Your Model Is My Commodity

Why the Next AWS Will Be Context, Not Compute

By now, the arc of large language models (LLMs) is starting to feel eerily familiar.

A foundational breakthrough. A Cambrian explosion of startups. A few hundred million dollars raised for a tool that can summarise your emails, hallucinate with confidence, and occasionally help your lawyer get disbarred. The cycle repeats. What looked like magic last year is infrastructure this year. Just like AWS a decade and a half ago.

Amazon didn't invent servers. It just made them invisible.

What we are witnessing now with LLMs and more quietly, with LVMs (Large Video Models) is the same commoditisation curve. The early years are messy, GPU-heavy, dominated by hardware chokeholds, clever caching, and thousand-token dances. But inevitably, they flatten into abstraction. The stack settles. The API becomes the product. And before you know it, every brand, startup, and telco is quietly piping inference into their workflows like it is electricity.

Which brings us to the central thesis: LLMs and LVMs are not just transformative, they are becoming inevitable. The same way a cloud server or a payments rail is inevitable. They will disappear into the plumbing. Every enterprise will run a model (or rent one). Not because it's a differentiator, but because not having one will feel like dial-up.

The New Internet Plumbing

The big tech firms know this already. Meta's open-source push, Google's Gemini stack, OpenAI's quiet shift toward agents and APIs: they are not battling over consumer apps. They are vying to be the layer every business builds on. We have seen this happen before: Android vs iOS, Azure vs AWS vs GCP. Only this time the battlefield is not the OS or the cloud; it's cognition.

But while LLMs get all the headlines, it is the quieter rise of LVMs - models trained on video, motion, cultural nuance, that tells us what comes next.

Language can be summarised. Video must be understood.

And therein lies the moat.

The Context Moat

In the race to commoditise models, two things stand out.

First, the foundation models are not the endgame. They are the plywood, not the furniture.

Second, models are only as good as their context. Especially in video.

An LVM trained on English-language YouTube explainer videos will fail miserably when asked to interpret a TikTok of a Tamil auntie explaining kitchen hacks with six gestures, a laugh track, and zero verbal explanation.

And yet, that is where billions of minutes of commerce, education, and behaviour are headed.

Every brand that wants to automate product tagging, recommendation, sentiment understanding, or just make sense of what their influencers are actually saying on camera, will need LVMs. Not just generic ones. But context-rich, culture-aware, possibly language-localised and gesture-literate models.

Just as AWS abstracted away the pain of running a server, the next phase of AI infra will abstract away the pain of training, fine-tuning, and context injection. But someone still has to do it.

Training, Not Just Tuning

Here's the catch: models may be commoditised, but training data will not be. Especially video.

You cannot scrape it easily. You cannot compress it blindly. And you cannot hope that a general-purpose model trained on 100M hours of YouTube can reliably detect sarcasm in a Malaysian cooking livestream.

This is where the economic engine starts to look interesting. Someone will own the structured video datasets. Someone will specialise in enrichment - labelling not just objects and sounds, but pauses, cues, glances, rituals.

And just like data centres became the cash cows of Web2, these model training pipelines, especially domain-specific ones, will become the high-margin enablers of Web3's real-world layer.

Want a model that understands how people really buy gold jewellery in India? You are going to need more than ChatGPT. You will need video from Surat, voices from Madurai, behavioural nuance from Mumbai's live commerce shows and yes, a lot of GPUs.

So, What Does This Mean?

It means models will be as common as spreadsheets. Everyone will have one. Most will be rented. Some will be fine-tuned. A few will be trained from scratch.

But the moat will no longer be the model.

It will be the context. The vertical. The specificity.

In that world, a thousand AI startups will not die because OpenAI is better. They will die because they did not own their own data, did not understand their customer's nuance, or did not realise that models are just the beginning.

The real value will lie in teaching those models to see. And to understand.

And for that, you need more than tokens. You need eyes, ears, and cultural fluency.

In the end, LLMs / LVMs will be like servers. Invisible, ubiquitous, expected. But the businesses that will truly matter? They will be the ones who knew early on: all intelligence is local.