The Future of AI: Powering Towards AGI with Smarter Inferencing

Remember when we thought affordable, flying cars and jetpacks were just around the corner? Well, here we are in 2024, and while I'm still waiting for my personal flying car, we're knee-deep in another technological revolution: the hunt for Artificial General Intelligence (AGI). But here's the million-dollar question that's been buzzing in every tech corridor from Silicon Valley to Singapore as well as in many conversations that I'm having with my clients: "What does the future of AI really look like?"

The AGI Conundrum: A Double-Edged Sword

AGI is that holy grail where machines can think, learn, and apply knowledge across diverse tasks just like humans. Sounds amazing, right? But hold onto your hats, folks, because achieving AGI with our current methods isn't just difficult—it's about as sustainable as trying to power New York City with a hamster wheel.

Let's talk numbers for a second. Did you know that training a single large language model like ChatGPT can guzzle up enough energy to power a small U.S. city for a year? That's right, you read that correctly. A recent investigation revealed that major tech companies, including Google, Microsoft, Meta, and Apple, are producing 7.62 times more emissions from their data centers than officially reported [1]. Talk about a carbon footprint the size of Godzilla!

The Power Predicament: When AGI Meets Reality

Now, I'm not one to name-drop, but when someone like Eric Schmidt, former CEO of Google, speaks up about AI, we should probably listen. Here's what he had to say:

"I went to the White House on Friday and told them that we need to become best friends with Canada because Canada has: really nice people, helped invent AI, and lots of hydro power because we as a country do not have enough power to do this" [2]

Did you catch that? The United States, home to Silicon Valley and some of the world's biggest tech giants, doesn't have enough juice to power AGI development. It's like trying to run a Formula 1 race with a AAA battery!

Rethinking Our Approach: The Promise of Efficient Inferencing

But before we all pack up and move to Canada (though I am a fan of that country and am friends with a number of those "really nice people".), let's talk about an actual game-changer: inferencing. It's the phase where AI models actually do their thing, like recognizing your face in that embarrassing high school photo or generating that report you forgot was due yesterday.

Picture AGI as a supercar and inferencing as its engine. Right now, we're trying to power this dream machine with a gas-guzzling V12 that's great for short bursts but terrible for long drives. Efficient inferencing is like swapping in a cutting-edge electric motor – suddenly, our AGI supercar can run longer, faster, and smarter without needing its own power plant. It allows for bigger, more complex AI models that can juggle multiple tasks and adapt on the fly, all while sipping energy like it's fine Turkish wine rather than chugging it like cheap beer.

By focusing on making inferencing more efficient, we're not just making AI smarter; we're making it sustainable. It's the difference between an AI that can solve world hunger but might accidentally melt the polar ice caps in the process, and one that can tackle our biggest challenges without breaking a planetary sweat. In essence, efficient inferencing isn't just a pit stop on the road to AGI – it's the eco-friendly fuel that'll get us there.

Here's where it gets exciting. Companies like Groq are developing Language Processing Units (LPUs) that can deliver up to 18x faster output while sipping energy as mentioned above. It's not just a tech upgrade; it's a paradigm shift in how we might reach AGI without turning Earth into a giant computer like in "The Hitchhiker's Guide to the Galaxy."

A Sustainable Path Forward: The Power of Efficiency

So, what's the takeaway here? The future of AI isn't about building bigger, hungrier models. It's about building smarter, more efficient systems. It's like the difference between a gas-guzzling muscle car and a sleek electric vehicle. Sure, they'll both get you from A to B, but one of them won't make Mother Nature cry.

By focusing on energy-efficient inferencing and being transparent about environmental impacts, we can work towards a future where AI's potential is realized responsibly. It's not just about what AI can do for us, but what we can do to ensure AI doesn't cost us our planet.

In conclusion, as we stand on the precipice of this AI revolution, let's not forget the lessons learned from previous tech booms. We have the power to shape this future. So, my fellow tech enthusiasts and concerned citizens, let's make sure it's a future we actually want to live in. One where AGI doesn't just make our lives easier, but also keeps our planet habitable. After all, what good is a super-intelligent AI if it doesn't have a home to live in?

Now, if you'll excuse me, I need to go check if my affordable flying car has been delivered yet.

[1] AI's Dirty Secret Is Far Worse Than We Thought [2] Eric Schmidt on AI and Power Requirements