Future of AI is On the EDGE

Message By CEO:

Since I wrote an article about AI and electricity last year, a lot has changed. It’s becoming increasingly clear that for AI products to work well, they don’t just need electricity they also demand computational efficiency and ultra-fast cloud connectivity.

The International Energy Agency (IEA) projects global data center electricity consumption will rise from 460 TWh in 2022 to as high as 1,050 TWh by 2026. This surge is driven partly by crypto, but mostly by the explosive growth in AI computing.

To put it in perspective: humanity consumes about 30,000 TWh of electricity annually, and AI alone is expected to add at least 1000 TWh every year. The challenge is that we don’t have enough electricity generation capacity today and not enough fresh water either.

Yes, fresh water. Data centers require huge amounts of it for cooling. Earlier this year, Los Angeles restricted water access to data centers because many fire hydrants ran dry during the devastating wildfires partly due to water being diverted to AI and other data centers.

Electricity limits, water limits, rising GPU costs, and the sheer complexity of data center operations are pricing out small and medium enterprises and consumers even more so. Unless we achieve breakthroughs like unlimited energy through fusion or Tesla coil technology, these constraints are here to stay.

 

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Before I dive into AI on the Edge why and how — let me clarify something important:

AI is not the product. AI is part of the product and what we know from Generative AI ventures teach us.

Investors, product managers, architects, and developers often focus too much on the AI itself. But the real value lies in the delivery of that AI to customers as the product as a whole, with great user experience as a core component of it. That’s the part many tech juggernauts overlook.

Right now, the world is obsessed with Generative AI. But in that excitement, we’re missing Computer Vision AI — which solves real life problems and empowers real-time use cases. There are domain-specific Large Visual Models (LVMs), but none have shown significant utility yet.

 

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According to an MIT research report, 95% of enterprises after spending around $40 billion on Generative AI over the last two years or so — have not seen real benefits.
Why?

• Lack of integration into existing workflows

• Lack of contextual information (which usually lives in the minds of domain experts, not in datasets)

• Inability to customize for specific use cases

So Generative AI has hit the reality wall.

That said, tools like Replit AI and Cursor AI for coding, Veo 3 and Sora for creative media, and ChatGPT, DeepSeek, Grok, and Gemini for language and information tasks will be excellent co-pilots within their respective domains.

The key for enterprises will be how they build custom agents and wrap them into real products.

 

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But let’s talk about what’s missing:

Computer Vision AI.

Think mission control for law enforcement, factory floor safety, warehouse management, parking lot monitoring, or battlefield threat detection or smart home and security.

No Generative AI LVM can handle even one of these domains. They’re far beyond the reach of current GPUs and the best computer vision frameworks we have today (like AlexNet).

Large Language Models (LLMs) became possible thanks to the neural networks architecture called Transformer, introduced in 2017 by Google researchers in the paper “Attention Is All You Need.” That breakthrough gave us ChatGPT — our first mainstream Generative AI experience. Kudos to the Juggernauts of Google. 

But no such breakthrough has happened for computer vision yet. LVMs are nowhere close. Generative models have billions of parameters and simply cannot run efficiently on low-power devices. They’re powerful, yes but their utility is limited to language and cognitive tasks.

 

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Computer Vision is different.

The future will be built on Computer Vision models. Whether it is factory floors, homes, offices, parks, roads, cars, everything around us will rely on real-time computer vision to improve safety, efficiency, and quality of life.

There are millions of possible use cases — from retail theft prevention to smart security systems to self-driving cars.

But here’s the challenge:

• Video streams are heavy and bandwidth-intensive

• They need massive computation power

• And when multiple streams are processed on the same GPUs, compute (FLOPS) requirements grow exponentially, not linearly

It works when you have one or two streams. But when you’re processing hundreds of thousands, the cost, power, complexity, and need for highly skilled talent skyrocket — just like with Generative AI.

 

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This brings me to what I call the 4 Cs of AI — a challenge for both Generative AI and Computer Vision:

Compute. Complexity. Connectivity and Cost.

Solving these 4 Cs is how we will democratize AI — and truly transform lives, at a scale not seen since the Industrial Revolution.

For Generative AI, solving these may take a long time.
But for Computer Vision, we have a solution and it’s here now.

The answer is running Computer Vision models (Camera AI) on the Edge.

This lets us build thousands of AI-powered CV apps that are fast, efficient, and cost-effective, without depending on the cloud.

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Why is Edge AI the future for Computer Vision?

Because it solves the 4 Cs — and unlocks the true potential of AI.

Next, I’ll break down how computational and electricity needs differ dramatically when CV runs on the Cloud vs on the Edge.

Here we go.

Arguments can be the accuracy of models. The difference in accuracy between Cloud and Edge is minimal when models are purpose-built for specific use cases and use a small number of parameters; they work well on small lower EDGE GPUs. These smaller models are not generalized but when many of them work together, it can seem as though a single model is doing everything, and the results look like magic.

And we at IRVINEi.com are at the forefront of this technology — leading the way to democratize AI for the masses.