AI Signal – January 2026

Key Signals This Month

Emerging Signals

AI chip geopolitics. The physical infrastructure behind AI – who makes the chips, who controls the supply – is becoming a geopolitical issue. If export controls tighten or supply chains shift, the cost and availability of AI tools could change quickly. Not directly about how your organisation uses AI, but it sets the ceiling on what's possible.

AI-native tools replacing, not just augmenting. There's a growing conversation about whether AI-powered tools will replace legacy business software entirely, rather than sitting on top of it. If that shift accelerates, the "right tools" question changes from "which AI assistant?" to "do we still need this entire software category?"

"Human-made" as a market signal. Some markets are seeing early demand for "human-verified" or "human-made" labels – the AI equivalent of organic certification. If this trend grows, it could affect how you position AI-assisted work to clients and stakeholders.

AI agents organising themselves. One experiment in January involved thousands of AI agents self-organising into something resembling an economy. It's too early to mean anything practical, but it's worth noting: most current thinking about AI assumes humans directing it. That assumption may not hold forever.

Enterprise slowness is structural, not just cultural. Jensen Huang's observation that enterprise is always the slowest adopter reframes expectations. If you're in a mid-market or enterprise environment and feel behind, that's normal. The question isn't how to move faster – it's how to move well.

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The Framework: January Deep Dive

Each month we map signals to four tiers that cover everything from the tools themselves to how you actually use them. This is the structured analysis behind the headlines above.

Tier 1: The Landscape

What's available – and what changed

The "best" AI tool changes every few weeks. The leading models – from companies like OpenAI (ChatGPT), Anthropic (Claude), and Google (Gemini) – now take turns outperforming each other. Six months ago, picking the right model mattered. Today, what matters more is how your team uses whichever tools you have.

Costs are in freefall. Jensen Huang (NVIDIA CEO) put a number on it at CES in January: the same AI capability that cost thousands a year ago is now over 100x cheaper. Every major provider dropped prices significantly in January alone. The direction is clear and accelerating.

The infrastructure is maturing. The connections between AI tools and business systems are becoming standardised. What was experimental a year ago – AI assistants that can read your documents, search your data, draft from your templates – is now production-ready tooling. The question is no longer "does this work?" but "are we set up to use it?"

Tier 2: The Foundation

What your organisation needs in place

AI governance is growing up. Anthropic rewrote Claude's entire operating constitution in January – and the big shift was from a checklist of rules to a narrative document that teaches reasoning. The old version was "don't do X." The new version is "here's how to think about trade-offs." They released it as public domain, so anyone can use it as a starting template. If your AI policy is still a list of do's and don'ts, it's already outdated.

Adoption is messier than headlines suggest. CJ Desai, President of MongoDB, talks to ten-plus large customers a week about AI. His read in January: AI writing assistants (like Microsoft Copilot) are delivering "not great" value in most deployments. AI coding tools are a genuine breakthrough. Customer support chatbots are "still tinkering." The pattern isn't that AI doesn't work – it's that most organisations haven't done the groundwork to absorb it. The gap between capability and readiness is where most companies are stuck.

Half of all jobs are now AI-touched. Anthropic (the company behind Claude) published research putting it at 49% – up from 36% just a year ago. And it's not just routine work. AI disproportionately covers tasks that previously required specialist knowledge. This isn't a future trend. It's the current reality, and most organisations haven't adjusted their hiring, training, or team structures to account for it.

Tier 3: The Practice

How your team should actually work with AI – this month's deep dive

Before you conclude "AI is bad for learning," here's the part that matters more.

It's not about using AI less. It's about using it differently.

The study found two distinct patterns of AI use:

Same tool. Same people. Completely different outcomes based on the interaction pattern.

Why this matters beyond coding

This isn't a story about developers. It's a story about every knowledge worker using AI right now. When you ask ChatGPT to draft an email and hit send without thinking, that's delegation. When you ask it to outline three approaches to a problem and then choose between them, that's inquiry.

The split Jensen Huang described at CES applies here too. He argued that AI automates tasks but humans expand into purpose. His example: despite 100% of radiology images being AI-processed, the number of radiologists has grown – because the purpose of the role expanded. But that expansion only happens if people develop the judgment to direct AI, not just accept its output.

Tier 4: The Application

Where AI is being applied – and what's emerging

Industry-specific AI is the dominant direction. Jensen Huang's prediction: the next five years are about depth in specific industries, not general-purpose tools. Generic AI gets you to 90% accuracy. Industry-specific solutions get you to 99.999%. The gap between those two numbers is where domain experts – and specialist advisors – create value. If you're in sustainability, finance, legal, or any regulated field, this is directly relevant.

Non-technical teams are building software. AI coding tools have matured to the point where people without engineering backgrounds are creating working applications. The bottleneck has shifted from building to knowing what to build and verifying it works. Good judgment about what your business needs – not technical skill – is becoming the scarce resource.

AI "agents" are getting practical – but context is everything. An agent is an AI that can take actions, not just answer questions – booking meetings, processing data, drafting and sending documents. One experiment in January staffed an entire company with AI agents. The finding: their success depended almost entirely on how they were set up – what information they were given, what boundaries they had, and when they should stop. Setup determines outcomes, not raw capability.

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Framework Check

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AI Signal is published monthly by Pandion. We help organisations build real AI capability – the foundations, the practice, and the fluency that turn tools into results.

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