At the start of 2024 our CEO, Tom Ormsby, made 5 predictions for AI in 2024. Now he returns to mark his own homework.

Having already reflected on my predictions for 2024, it feels that the past year has been somewhat chaotic in the land of AI. With constant model releases, battles on benchmark leaderboards, and a trial and error approach to developing solutions, it was clear that the world needed some time to find its feet and really think about the implications of AI technologies on society and the ways we work. But, if 2024 was the year of noise, then 2025 is the year of order. Or at least, the year we start to put things into order. Now the 2024 dust has settled, we find a lot of organisations reflecting on what the future really looks like for them, and how they can use a promising technology in a way that makes sense for their business. It will be an interesting year ahead, and below I've pulled out 5 key areas that I'm paying attention to in 2025...let's check in again at the end of the year to see what really happened.

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01. Agents, agents, everywhere!

Let's get this one out the way, AI agents became the big genAI topic of the last part of 2024, and 2025 is likely to see that popularity surge as OpenAI continue to push their o1 model, and focus on how they can chain model outputs together to tackle more complicated workflows. However, that will come with the same challenges that came with LLMs and AI assistants, as developers and users trial and error to find the right use cases. Questions will also arise around the cost-effectiveness of these solutions, as prices to deliver these workflows remain higher than automations (and in some cases higher than for a human to complete them). Whilst it is likely that agent based systems will stick around and become more sophisticated in the future, companies pushing agents will focus largely on language use cases and automations for now. Think: marketing/CRM, web scraping, and collaborative tools.

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02. OpenAI struggles to lead

The gap between OpenAI and the chasers closed significantly in 2024, and 2025 could be a challenging a year for the company as it struggles to build a moat around its product. Questions had already started to emerge about the company's ability to maintain its lead as the chasing pack of LLMs significantly closed the gap, and OpenAI shifted focus to chain-of-thought "reasoning" and additional features such as search. Since penning this prediction, OpenAI has already faced a significant challenge from DeepSeek, the Chinese competitor that has matched its models in benchmarks andintroduced a new method for training models. The challenge is exasperated further by DeepSeek's decision to publish a paper detailing their training and technical approach, as well as open sourcing their model for anyone to use. So far, OpenAI's response has been to release further features built on top of their model such as the newly announced "Research".

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03. Year of the AI builders

As the initial wave of fear, excitement, and noise has calmed down, and buyers/developers are beginning to understand more clearly what AI can do, conversations are turning more towards "What can AI do for me?". Organisations are beginning to learn more about the data and processes that allow AI models to be built, and explore how they could tackle challenging workflows within their organisations with this technology. We can expect a major focus on building AI solutions in 2025, with 64% of CEOs listing AI as a top investment priority. This will show in two ways: organisations will begin to demonstrate how they have built their own internal AI workflows to empower their businesses, and we will begin to see more comprehensive AI powered products gaining traction with a clear value proposition.

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04. Embedded Language Models

The drive to create smaller models is not just about ecology and efficienct, it's also enabling language models to be embedded into hardware. By growing the capabilities of smaller models, we are enabling engineers to explore how they can run models on devices with limited storage, memory, and compute power, which means: we may begin to see a wave of language features appearing on other devices. We're already familiar with smart speakers such as Alexa, but we may now start to see them deployed locally within our phones (allowing access to the models without internet connection), within robots to allow you to give spoken instructions, or perhaps even within appliances as people re-explore the smart home dream. With these products just around the corner, it's likely that we will see early version of these hit the market in 2025. It will be interesting to see how this advancement influences the design of products.

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05. Increased strain on AI Skills

As the AI demand refuses to ease, and as organisations are realising the potential of AI, and facing pressure to show that they are doing something about it, we will see a lot of businesses committing to building skills within their teams. The 2025 World Economic Forum Jobs Report published at the start of January highlighted that 70% of respondents intended to hire for AI skills. This creates a major challenge, as the market for these skills is already limited. There is a high likelihood that organisations will struggle to fill roles and retain staff with AI skills as companies jostle to build their own internal capabilities. It will take time for universities, apprenticeships and other training routes to deliver the talent needed to relieve this demand. One route some organisations may consider is upskilling/reskilling internal staff, however 63% of survey respondents considered this a major barrier, with it likely to be costly to invest in and a need for training organisations to step up and provide support.

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The AI world is moving fast, and it will be interesting to see how organisations adapt to the technology in 2025, I look forward to returning to these predictions at the end of the year to see what actually played out.

Like the monolith from 2021: A Space Odyssey, AI is being touted as something that will herald a new era of computational power and human capability. The things that AI is already able to do are truly mind-blowing - and it’s still very much in its infancy.

But let’s take the rose-tinted glasses off for a moment. We are all staring down the barrel of a full-blown, man-made climate crisis. Technology is helping us mitigate its impacts somewhat, with improvements in fields like renewable energy offering glimmers of hope.

AI holds the potential to make our lives easier and more efficient. However, AI’s relationship with energy consumption is complicated. At worst AI is a huge energy and resource hog - at best, AI professionals are working on mitigating that fact.

Pinning down the exact carbon impacts of AI, and especially of specific Large Language Model (LLM) AIs like ChatGPT, is tough. But energy use - and misuse - is totally under human control. It’s our responsibility as humans who use AI tools to do so ethically and mindfully.

Before we explore the ways we as AI practitioners can redress this much-needed balance, we need to fully examine some of the ecological worries inherent when using AI, especially Large Language Models.

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4 Major Environmental Concerns with Large Language Model AI Usage

1) LLM’s Massive Energy & Resource Consumption

AI - and especially elements like deep learning and neural networks - requires much more computational power than standard computing. The sheer amount of information storage and data crunching inherently needed by AI consumes far more energy than any kind of computing that has ever come before.

The wide-reaching, multi-purpose AI tools that make the headlines, like ChatGPT and Gemini, are called “large language models”. To vastly simplify, these tools maintain access to trillions of data points that they can draw upon when they give a response, and contain countless parameters to help them score and link those data points; hence why their responses seem so natural and human.

But maintaining access to those countless data points, parameters, and the inferred links between them -  and growing its access to millions more data points every day - takes a lot of energy, infrastructure, and hardware. And that’s before we get to the energy consumption involved in answering the countless requests they receive every 24 hours.

This becomes especially concerning when you zoom out and think about the vast data centres needed to operate cloud tools of all kinds, not just AI ones. Data centres constantly consume energy. Their servers are running 24/7, as are the routers and switches that get data where it needs to go. IT equipment needs to be cooled, so air conditioners are always running. Their employees have to commute to and from them every day, likely using combustion engines of some kind.

With energy supply around the world still so reliant on fossil fuels, this is understandably a significant draw on carbon resources. (Though it’s worth noting that both Google and Microsoft have made strides in their efforts to become carbon neutral/negative.)

Research from University of Massachusetts, Amherst found that merely training a large language AI model emits approximately the same amount of CO2 as 5 average cars across their whole lifespan. That’s just training the thing - once the LLM is actually put to use, it could end up consuming orders of magnitude more energy than that throughout its existence.

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2) Mining Rare Earth Minerals for Tech Manufacturing

In order to build new computer hardware, manufacturers need to mine rare earth elements (REEs) and minerals. This strips the Earth of its natural finite resources; and once they are mined, they still need to be transported, processed, and then manufactured into whatever board or device they are destined to become - all of which may still rely on fossil fuels and polluting practices.

Also well worth mentioning here is the continued labour and human rights abuses taking place throughout technology supply chains.

Heading back to AI for a moment, computer hardware undergoes quite intense usage when running large language models day in, day out. This all chips away at the hardware’s natural lifespan. When that hardware fails and needs to be replaced, that perpetuates the demand for more resource stripping and potentially sends more e-waste to landfill.

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3) AI’s Trendiness and Simplicity Leads to LLM Overuse

Sadly, many view AI as the latest tech toy. And who can blame them? Interacting with publicly available LLM AI tools like Chat GPT is deceptively simple. With similar effort to a Google search, users can generate new text, stories, art, or code; get answers to questions; research and hash out ideas; and much more.

As such, both individuals and businesses alike are already using LLM AI tools alarmingly frivolously, such as asking their LLM of choice what the weather is up to or what’s the best air fryer to buy - things that would usually be the subject of a normal web search.

And though typing a prompt into a generative AI tool may appear very similar to a web search, each LLM AI query comes at a far higher carbon cost than anything generated by standard computing. Research suggests that a ChatGPT query consumes around 60 times more energy per query than a simple Google search.

But this isn’t just a case of ease - it’s a case of trendiness too. Organisations want to appear ahead of the curve by adopting AI and LLM tools, because it’s the hot new tech. So some end up doing so just for the sake of it; without a meaningful use case that justifies the need for such a complex, heavy, and potentially polluting computational tool.

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4) How You Code - And Where You Compute - Matters

In computer programming, there's usually more than one way to code a solution to a problem. Sometimes code is kept efficient and lean, only carrying the bare minimum instructions needed to carry out the functions in question. Other times, code becomes bloated and inefficient, full of exceptions and weird sticking plaster workarounds.

When code is poorly optimised, it takes more energy and computing power to navigate the inefficient twists, turns, and dead ends. Code that is well-optimised is far more energy efficient.

This is true of any software, not just tools that rely on AI. But if your software does involve AI, you need to make sure your AI is targeted precisely to your specific use case and the code doesn’t make erroneous or frivolous AI requests.

Simply moving data around also consumes energy. A data packet travelling from LA to London, through various routers and data centres, is going to have a larger carbon impact than one going from Liverpool to Manchester. So think: does a computation have to happen on some distant server somewhere and then be transported to you? Or can that process take place within your own network? Or perhaps directly on the relevant device? There’s no need in making your data move further than it has to!

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Ways AI is Already Helping Fight Climate Change

AI itself isn’t some world-ending carbon hog - it’s how we use it that counts.

In fact, it’s already being used to great effect in the fight against climate change. Let’s acknowledge some of the good work that is already happening in the eco-AI space:

• Eco-friendly Number Crunching - AI tools are already being used in waste processing, in reforestation efforts, and even measuring changes to icebergs. AI is also being used to optimise energy use and distribution, monitor ocean health, and water conservation - with scope for uses like precision agriculture.
• Accurate Weather & Climate Event Predictions - AI weather forecasting tools can predict weather with more accuracy than standard weather simulation systems. Experts have argued for the use of AI to advance climate modelling and prediction.
• Waste Intelligence - British company Grey Parrot supports companies with AI-powered waste analytics, enabling facilities to “recycle more and waste less”.
• Smart Urban Planning - AI shows a lot of promise in areas like city planning, infrastructure design, predictive climate modelling, and generally helping to create more sustainable, enjoyable cities.

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5 Considerations for Eco-Friendly AI Adoption

There are many sensible ways SMEs can benefit from AI and be as kind as possible to the planet. Here are a few things you can bear in mind before embarking on your next AI tech transformation project:

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1) Consider Use Case First, AI Novelty Last

We get it, AI is trendy. Companies want to boast about how their tools are using the latest technology buzzword as it makes them seem cutting edge. Yet this trendiness can lead to AI solutions being applied in situations where standard computation would have worked equally well.

Our advice? Maintain a single-pointed focus on the specific use cases you need from your new technical solution and what problems you need it to solve. If that involves AI, then great! We’re here to help. But don’t try to shoehorn in a particular kind of tech where it might not be needed.

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2) Model Size Should Fit the Scope of the Project

Not all AIs are created equal. Yes, the large language models make the headlines, but not all AI implementations require such massive, wide-reaching datasets.

Small language models (SLMs) are artificial intelligence tools which rely on much smaller, more targeted data sets. This makes them far narrower in scope, but also often far more carbon efficient than their LLM cousins.

SLMs can be perfect for applications that require some of the more flexible and artificially creative elements of AI, but only need a limited focus; for example an AI-powered website chatbot that only needs to know details about a company’s product catalogue.

In contrast, using a vast LLM like ChatGPT to power a simple website chatbot would be like using a sledgehammer to crack a nut!

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3) Be Aware of How AI Differs from Standard Computing

Large language, generative artificial intelligence is very different from any kind of computing that has come before. It requires a huge amount of data crunching, which in turn requires a lot of hardware and energy resources to maintain.

Keep this in mind when deciding to use AI, and don’t get carried away with using overpowered LLM AI solutions when a SLM or more conventionally programmed solution would suffice.

But here’s one thing you and your teams can do in the here and now: don’t overuse generative LLM tools for frivolous things where a simple web search would suffice!

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4) Aim for Efficiency in All Digital Transformation

Whether your solution uses AI or not, aim to design the most energy and computationally efficient solution. For our more tech savvy readers, this might mean optimising your code so it doesn’t run into errors, exceptions, and dead ends.

But creating an efficient solution truly starts at the ideation phase. Does that data point really need to be fetched from the other side of the planet? Do all of our networked devices need to be set up to carry out this complex type of calculation, or will one or two machines suffice? Does the software really need to speak to an AI in order to do [X]? Do we really need new hardware when the old stuff is still up to spec?

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5) Keep Your Tech Supply Chain Clean

We wholeheartedly welcome the fact that an increasing number of data centre operators are committing to use only renewable energy in their data centres. Alas, despite these noble efforts, it still doesn’t mean that the whole supply chain is rendered magically spotless.

The tech hardware supply chain is reportedly slow to address human rights abuses. Rare earth elements are expensive and polluting to extract - and only available in finite quantities.

Sadly this means that no tech supply chain is ever going to be 100% clean. But here’s another tip you can start right now: research your supply chains and do what you can to ensure suppliers align with your principles.

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In Conclusion

Right now, AI definitely helps to expand human throughput, which is a great thing. But, despite the hand wringing in the headlines and the shareholder-pleasing statements coming out from "Big AI", machines cannot think like humans yet, and can’t make decisions on what is right or wrong. They’re not even close.

AI certainly magnifies our ability to make positive impacts in the world. But it magnifies our ability to make negative impacts too. Whether those negative impacts are cruelly negative on purpose, or come about from well-meaning misunderstanding or error.

This is why it's so important for us as decision-making humans to carefully consider the impacts of our use of technology before we rush into using AI simply because it's the latest tech trend.

Thankfully, AI is still in its infancy, and we have time to make it a much less polluting, efficiency-driving force for good.

But it’s up to us as humans and decision makers to create that future.

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If you foresee a use for responsible, sustainable AI in your future technical transformation projects, book a totally free consultation call with our expert team today.

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