Let’s do a quick thought exercise. Imagine that tomorrow (at 10 a.m. PST, of course), a major AI lab announces an incredible breakthrough. A new model has achieved AGI (Artificial General Intelligence) and can now perform at or above human-level intelligence. How would it impact engineering and product teams, and how quickly?

The most likely outcome? Most organizations would see the announcement, come into work the next day, and keep doing business as usual.

One of the most important things to understand about AI’s impact on teams isn’t the technology itself, it’s how organizations will be forced to evolve around it. While many focus on individual AI tools and capabilities, the real story is about organizational adaptation and the inherent advantages that startups and early movers will capture.

Despite the rapid advances in AI-assisted coding, most engineering organizations today are built around human capabilities and constraints. Their workflows, hierarchies, and decision-making processes exist because humans think and work in particular ways. AI doesn’t operate within these constraints, it introduces fundamentally different strengths and limitations that don’t map cleanly onto traditional org structures.

This creates an interesting tension.

Year 1: Surface-Level AI Adoption

In the first phase, we’ll see what appears to be steady progress. Engineering teams will integrate AI-powered coding assistants, automated testing, and AI-driven documentation. Product managers will start developing AI expertise, particularly in areas like prompt engineering, risk assessment, and AI model evaluation.

At a glance, these changes will seem transformative. But beneath the surface, most organizations will still be fundamentally human-centric. AI will be bolted onto existing processes rather than driving structural change.

For example, a PM today might use AI to summarize customer feedback faster or generate rough PRD drafts more efficiently. An engineer might lean on AI pair programming to speed up boilerplate coding or refactor existing code. These are real productivity gains, but they don’t fundamentally change how teams are structured, how decisions are made, or how work gets prioritized.

Year 2: AI-Native Reorganization

The real shift begins in Year 2, when forward-thinking companies realize that marginal improvements aren’t enough. Real unlocks come about when we think AI first. Early adopters will recognize that traditional org structures, designed around human strengths and limitations, aren’t optimized for AI-native workflows.

This is where "AI-Native Reorganization" comes in. It’s not just about using AI tools. It’s about rethinking the entire development lifecycle around AI’s unique strengths and constraints.

What does AI-Native Reorganization look like?

• AI-first development workflows: Instead of engineers manually writing code with AI assistance, teams might shift to AI-first development cycles where engineers primarily review, validate, and refine AI-generated code. AI agents might own entire feature development lifecycles, with humans acting as supervisors rather than primary builders.
• Radically different team structures: Instead of a traditional structure of PMs, engineers, and designers, teams might consist of a much smaller group of “AI Orchestrators” who specialize in curating, prompting, and integrating AI capabilities across multiple domains.
• AI-prioritized decision-making: In AI-native teams, decision-making could shift away from human intuition toward AI-driven strategic planning. AI models might analyze competitive landscapes, user behavior, and system performance to suggest features, roadmaps, and priorities. Humans will still be required to ultimately provide oversight and approval, but not necessarily to kick off the decision from the beginning.

We’re already seeing early signals of this shift. Brex, for example, has redesigned its front-end architecture to be optimized for large language models (LLMs), enabling AI-driven workflows.

 Box is also actively rethinking how its products integrate AI at a fundamental level:

Now, imagine a startup launching today as an AI-native company. Instead of hiring a large engineering team, it builds with a much smaller core group of AI operators who use AI agents for everything from software development to marketing copy generation. This startup doesn’t just use AI, it structures itself to maximize AI’s potential from day one. We’re already seeing this trend starting now, and this is likely to accelerate going forward. 

There is, however, an elephant in the room: job loss. The phrase "AI productivity gains" implicitly suggests increased efficiency derived from fewer people accomplishing more, translating into potential headcount reductions, slower hiring, or both. This is not merely hypothetical, it's a real, tangible impact on people's livelihoods. Organizations must acknowledge this human reality transparently and responsibly, proactively supporting employees through reskilling, redeployment, or thoughtful transition planning. While embracing AI-driven efficiency is essential, companies should balance these gains with genuine empathy and commitment to workforce transitions.

Year 3: The Compounding Advantage Becomes Unstoppable

By Year 3, early adopters will start to demonstrate undeniable advantages in speed, scalability, and adaptability. Meanwhile, lagging organizations will realize, likely too late, that their incremental AI adoption hasn’t kept pace with the structural changes happening around them.

This is when the urgency sets in.

Consider Google’s Code Red moment after the release of ChatGPT. Seemingly overnight, Google recognized that its existing AI strategy wasn’t moving fast enough, triggering a major internal restructuring that brought DeepMind and Google Brain closer together. Two years later, Google is releasing state-of-the-art AI models at an accelerated pace, demonstrating the strategic value of rapid AI-driven organizational change.

This follows a familiar technology adoption curve, but with a crucial difference: AI-native organizations gain advantages that compound faster than traditional digital transformations. The speed at which AI improves means that organizations designed to leverage AI at their core will continuously outpace those that are merely adapting AI into legacy structures.

The Startup Advantage: A Unique Window of Opportunity

This dynamic creates an unprecedented opportunity for startups. Unlike incumbents, startups don’t need to undergo painful restructuring, they can be AI-native from the start.

AI-native startups benefit from:

• Smaller, more efficient teams – Instead of hiring large engineering orgs, they rely on AI agents for core functions, keeping teams lean and costs low.
• Faster iteration cycles – AI-native product development moves at a different pace, where features can be built, tested, and refined in days rather than months.
• Better AI-first decision-making – These companies make AI-driven decisions at every level, from product roadmaps to marketing campaigns, without the legacy constraints of human-centric org structures.

For example, a hypothetical AI-native SaaS company might operate with just five full-time employees but leverage AI agents to handle customer support, automate code generation, write marketing copy, and conduct sales outreach. They don’t need an entire operations team, they simply integrate AI-driven workflows from the beginning.

This isn’t a hypothetical, there are many real-world examples of incredibly fast-growing startups with very small teams (pulled from this post by Ben Lang):

‍The Choice: Proactive or Reactive AI Transformation

Engineering leaders today face a crucial decision. They can proactively reorganize their teams to be AI-native, leveraging AI’s strengths in ways that traditional structures can’t. Or they can wait, only to be forced into reactive AI-driven transformation under competitive pressure.

The latter isn’t just more difficult, it’s existentially risky.

Organizations that delay until Year 3 won’t just be behind in AI adoption; they’ll be simultaneously struggling to reorganize while trying to catch up on lost technical capabilities, institutional knowledge, and strategic positioning.

The most important question isn’t whether companies are adopting AI tools, it’s whether they are fundamentally restructuring to capture AI’s full potential. Those that do will dominate. Those that don’t will struggle to remain competitive.

This is the moment where AI moves from enhancing businesses to reshaping them. The question is: Who will adapt in time?

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