As of February 15, 2026, three major AI developments are reshaping the industry: (1) DeepSeek’s R1 open-source reasoning model released January 2026 proved Chinese firms can match frontier capabilities, (2) OpenAI’s operator agents now autonomously browse and interact with websites, and (3) context engineering has emerged as the critical skill for AI workflow optimization. These developments mark a shift from model performance competition to orchestration and infrastructure value. The model layer is commoditizing while value accrues to context engineering, multi-agent coordination, and trust infrastructure.
The Model Layer Is Commoditising (And That Changes Where Value Sits)
In January, DeepSeek (a relatively small Chinese firm) released R1, their open-source reasoning model. It shocked the Valley because they did it with limited resources. No massive compute budgets, no gigawatt-scale clusters. They just built it, and it works.
The lag between Chinese releases and Western models is shrinking from months to weeks. Silicon Valley apps are quietly shipping on top of Chinese open models. This isn’t a one-off, it’s a pattern.
What this tells us is simple. The model itself is becoming a commodity. If a small team in China can match frontier performance with constrained resources, then the model isn’t where competitive advantage lives anymore.
I’ve been saying this for months (to anyone who’ll listen, frankly). The value concentrates in the layer between the model and the task. Not the inference engine itself, but the harness you build around it. The context architecture. The information structure that tells the model what to pay attention to, what to ignore, and how to connect the dots.
We used to call this “AI-ready data architecture” when we were being polite. What it actually is, is context engineering. And if you’re not thinking about this layer explicitly, you’re building on sand.
What you should do on Monday: Audit one AI workflow you’re currently running (customer support, content generation, data analysis, whatever). Map out exactly what context you’re providing to the model. I’m not talking about the prompt, I mean the full context that’s available to the system. What data sources does it access? What structure does that data have? Where does it break down? Most people find their context layer is held together with duct tape and hope. That’s the problem to solve, not which model to use.
Context Engineering Is Where The Money Went (We Just Didn’t Have The Language For It)
Anthropic’s Claude Code is generating a $2.5 billion revenue run rate. That’s not revenue from a better model, it’s revenue from a harness. The model matters, but what actually makes Claude Code valuable is the structure around it (how it accesses codebases, how it maintains context across files, how it handles errors).
Entire raised $60 million in seed funding to build infrastructure for agent-human collaboration. Again, not model development. Infrastructure. The layer between intent and outcome.
This is the bit that gets me, and I’m going to be very frank here. I’ve been building this stuff for years in attribution modelling and CDP architecture. When you improve a ROAS by 5x, it’s not because the attribution model suddenly got smarter. You restructured how it received information, what it could see, how it connected data points. You changed the harness.
Same thing with agent effectiveness now. If your agents are inconsistent, it’s probably not the model. It’s that your context layer breaks down under specific conditions you haven’t mapped yet.
If you’re deploying agents (or planning to): Stop thinking about “prompt engineering” and start thinking about “context harness design”. Document the full information architecture your agents operate within. What can they access? What can’t they? Where does context get lost between systems? This isn’t a prompt problem, it’s an infrastructure problem. Treat it like one.
The GEO Tracker Isn’t What You Think It Is (And Neither Is AEO)
I’m building a GEO tracker with my co-founder Morten. Most people think it’s an “AI citation checker” (did ChatGPT mention your brand?). It’s not that, or at least that’s not what makes it valuable.
What it actually does is reveal how AI systems construct understanding from your information architecture. It shows you where your context harness is failing. Not “are you visible to AI” (surface-level question), but “when AI systems try to answer questions in your domain, where does your context structure break their reasoning process?”
This matters because Generative Engine Optimisation (GEO) and AI Engine Optimisation (AEO) are being treated as content tactics. Write FAQs, add semantic triples, structure your schema markup. That’s all fine, but it’s surface-level optimisation.
The real question is whether your information architecture serves as an effective harness for AI decision-making. Can an AI system reliably construct accurate understanding from how your information is structured? Or does it have to guess, interpolate, hallucinate?
I’ve been running attribution models and CDPs for years. The pattern is identical. If your data structure is unclear, the system makes up answers. They might be plausible answers, but they’re wrong. Same thing happens with AI engines now, except the stakes are higher because the AI is directly answering user queries about you.
Here’s what I’m seeing in early testing (we’re not launched yet, but we’ve run diagnostics). Most organisations have context architecture that works fine for human navigation but falls apart when AI systems try to extract structured understanding. The information is there, but the relationships between concepts aren’t explicit. AI systems either miss critical context or over-index on irrelevant patterns.
What you should do on Monday: Pick your three most important content assets (product docs, thought leadership, case studies, whatever). Give them to ChatGPT, Claude, and Perplexity with the same question about your domain. Compare not just whether they cite you, but how they construct their answer. Where do they get confused? Where do they conflate concepts that should be distinct? Where do they miss relationships that should be obvious? That’s your context architecture failing. Document those failure points. That’s your roadmap for infrastructure improvements, not content rewrites.
Agentic Workflows Finally Got Their Plumbing (MCP Is The Boring Infrastructure That Makes Everything Work)
Anthropic released the Model Context Protocol (MCP) and called it “USB-C for AI”. Accurate description, if a bit dry.
What it actually means is that AI agents can now talk to external tools (databases, search engines, APIs) without custom integration work for each connection. OpenAI embraced it. Microsoft embraced it. Anthropic donated it to the Linux Foundation’s Agentic AI Foundation. Google’s building managed MCP servers for its products.
This is infrastructure, not sexy product launches. But infrastructure is what enables everything else. Before USB-C, you needed seventeen different cables to connect devices. Before MCP, you needed custom integration for every agent-tool connection.
Now you don’t. Which means agentic workflows can actually scale beyond proof-of-concept demos.
I worry sometimes that people are building agents without thinking about the context harness those agents operate within. An agent is only as good as the information it can access and the structure of that information. MCP solves the access problem (plumbing). It doesn’t solve the structure problem (that’s still context engineering).
What you should do on Monday: If you’re experimenting with agents, start building with MCP-compatible tools. Don’t reinvent integration plumbing. But do invest serious time in designing the context harness your agents operate within. What information do they need to complete tasks reliably? How is that information structured? What happens when context is incomplete or ambiguous? Test those edge cases deliberately. Most agent failures aren’t model failures, they’re context failures. Fix the harness.
The Privacy Reckoning Started (And It’ll Get Messier Before It Gets Clearer)
Mozilla launched a “one-click” privacy tool in Firefox (early February) that lets users opt out of AI training datasets. It sends requests to AI developers to delete browsing history and contributions from their models.
This is just the beginning. Regulatory tug-of-war between federal and state governments (particularly in the US) is intensifying. ChatGPT launched advertising in February, which changes the implicit contract between users and AI systems. People thought these were neutral tools, but advertising integration shifts that perception.
The practical bits: Audit your data practices now, not later. Build first-party data infrastructure if you haven’t already. Assume that access to third-party training data will become more restricted and expensive. The organisations with strong first-party data and clear consent mechanisms will have structural advantage as regulation tightens. Also, if you’re using AI tools for competitive intelligence, be aware that advertising integration might bias outputs. Cross-reference critical decisions against multiple sources.
World Models Are The Next Architectural Shift (But Most People Don’t Need To Care Yet)
Here’s the difference that matters. LLMs predict the next word based on language patterns, but world models understand how things move and interact in 3D space. What we’re seeing is a shift from language-based reasoning to spatial reasoning.
Yann LeCun launched AMI Labs (Advanced Machine Intelligence), seeking €500 million at a €3 billion valuation. Google DeepMind has Genie. Fei-Fei Li’s World Labs launched Marble. General Intuition raised $134 million in seed funding to teach agents spatial reasoning.
This is a fundamental architectural shift, not an incremental improvement. We’re hitting the limits of scaling transformers. The next leap comes from systems that can model physical reality.
Honestly, unless you’re in robotics, autonomous systems, or design tools, you can probably watch this from the sidelines for now. But if you are in those sectors, start tracking world model developments closely. The applications will cascade faster than people expect.
What you should do on Monday: If you’re not in robotics or spatial industries, file this under “monitor but don’t act yet”. If you are, start experimenting with how world models might change your design or simulation workflows. The pattern to watch is where these systems get integrated into existing toolchains. That’s when they’ll matter for most businesses.
Google Shipped Personal Intelligence (And Changed The Search Game Quietly)
In January, Google rolled out Personal Intelligence in Gemini. It connects Gmail, Google Photos, YouTube, and Search for personalised responses. They launched “AI Mode” in search, AI tools in Gmail for free users, and integrated AI across Chrome with features like “auto browse” for complex tasks.
This matters because search and discovery are fragmenting across platforms. Google isn’t just a search engine anymore, it’s an answer engine with deep personalisation. Same with ChatGPT, Perplexity, and increasingly social platforms.
You can’t optimise for one channel and call it done. You need a portfolio approach to visibility. And your information architecture needs to work across all of them, not just traditional SEO.
What you should do on Monday: Map where your audience actually discovers information now. Not where you wish they discovered it, where they actually do. For most B2B organisations, it’s ChatGPT, Google AI Mode, LinkedIn, and maybe Reddit. For consumer brands, add TikTok and Instagram to that mix. Test how your content performs in each environment. Don’t just check if you’re cited, check how AI systems construct answers about your domain. Then redesign your information architecture to work across platforms, not just for Google’s traditional crawler.
Short-Form Video Keeps Winning (And AR Glasses Are Coming This Year)
Instagram Reels, YouTube Shorts, TikTok, Snapchat Spotlight. All still dominating attention. Threads is on track to overtake X in total active users sometime this year, partly because it’s leaning into real-time sports content whilst X courts controversy.
Snapchat’s launching AR Specs (actual AR glasses you can wear) this year. Meta will follow to compete. This opens up entirely new formats for promotion and discovery. Pop-up notifications based on location and product shown directly to wearers.
I’m not going to pretend I’m a video expert (I’m not), but the pattern is clear. If you’re not building repeatable video frameworks, you’re already behind. Not one-off viral attempts, systematic production.
Your move: Build a video framework, not a video. Design for hooks in the first 3 seconds. Test what works. Iterate. If you’re in retail or location-based business, start thinking now about how AR discovery changes behaviour once functional glasses reach consumers. You won’t have long to adapt once it hits.
The AI Sameness Problem Is Real (And It’s Justifying The Backlash)
Three in four marketers say AI-generated creative risks making brands look identical. 86% have already seen AI outputs that resemble competitor content. Gartner put GenAI in the “Trough of Disillusionment”. MIT reported that 95% of generative AI pilots are failing to deliver measurable business value.
The backlash is starting. Too much “AI slop”. Too many generic outputs that don’t get engagement or algorithmic distribution.
Honestly, this was predictable. If everyone uses the same tools with the same prompts optimising for the same metrics, of course the output converges. That’s not an AI problem, it’s a strategy problem.
What you should do on Monday: Use AI for scaffolding and structure, not final creative work. Let AI handle research, outlines, first drafts. But don’t publish AI outputs directly without substantial human editing for voice, perspective, and cultural specificity. The brands winning right now use AI to amplify human creativity whilst maintaining distinctive voice. The brands losing let AI do everything and wonder why nobody cares.
What This Actually Means For You
We’re six weeks into 2026. AI moved from experimentation to operational deployment. The model layer is commoditising. Value concentrates in the context layer (the harness). Marketing became more valuable but more technically demanding. Privacy is tightening. Discovery fragmented across platforms.
The advantage is shifting from those with the biggest models to those with the best infrastructure around those models.
Most organisations aren’t building that infrastructure. They’re chasing model improvements, prompt tactics, surface-level optimisations. That’s the opportunity.
If you want to survive 2026, stop chasing headlines. Build infrastructure. Invest in context engineering. Design for discoverability across fragmented platforms. And don’t let AI make you sound like everyone else.
The game changed, and most people haven’t noticed yet. That’s your window.
I’m building a context engineering diagnostic method (maybe even tool) that reveals where your information architecture breaks AI reasoning. If you want to know when it’s ready, follow me on LinkedIn for more on context engineering and AI infrastructure.
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Pal Erik Waagbo 