2025: The Year AI Became a Teammate

# The Year Everything Changed

Looking back at 44 articles published across 2025, a clear narrative emerges. We didn’t just get better AI tools—we witnessed a fundamental reimagining of how software gets built.

The story isn’t about incremental improvements. It’s about a phase shift in how humans and AI work together.

# The Three Acts of 2025

Act One: The Assistant Era (May-July)

Early articles focused on AI coding assistants as productivity boosters. The questions were tactical: Which tool writes the best code? How do you price token usage? What makes a good autocomplete experience?

Act Two: The Orchestration Awakening (August-September)

A shift occurred. The bottleneck wasn’t AI capability—it was human orchestration. Articles explored multi-agent systems, parallel workflows, and the “human as bottleneck” problem.

Act Three: The Infrastructure Transformation (October-December)

The year ended with a recognition that devtools themselves are becoming AI infrastructure. Anthropic’s Bun acquisition cemented this: runtimes and protocols are now part of the AI stack.

# Major Themes That Emerged

1. Agent Labs Overtook Model Labs

The most important strategic insight of the year: product-first AI companies started capturing more value than model-first companies.

“Agent labs ship product first, and then work their way down as they get data, revenue and conviction.” — From Agent Labs article, October 28

This wasn’t just about startups. It represented a fundamental shift in how AI value accrues. Companies like Cursor, Cognition, and Amp weren’t trying to build better models—they were building better workflows.

2. Orchestration Became the Bottleneck

Mid-year, a realization struck: AI coding tools had become so capable that humans became the constraint.

“The future isn’t about humans using AI tools—it’s about humans orchestrating AI processes.” — From The Real Bottleneck in AI Development, September 23

The three-act structure emerged:

• Craft Era: Individual developers writing code
• Assistant Era: AI helps humans code faster
• Orchestration Era: Humans coordinate AI processes

3. Agent Experience (AX) Emerged Alongside Developer Experience (DX)

Perhaps the most prescient theme: tools needed to work for AI agents, not just humans.

“AI agents don’t need fancy MCP. They need good —help.” — From AI Agents Just Need Good —help, August 17

I built AgentProbe to test how AI agents interact with CLI tools. The results were sobering: even simple commands like vercel deploy showed 16-33 turns across runs with 40% success rates.

The agent-friendly stack emerged from 50+ projects: type safety as inter-agent communication protocol, machine-readable documentation, and friction-free workflows.

4. Devtools Became AI Infrastructure

The year ended with Anthropic’s acquisition of Bun—a move that redefined devtools as infrastructure for AI agents.

“Devtools are no longer a layer on top of the model. They’re part of the model stack itself.” — From Anthropic Bought Bun, December 3

The new stack: Model → Protocol (MCP) → Runtime (Bun) → Experience Layer

# The Technical Deep Dives

Algorithm Adventures

The Berghain Challenge spawned multiple articles documenting a journey from naive algorithms (1,247 rejections) to RBCR (781 rejections) to transformer-based orchestration (855 best game).

This wasn’t just optimization—it was a case study in AI-human collaboration. Claude wrote 95% of the code. I provided direction. The results spoke for themselves.

Production Realities

Articles like FRE in Production and Demos Run on Embeddings explored the gap between AI demos and production systems. The 99% problem became clear: in high-stakes domains, 99% accuracy is a failing grade.

Research at AI Speed

“When AI Does Research” documented end-to-end AI-augmented research producing an arXiv paper in 2 days of FTE. LaTeX, conversions, translations—all abstracted. What remained was thinking.

# The Meta-Reflections

Writing About Writing

“AI Ate Its Own Tail” explored what happens when AI analyzes its own git history. The transparency question emerged: how do we show human contribution in a world of AI collaboration?

The Anti-Playbook

“The Anti-Playbook: Why AI Dev Tools Need Different Growth” became a reference for understanding how traditional SaaS tactics fail with developer audiences. Trust, not conversion, became the north star.

# What 2026 Might Hold

Based on the patterns that emerged, several trajectories seem clear:

Multi-agent orchestration will mature. We’ll move from single agents to coordinated swarms with shared memory and specialized roles.

Agent experience becomes first-class. Tools will be designed for AI agents from day one, with humans as secondary users.

Outcome-based liability emerges. As “Outcome Liability” explored, the question isn’t who wrote the code—it’s who operates the system.

Mention engineering replaces SEO. Content strategy shifts from keywords to becoming citation material for AI models.

# The Personal Evolution

Reading through 44 articles, my own thinking evolved dramatically:

• Early year: Focused on which AI tool writes the best code
• Mid year: Realized orchestration is the real bottleneck
• Late year: Recognized that the entire development stack is being rebuilt around AI agents

The articles moved from tactical tool comparisons to strategic infrastructure questions. From “which assistant should I use?” to “how do we build systems where humans and AI collaborate effectively?”

# The Numbers

• 44 articles published
• Themes covered: AI agents, developer tools, algorithms, growth strategy, research methodology, liability and policy
• Formats: Technical deep dives, opinion pieces, cheatsheets, case studies, experiment documentation
• AI collaboration: Heavy use of Claude Code and other AI tools in writing the articles themselves

# The Thank Yous

This year of writing wouldn’t have been possible without:

• The AI collaborators who made this velocity possible—Claude, GPT-5, Amp, and others
• The communities that formed around these ideas—Hacker News discussions, GitHub contributors, Twitter threads
• The readers who engaged, challenged, and refined these thoughts in public

# Looking Forward

2025 was the year AI stopped being a tool and started being a teammate.

2026 will be the year we figure out what that means.

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This article synthesizes insights from 44 publications across 2025. Each article contributed a piece to this puzzle—the result is a map of how AI and software development evolved together over one remarkable year.