Computation Battle for Artificial Intelligence Systems

The world is waking up to the adoption of AI. Truth be told, AI is here to stay, though that statement may be up for debate. However, while many of us are only looking towards the implementation of AI systems, we ought to also pay attention to the underlying resources that power these systems.

The Race for Market Share

The tech giants are in a race I will term as “first to reach” when it comes to providing AI services. We have seen major cloud providers upturn their business models to suit the AI market. For one, most of these businesses now have one or two provisions to power AI models. The chip makers are churning out new AI-centric chips for the market. There has been a comparison about the best one to use. While these are all good for the valuation of the service providers, one question is how does this happens to benefit the average business trying to adopt an AI system without the burden of costs. More importantly, is this not a cause for confusion on how and what to use (in terms of resources) to build an AI system? So far, there has not been a standard for using these services, and the competition among the tech giants is not helping to tell the truth about the right one to use among the computing facilities. Each service provider focuses more on marketing appeal than the value appeal of their services. 

The Growing Complexity of Decision-Making

Come to think of it, at the initial stage, we had the Compute Processing Unit (CPU) type of computation to run our workloads. Then we got wind that it would not be sufficient to power an LLM model, so tech innovators suggested the Graphical Processing Units GPU. While businesses are still figuring out the best way to use the GPU without breaking the bank, we have the Tensor Processing Unit. And now -drum roll- we have the Layer Processing Unit (LPU) and the Language Processing Unit (LPU). Yes, both have their sign as LPU. All those include the numerous inference chips available under each category for use.  We have all of these, and now businesses, especially small businesses, are confused about the right tool to meet their business use case or build an effective generative AI system without throwing money to the wind. 

Climate Impact: The Hidden Cost of Innovation

One prominent concern about these innovations is the effect of climate change. According to this report, the emission of carbon by one enterprise-grade AI model is compared to the emission of carbon from 5 cars over their usage. That says a lot if we compare that to the number of AI models we currently have and the many that are still in the pipeline. The magnitude of such an effect on our climate is unimaginable.

Financial Implications: The Hidden Pitfalls

The financial implications of these innovations also need to be considered. I mean, with the way the processors and chips are flooding the market, it may not be obvious now, but it could get to some point where unsuspecting businesses will only be enriching the service providers. These service providers will, in turn, invest the money into marketing and promotion to further get the businesses to believe they can get value for their money if they try out another type of “processor” in the list of their “offerings”. How would the computation service providers, while they trying to outdo each other in terms of market share, ensure that their innovation is truly innovation to their customers, unlike what we are beginning to see in cloud service providers?

Conclusion: Preparing for the Future

While all of these seem to be a concern or not now, should businesses brace up for more of these coming? To be safe, it is important to seek the service of an expert like an AI Architect to chart the course for a business intending to adopt AI to improve its business model.

Rewritten

AI Adoption: The Race, The Risks, and The Reality Check for Businesses

As AI continues to reshape industries across the globe, businesses are faced with an exciting yet overwhelming challenge, which is how to adopt and integrate these advanced technologies effectively. From cloud services to specialized chips, the race for AI supremacy is accelerating at a breakneck pace. Yet, while the spotlight often shines on AI systems themselves, there’s a crucial aspect many overlook, and that is the resources and infrastructure that make these systems run smoothly. Therefore, understanding what powers AI is just as essential as understanding how it can revolutionize your business.

The Race for Market Share

Tech giants are locked in a fierce race for market dominance in the AI sector. It’s what I like to call a “first to reach” race, with each player scrambling to provide AI services that cater to diverse needs. Major cloud providers have been overhauling their business models to serve the AI market, introducing new solutions tailored for AI integration. On top of that, chipmakers are pumping out AI-centric chips at a staggering pace, and there’s endless debate about which ones are the best.

But the million-dollar question is: How does all of this benefit the average business aiming to adopt AI without breaking the bank? The rush to compete has left a cloud of confusion over which tools and resources to use for building an AI system. What’s worse is that there’s still no clear standard for using these services, and the tech giants aren’t making it any easier. Instead of focusing on the true innovation of their products and services, they’re prioritizing marketing flair.

The Growing Complexity of Decision-Making

In the early days, businesses used the CPU (Central Processing Unit) to power their systems. Well, at some point, it appeared the CPU could not handle the AI workloads, and then came the introduction of the GPU to handle the demands of large language models (LLMs). Fast forward to the present, and we’re dealing with not just GPUs but also TPUs (Tensor Processing Units), LPUs (Layer Processing Units), and even more specialized units like the Language Processing Unit (LPU). With so many options on the table, it’s no wonder that businesses, especially smaller ones, are struggling to figure out which tools fit their needs without draining their resources.

Climate Impact

One of the most pressing concerns tied to these advancements is their environmental impact. It is much like the hidden cost of all of our innovations. A 2023  report revealed that the carbon emissions of just one enterprise-grade AI model are comparable to the emissions from five cars over their lifetimes. That’s alarming, considering the growing number of AI models in use and those still in development. If this trend continues unchecked, the effect on the climate could be catastrophic. 

Financial Implications: The Hidden Pitfalls

As the market becomes flooded with processors and chips tailored for AI, businesses may not immediately see the financial implications. But as the competition intensifies, there’s a risk that companies could be unknowingly funneling money into the pockets of service providers without gaining the expected returns. These providers, in turn, will likely reinvest that money into more marketing campaigns, convincing businesses that their latest “processor” is the key to unlocking the full potential of AI. The question is: How can businesses discern true innovation from mere marketing fluff in a market as competitive as this one?

Conclusion: Preparing for the Future

As AI technology continues to evolve and more innovations emerge, businesses must brace for more challenges. To navigate this complex landscape, it’s crucial to seek expert advice from an AI architect who can guide your AI adoption strategy and help you build a solution that’s both cost-effective and efficient.