Two Biggest AI Trends for 2026

Tools that dominate in 2026 will be the ones that bring real, measurable value.

The last two years were driven by experimentation and momentum. AI tools were adopted quickly, often ahead of clear cost models or proven outcomes. Entering 2026, that tolerance is fading. AI is no longer new, and it is no longer cheap. Buyers are more disciplined, and internal teams are under pressure to justify continued spend.

Valuations and usage metrics are no longer enough. AI companies will increasingly be asked to prove value in hard terms: reduced incidents, shorter lead times, lower operational costs, or clear revenue lift. Tools that generate more output without reducing real work will struggle to convert. The market is shifting from curiosity-driven adoption to outcome-driven purchasing.

One of the clearest efficiency levers is memory. Recomputing context on every interaction is expensive and fragile. Persistent memory — retaining system understanding, architectural decisions, and org structure — reduces token usage, stabilizes output quality, and lowers reliance on ever-larger models. Context engineering will become an efficiency discipline.

Sustaining AI’s growth trajectory depends on infrastructure catching up. Compute, energy, and data center capacity are no longer abstract concerns. They directly influence cost, latency, and availability. As demand grows, teams are pushed to optimize workloads, reuse context, and design systems that can operate within real-world constraints. Efficiency becomes a prerequisite for scale, not an optimization.

Marked as the ‘agentic year’, 2026 will push AI automation to the next level.

Across organizations, AI is no longer something teams opt into. It is increasingly the default way work gets done. From coding and testing to documentation, incident response, and internal operations, AI systems are being embedded directly into workflows. Human involvement shifts from initiating every step to overseeing execution.

Developers increasingly interact with systems by specifying goals, constraints, and expected behavior. Instead of instructing how something should be built, they define what should hold true. This reframes development as system direction rather than execution, with AI handling the mechanical translation.

By 2026, many organizations are explicitly designing for multi-agent workflows. Specialized agents handle discrete responsibilities such as coding, testing, security, release management, and coordinate through shared context and memory. This approach enables efficiency and resilience, but also requires new visibility, coordination, and control mechanisms.

We will likely see convergence from dozens of separate tools towards a single ecosystem that acts as a control plane where developers supervise all workflows. MCP has already confirmed this trend, and its applications will continue to expand. This will fundamentally change user behaviour as many tools will become data sources that connect to the main source of truth.

2026 will be another major year for AI, but for different reasons. Many existing tools will likely fail, but the winners will see better adoption and sustained long-term growth.

If you’re an engineer, focus on designing autonomous workflows that scale through real system context and intent.

If you’re building in the AI space, focus on bringing measurable value and deep integration with agentic workflows. This will dictate adoption and help you scale.

Got a burning engineering question? Just hit reply and we’ll tackle it. And if you enjoyed this issue, consider sharing it with your teammates.

Want to share your dev tool, research drops, or hot takes?

Submit your story here — we review every submission and highlight the best in future issues!

Till next time,