Tech Horizons 2026: Agentic AI, Hardware Realities, and What's Actually Coming

Happy New Year! If you’re reading this on New Year’s Day 2026, I hope you’ve had a chance to rest over the festive period and you’re feeling somewhat ready for whatever this year throws at us. If you’re anything like me, you’re probably still working through that third coffee trying to remember what day it is.

I don’t think I’m going to be making any crazy predictions this year about flying cars or the singularity (spoiler: neither is happening anytime soon); but, I thought that I’d take a look at what’s actually on the horizon for 2026. The tech landscape is shifting in some genuinely interesting ways, and some of those shifts are going to hit closer to home than you might expect, particularly if you’re a gamer or hobbyist trying to build anything involving AI.

Table of contents

The Year of Agentic AI (For Real This Time)

if we are being honest, we’ve all been hearing about “agentic AI” for a while now. With that said, 2026 looks like the year it actually moves to much bigger production environments. Industry consensus suggests this is when enterprises will properly productionise AI agents rather than just running proofs of concept around the technology.

What does that actually mean? We’re talking about AI systems that can execute tasks autonomously for extended periods; think full workday-length operations rather than the current hour-or-so limit. According to recent analysis, AI task duration has been doubling every seven months, and if that trend holds, we’re looking at agents capable of 8+ hour autonomous workstreams by late 2026.

The interesting bit isn’t just that agents will run longer, it’s how this changes staffing and project planning. When you can deploy an agent that works autonomously on a specific task for an entire day, the economics of certain types of work start to look very different. Some predictions suggest businesses will pay more for AI agents than for people for the first time this year, factoring in the full cost of recruitment, training, and management.

But here’s the reality check: this doesn’t mean your job is going anywhere (despite what the doom-mongers on LinkedIn will tell you). What it means is that boring, repetitive tasks that nobody wanted to do anyway will increasingly be delegated to autonomous systems. The work that requires judgement, creativity, and understanding of broader context? That’s staying firmly in human hands.

Agent Observability and Infrastructure

If you’re building or managing AI systems, agent observability is going to become critical. When agents are running autonomously for hours, you need visibility across code execution, threat detection, and data lineage. This convergence of engineering observability, security observability, and data observability into a single discipline represents one of the more practical challenges we’ll face this year.

There’s also the infrastructure consideration. Vector databases are resurging as essential infrastructure because multimodal models and world models demand new data architectures. If you’re not already thinking about how your data is structured for AI access, 2026 is the year to start.

Small Language Models: The Quiet Revolution

While everyone’s arguing about whether GPT-5.23 or Claude Opus 4.5 is “better,” there’s a genuinely interesting shift happening at the other end of the spectrum. Small Language Models (SLMs) are becoming the sleeper trend of 2026, and for good reasons that have nothing to do with benchmark charts.

A colleague recently shared a story that’s becoming increasingly common: their mid-size SaaS company’s AI bill had crept past £20,000 per month. Nothing they were doing really needed a model trained on the entire internet. After a weekend of experimentation, they swapped the giant model for a fine-tuned small one, ran it on a single GPU box, and kept private data in-house. The performance hit was negligible for their use case, but the cost savings were substantial.

The appeal of SLMs comes down to three factors:

1. Cost efficiency: You’re looking at 10x cost reductions compared to frontier models for specific tasks
2. Latency: SLMs typically deliver 50-90% lower latency than LLMs due to smaller model sizes
3. Privacy and control: Running models on your own infrastructure means sensitive data stays in-house

Models like Mistral Nemo 12B are proving that you don’t need hundreds of billions of parameters for many real-world applications. The key is understanding your specific use case and choosing (or fine-tuning) the right model for it. If you’re building AI features into applications this year, SLMs should be on your evaluation list alongside the frontier models.

The Balancing Act

SLMs aren’t a silver bullet, of course. They generally require more training or fine-tuning data to achieve comparable performance to LLMs. If you’re working in a data-constrained environment, they might not deliver optimal results. But for organisations with specific, well-defined use cases and enough data to fine-tune effectively, SLMs offer a compelling alternative to paying enterprise rates for GPT-5 or Claude.

Large Language Models: Evolution, Not Revolution

Speaking of frontier models, what’s actually happening with GPT-5*, Claude, and the rest? The honest answer, in my opinion is: steady improvement rather than transformational leaps.

GPT-5+ continue to be consistent high-performers across most use cases. Claude Sonnet 4.5 has become one of the best coding models available along side the latest version of Opus. Google is differentiating itself through breadth—achieving breakthroughs across frontier models, on-device inference, video generation, open-source weights, and search integration. The days of every lab competing on every frontier are ending; we’re seeing specialisation instead.

What matters for practitioners in 2026:

• Context windows remain crucial—having 128K+ tokens of usable context makes a real difference for complex tasks
• Reasoning capabilities continue improving, particularly for tasks requiring multi-step logic
• Cost vs. performance trade-offs are becoming more nuanced as the model landscape diversifies

The interesting development is AI budgets receiving proper scrutiny for the first time. Buying committees and boards are pushing back on AI spending, which is driving the interest in SLMs and open-source alternatives. If you’re managing AI initiatives this year, expect to justify your model choices more rigorously than you have in the past.

Robotics: Demonstrations, Not Deployments

Let me start with what won’t happen in 2026: you won’t have a humanoid robot folding your laundry by February. Sorry to disappoint anyone who believed the hype. Trust me, I want that to happen as much as you do.

What will happen is that humanoid robots will continue dominating headlines while actual activity remains focused on demonstrations, pilot tests, and data collection. The industry is reaching an inflection point, but we’re still years away from widespread commercial deployment. Predictions suggest around one million humanoid robots at work by 2035; notice that’s nearly a decade away, not next year.

The more interesting development is in the capabilities being demonstrated. Recent work from Boston Dynamics and others shows robots learning through reinforcement learning in simulation before being deployed in the real world. This “train in simulation, deploy in reality” approach is maturing, and the results are genuinely impressive.

For 2026, watch for:

• Increased pilot programs in warehouses and manufacturing
• Better human-robot interaction as emotional intelligence becomes a focus
• Domain-specific applications rather than general-purpose humanoids

If you’re in logistics, manufacturing, or warehouse operations, 2026 is the year to seriously evaluate robotics solutions, `but as pilot programs and limited deployments, not full-scale rollouts.

The Hardware Squeeze: AI’s Collateral Damage

Now for the bit that’s going to directly affect anyone building PCs, gaming, or tinkering with local AI models: the hardware situation is becoming genuinely problematic.

NVIDIA is reportedly cutting gaming GPU production by 30-40% in early 2026, prioritising AI data centre chips that generate twelve times more revenue than gaming products. AMD has already implemented 10% price increases across its Radeon lineup, citing supply constraints. The reason? Memory shortages driven by insane demand for HBM (High Bandwidth Memory) for AI applications.

This isn’t just about graphics cards. RAM prices are skyrocketing due to the same AI infrastructure boom. IDC warns that the PC market could shrink by 5-9% in 2026, despite this being a year when it should boom due to the Windows 10 end-of-life refresh cycle. Average PC selling prices could rise 6-8%.

What This Means for You

If you’re a gamer or PC enthusiast, the advice is frustratingly predictable: buy hardware sooner rather than later if you need it. Prices aren’t going down any time soon. The RTX 60 series likely won’t arrive until 2027, and when it does, expect even higher pricing as NVIDIA fully transitions toward treating gaming as a premium segment.

For hobbyists wanting to run local AI models, SLMs become even more attractive. You don’t need a top-end GPU with 24GB of VRAM to run a well-tuned 12B parameter model. The democratisation of AI might actually come from smaller models running on more modest hardware, rather than everyone renting compute from hyperscalers.

The broader point is that what began as an AI infrastructure boom is reshaping consumer hardware markets in unintended ways. We’re essentially living through a modern version of the cryptocurrency mining craze, except this time it’s big tech companies doing the buying rather than individual miners.

Data Centre Investment: The New Railroad

Speaking of infrastructure, data centre build-out is projected to reach 3.5% of US GDP in 2026, I’m not sure of the UK-centric stats at the moment, in my opinion, because we’re not taking AI infrastructure seriously enough as a country. For context, that’s comparable to the historical expansion of the railroads. Big Tech capital expenditures are on track to exceed $500 billion, with companies like Microsoft, Google, Amazon, and Meta racing to keep up with current demand rather than building for speculative future needs.

This is simultaneously impressive and concerning. On one hand, it represents genuine belief in AI’s transformative potential backed by actual capital investment. On the other hand, it’s an unprecedented concentration of resources that’s creating shortages and price increases downstream.

What Should You Actually Do?

If you’ve gotten to this point, you’ve made it through my analysis work, congratulations. But, what does all this mean practically?

If you’re building AI applications:

• Seriously evaluate SLMs alongside frontier models, cost and latency matter
• Design for agent observability from the start if you’re implementing autonomous systems
• Prepare to justify your model choices and costs more rigorously
• Consider how your data architecture supports AI access patterns

If you’re in leadership or management:

• Budget for AI properly, this isn’t a side project anymore
• Understand the difference between AI experimentation and production deployment
• Focus on specific, measurable use cases rather than “AI strategy” in the abstract
• Prepare for agent-human collaboration rather than replacement scenarios

If you’re a hardware enthusiast or gamer:

• Buy what you need sooner rather than later
• Consider that SLMs might meet your local AI needs without requiring enterprise-grade hardware
• Accept that you’re competing with hyperscalers for the same components

If you’re evaluating robotics:

• Focus on specific, constrained use cases rather than general-purpose solutions
• Expect pilots and limited deployments, not full automation
• Budget for longer-than-expected development and refinement cycles

Looking Forward (Cautiously)

Technology predictions are notoriously unreliable, which is why I’ve tried to focus on trends that are already visible and backed by actual investment rather than speculation. The shift toward agentic AI, the rise of SLMs, the hardware crunch, and the cautious progress in robotics are all happening now; 2026 is when we’ll see whether they scale.

What I’m most curious about is how these trends intersect. Will the hardware crunch accelerate SLM adoption? Will agent deployments create demand for new types of infrastructure? Will the cost pressures on AI budgets drive more pragmatic evaluation of what actually needs frontier models?

We’ll find out together. Happy 2026, and here’s to a year of building useful things with whatever tools and hardware we can actually get our hands on.