May Walter

11 Predictions for 2026 Every year I make a list of predictions & score last year’s predictions. 2025 was a good year : I scored 7.85 out of 10. Here are my predictions for 2026 : 1. Businesses pay more for AI agents than people for the first time. This has already happened with consumers. Waymo rides cost 31% more than Uber on average, yet demand keeps growing. 1 Riders prefer the safety & reliability of autonomous vehicles. For rote business tasks, agents will command a similar premium as companies factor in onboarding, recruiting, training, & management costs. 2. 2026 becomes a record year for liquidity. SpaceX, OpenAI, Anthropic, Stripe, & Databricks IPO, with SpaceX & OpenAI ranking among the ten largest offerings ever. The pent-up demand from 4+ years of drought finally breaks. Fear of disruption by fast-growing AI systems drives defensive acquisitions exceeding $25b as incumbents buy rather than build. 3. Vector databases resurge as essential infrastructure in the AI stack. Multimodal models & world/state-space models demand new data architectures. Vector databases grow revenue explosively as they become the connective tissue between foundation models & enterprise data. 4. AI models execute tasks autonomously for longer than a workday. According to METR, AI task duration doubles every 7 months. 2 Current frontier models reliably complete tasks taking people about an hour. Extrapolating this trend, by late 2026, AI agents will autonomously execute 8+ hour workstreams, fundamentally changing how companies staff projects. 5. AI budgets receive scrutiny for the first time. Buying committees & boards push back on AI spend. Small language models & open-source alternatives rise in popularity as research labs determine how to specialize them for particular tasks, achieving state-of-the-art performance at a fraction of the cost. Developers prefer them for 10x cost reductions. 6. Google distances itself from competitors via breadth in AI. No other company achieves breakthroughs across as many domains : frontier models, on-device inference, video generation, open-source weights, & search integration. Google sets the pace, forcing OpenAI, Anthropic, & xAI to specialize in response. The era of every lab competing on every frontier ends. 7. Agent observability becomes the most competitive layer of the inference stack. Engineering observability, security observability, & data observability fuse into a single discipline. Agents require unified visibility across code execution, threat detection, & data lineage. This marks the beginning of the confluence I predicted in 2025 : the three observability spaces finally converge. 8. 30% of international payments are issued via stablecoin by December. The efficiency gains in cross-border settlement are too large to ignore. As regulatory clarity improves in major markets, stablecoins move from the periphery of crypto to the core of global trade finance, displacing traditional SWIFT rails for a significant portion of B2B volume. 9. Agent data access patterns stress & break existing databases. Agents issue at least an order of magnitude more queries to databases & data lakes than people ever did. This surge in concurrency & throughput requirements forces a redesign of the overall architecture for both transactional & analytical databases to handle the relentless demand of autonomous systems. 10. The data center buildout reaches 3.5% of US GDP in 2026. The scale of investment mirrors the historical expansion of the railroads. The only factor that slows overall building is perceived risk within the credit market, particularly in the private credit market. The massive growth in that asset class suddenly shows strains of increasing default rates, creating a potential bottleneck for the most capital-intensive infrastructure projects. 11. The web flips to agent-first design. Most developer documentation & many websites become agent-first rather than people-first. This shift occurs because many purchasing decisions are now informed first through agentic research. Consequently, the front door needs to be designed for robots, while the side door caters to people.

it’s interesting when you find yourself in a place where you’re just shoveling context for the models. e.g. i have this obscure bug, let me add some logs so the model gets more context from runtime. this problem is somewhat solved, so the next step becomes performing the action that actually produces logs: reproducing bugs and explaining the environment it occurred in. once all of this is in place, it’s a pretty good flywheel that can solve most bugs

what i think is important when working with coding agents - integrate runtime context - frontload with context - use models for what they are best at - optimize review flows - make it easy to move between abstractions (code, language, etc) pt. 2 later