AI doesn’t have to be brute forced requiring massive data centres. Europe isn’t necessarily behind in AI arms race. In fact, the UK and Europe’s constraints and focus on more than just economic return and speculation might well lead to more sustainable approaches.

This article is a follow on to Will Generative AI Implode and Become More Sustainable? from July 2024. It’s purpose is to challenge some of the narratives that the big tech players are pushing out (who might just be a little biased due to their strategy, operating models and investments).

AI needs big massive data centres. Really?

The prevailing narrative suggests that AI requires enormous centralised computing facilities, but let’s examine this assumption more closely:

• Local models are now easier than ever to implement - tools like LM Studio enable personal deployment with just a couple of clicks, democratising access to AI capabilities (but also increase bring your own AI risks?)

  • Reduced dependency on cloud providers

  • Better data privacy and sovereignty

  • Protection against intellectual property and know-how theft through private training data

  • Lower latency for many applications

  • More control over model behaviour and updates

  • Retention of competitive advantages and domain expertise

• While training large models currently demands significant resources (and federated training remains challenging), inference is a different story entirely

  • Distilled models running efficiently on modest hardware

  • Quantised models maintaining performance with reduced resource requirements

  • Specialised hardware accelerators improving efficiency

  • Progressive improvements in model compression techniques

• We can move compute more easily than power - edge and distributed architectures offer compelling advantages that will work alongside or provide alternatives to centralised operating models

  • Leveraging renewable energy at the edge

  • Reducing data transfer and associated energy costs

  • Better alignment with local computing needs

  • More resilient and fault-tolerant systems

  • Potential for heat reuse in local applications

• Today’s big infrastructure investments risk becoming tomorrow’s stranded assets. Consider what happened during the last GPU hype cycle for Bitcoin mining - more efficient ASICs eventually replaced GPUs. We’re seeing a similar pattern emerge as models become more efficient and capable of running on consumer-grade hardware (just look at the capabilities of current distilled models)

  • The evolution of AI-specific hardware

  • Rapid advancement in model efficiency

  • Changing economics of centralised computing

  • Environmental pressures driving efficiency

  • Growing focus on sustainable AI architectures

AI needs to return results right now in real time. Does it?

The assumption that AI must always provide instant responses deserves scrutiny:

• AI can operate asynchronously, enabling higher quality processing at lower power consumption

• In enterprise contexts, AI can respond to events through more scalable, sophisticated event-driven architectures that avoid the point-to-point solution integration nightmares of the past

This approach not only reduces resource requirements but often leads to better outcomes through more thoughtful processing.

AI needs to be all powerful and aiming for AGI to be valuable. Not necessarily.

The race toward Artificial General Intelligence (AGI) often overshadows more practical applications:

• AI simply needs to be good enough for the job - narrow AI already demonstrates massive value in specific domains

  • Document processing and analysis

  • Routine decision support

  • Pattern recognition in specific fields

  • Targeted process automation

• “Middle AI” that augments human decision-making often proves more pragmatic than attempts to fully automate complex processes

  • Enhancing human capabilities rather than replacing them

  • Providing insights while leaving final decisions to experts

  • Creating human-AI collaboration workflows

  • Maintaining accountability and explainability

• Smaller models, when orchestrated intelligently, can lead to superior outcomes

  • Better resource utilisation

  • More focused and accurate results

  • Easier to maintain and update

  • Lower operational costs

  • Reduced environmental impact

• Just as diverse teams typically produce better ideas and implementations, diverse “model zoos” might achieve similar benefits through complementary capabilities

  • Specialised models for specific tasks

  • Ensemble approaches combining multiple perspectives

  • Redundancy and resilience through diversity

  • Better handling of edge cases through specialised expertise

AI will be chat or end user orientated. Think bigger.

While chatbots dominate current AI discussions, they represent just one pattern among many possibilities:

• Chatbots are merely one interface pattern, not the definition of AI interaction

• Human-Computer Interaction (HCI) can be fundamentally reimagined, and we’re only beginning to explore the potential of hyper-personalisation

• Multi-modal AI remains in its infancy, with vast untapped potential

• AI capabilities will increasingly be embedded within other platforms and applications, often invisibly enhancing functionality rather than serving as standalone interfaces

• Agentic AI represents a significant evolution beyond simple chat interfaces, where AI systems can:

  • Operate autonomously to accomplish complex tasks

  • Chain together multiple capabilities and tools

  • Learn from interactions and adapt their approaches

  • Collaborate with other AI agents to solve problems

  • Work asynchronously on long-running tasks

  • Maintain context and state across multiple interactions

Sustainable Innovation Through Constraints

Europe’s approach to AI development, often criticised as lagging behind, might actually pioneer more sustainable and ultimately more valuable approaches. The constraints and considerations that shape European AI development – from regulatory frameworks to environmental concerns – could drive innovation in unexpected ways.

Consider how these “limitations” might actually catalyse more efficient solutions:

• Regulatory requirements pushing development toward more transparent, explainable AI systems

• Energy constraints driving innovations in model efficiency

• Privacy concerns leading to better local processing capabilities

• Environmental considerations spurring creative approaches to compute resource utilisation

Learning from Cloud Adoption: The Public and Private AI Journey

The evolution of AI deployment models bears striking similarities to the cloud computing journey. Just as organisations learned to balance public cloud, private cloud, and hybrid approaches, we’re seeing a similar pattern emerge with AI:

• Enterprise Private AI

  • Control over sensitive intellectual property and training data

  • Alignment with existing security and compliance frameworks

  • Integration with legacy systems and processes

  • Customisation for specific business needs

  • Protection of competitive advantages

• Public AI Services

  • Cost-effective for general-purpose tasks

  • Rapid prototyping and experimentation

  • Access to cutting-edge capabilities

  • Reduced operational overhead

  • Pay-as-you-go flexibility

• Hybrid AI Approaches

  • Best-of-both-worlds solutions

  • Data sovereignty and security where needed

  • Cost optimisation across deployment models

  • Risk management through diversification

  • Flexibility to adapt to changing requirements

The lessons learned from cloud adoption can inform AI strategy:

• Not everything belongs in the public cloud

• Cost benefits aren’t always straightforward

• Vendor lock-in concerns are real

• Security and compliance need careful consideration

• The right solution often combines multiple approaches

Looking Forward

The future of AI doesn’t necessarily lie in brute force approaches or massive centralised computing facilities. By challenging these assumptions and exploring alternative federated, distributed and hybrid architectures, we can develop AI systems that are not only more sustainable but potentially more effective at solving real-world problems.

The current AI arms race, with its emphasis on model size and computing power, might be missing the forest for the trees. Sometimes, constraints breed creativity, and Europe’s more measured approach to AI development could lead to innovations that better serve both business and societal needs.

What’s clear is that there’s more than one way to advance applied AI in our organisations, in a way that mitigates the concentration risks of current approaches. As we continue this journey, perhaps it’s time to question whether bigger always means better, and whether the path to truly transformative AI might lie in smarter, more efficient approaches that consider the broader impact on our society and planet.

Want to discuss alternative approaches to AI implementation? Get in touch at sustainability@scottlogic.com or connect with Oliver on LinkedIn.