AI/ML Predictions for 2025–2026

Why managing AI risk presents new challenges

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The difficult of using AI to improve risk management

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How to bring AI into managing risk

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Pros and cons of using AI to manage risks

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Benefits and opportunities for risk managers applying AI

AI is no longer just the future—it’s the right now. Over the next couple of years, from 2025 to 2026, we’re expecting some big, exciting changes that will completely reshape how businesses use AI and Machine Learning (ML).

At GMI Cloud, we’re lucky to be right in the middle of it all. Our AI development platform lets us power some of the most demanding AI workloads, and we’ve got a front-row seat to the trends shaping the industry. From skyrocketing demand for computing power to major questions about sustainability and ethics, there’s a lot to prepare for—and a lot to be excited about.

Here’s what we’re predicting—and how we’re gearing up to help businesses navigate what’s next.

The Carbon Cost of AI Is About to Get Real

The environmental impact of AI will be a hot topic by 2026. Yes, it's already being discussed, but it's not at the forefront of media yet. I'm talking about difficult societal questions about where to build nuclear reactors, not "if" we should build them. The energy requirements of AI will demand to be met.

Governments might start cracking down with regulations—think mandatory energy reporting, carbon offsets, or stricter efficiency standards for data centers. And businesses that ignore this won’t just risk bad press—they could face real financial and operational challenges.

But here’s the flip side: this is also a massive opportunity. Companies that embrace renewable energy, invest in efficient cooling systems like direct-to-chip water cooling, and aim for ultra-low PUE (Power Usage Effectiveness) will have a real edge.

What we’re doing about it: At GMI Cloud, we’ve already prioritized sustainability. Our data centers use renewable energy and cutting-edge cooling tech, which doesn’t just reduce our carbon footprint—it also cuts costs for us and our customers. Win-win.

Tackling Bias, Stolen Data, and Deepfakes

AI is incredible, but it’s not without its baggage. Over the next two years, we expect to see more headlines (and lawsuits) about AI bias, stolen training data, and deepfakes causing real harm.

Honestly, we’re bracing for a moment when something AI-related goes too far. Right now we're just at the talking stage, but no regulatory body or legislator is truly acting. It’s a lot like what happened with social media, but AI has its own unique challenges.

How we’re stepping up: We’re exploring ways to help businesses audit and test their AI models for fairness. This means retraining models with ethically sourced data and addressing bias at the root. As for deepfakes? That’s a tougher nut to crack, and we believe it’s going to take a collective effort to figure out. It’s a big discussion that the whole industry needs to have, and we’re here for it.

The Talent War Is Moving to AI Infrastructure

If you think hiring AI training engineers is tough, just wait. By 2026, the most in-demand jobs in AI won’t just be for model developers—they’ll be for infrastructure experts. These are the folks who can make sure AI models run smoothly across distributed GPU environments, maximizing efficiency and minimizing downtime.

Right now, many companies are struggling to hire this kind of talent, and it’s only going to get harder. Hyperscalers (like the big cloud providers AWS, GCP, and Azure) are happy to lease out their expertise, but it’s often at a premium—and with little incentive to solve problems quickly, given their "by the hour" payment model.

How we’re helping: We’re bridging the talent gap with our team of top-notch AI infrastructure experts, and we offer their expertise as part of our flat-fee services. No surprise bills, no endless upselling—just predictable costs and real results.

Specialized Chips Are Taking Over

GPUs have been the go-to hardware for AI for a long time, but that’s starting to change. These ultimately stand for Graphics Processing Units, intended for computer graphics. Specialized chips like TPUs and other custom accelerators are proving to be faster and more efficient for specific tasks like natural language processing or video analysis.

Here’s the catch: businesses that built their own GPU data centers are realizing it’s a lot more expensive to maintain than they expected. Upgrades are constant, and the pace of change makes it hard to keep up. It’s a costly treadmill—and not everyone can stay on it.

How we’re preparing: We've always provided early access to specialized chips to those who want it, and we're committed to doing so. Flexibility and specificity are often at odds with each other, but we're keen on finding the right balance. Businesses shouldn't have to worry about the upgrade treadmill—they can focus on building and deploying their AI vision while we handle the infrastructure.

Looking Ahead

The next couple of years are going to be pivotal for AI—and for the businesses that build or rely on it. At GMI Cloud, we’re not just keeping up with the trends—we’re staying ahead of them. From sustainability to ethics to cutting-edge hardware, we’re here to help businesses thrive in an AI-first world.

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