Course, Cracking Strategy, and other Resources