What is Hypervelocity Engineering?

Following on from my post on Becoming an AI Engineering Team, I wanted to talk about a term I’ve been seeing a lot in the context of the new engineering paradigm – Hypervelocity Engineering (HVE). I want to talk about what this term means to me – and even more importantly, what it doesn’t mean. This evolution is not just about efficiency, but about survival, and some careful up-front thinking can save a lot of pain down the road.

Velocity is Key

To me, I see the distinction between hyperspeed and hypervelocity to be quite important. Velocity is a vector – implying both speed and direction. It’s not about developing code fast, it’s about developing quality code with solid engineering more quickly. This isn’t vibe coding, it’s a team working together with AI help to execute much more effectively.

Hypervelocity Engineering is not vibe coding

Velocity has another ramification – engineering teams use developer velocity as a key metric to determine how much work can be pulled every sprint. An AI Engineering Team operating at hypervelocity should be able to pull many more story points every sprint. This is quality work, though, that fits with the goals of the project and is built to the standard of the team – “move faster and break more things” is not the goal of HVE.

“move faster and break more things” is not the goal

HVE In Practice

So what does “pulling more work” look like in practice? It may mean going from a long cycle of building paper prototypes and scheduling meetings with stakeholders to go over them, to mobbing around a Claude window with those same stakeholders and generating interactive dashboard prototypes in real time.

It may mean codifying cross-cutting best practices (observability, error-handling, code contracts) into AI hints and letting it sprinkle those into the code while you work. It may mean your data scientists writing experiment “throwaway” code are suddenly writing well-structured code with unit-tests, finding subtle evaluation bugs that in the past would have caused significant rework or have caused them to pursue a hypothesis based on faulty results. ADRs and the code implementing them can be co-developed, meaning when you take the ADR to the team for discussion, you’ll have concrete data from your implementation to help justify it, and the AI will ensure future code adheres to the decision.

It means building AI guidance and adapting it over time, resulting in both increased velocity and longer-term increased acceleration. Code is secure by default, robust by default, and efficient by default because your team has made the effort to set out your engineering expectations for both your team and your AI collaborators to read. Code is well-factored into small, atomic files not only because it’s good engineering practice, but because AI Agents tend to rewrite entire files so small files reduce code churn and token usage and decrease the complexity of merging. Multithreading safety issues, memory issues, off-by-one errors, and other common but subtle code issues are rarely introduced by the AI or are caught by the AI reviewers. This leaves your SDE reviewers to concentrate on overall structure and whether the code they’re reviewing advances the project in the right direction and delivers business value.

common issues are caught by AI, leaving SDEs to review the code’s business value

Pairing becomes a default instinct – not just among coders, but across the entire team. Your TPM always has a “pair” to bounce ideas off and collaborate with, sharpening stories before bringing them to the team, and collaborating to devise risk mitigations and persuasive strategies for winning over stakeholders. Her “pair” has “read” Crucial Conversations, knows HBR well, has no ego, and wants no credit.

Your designers can discuss color theory and complementary colors, quickly iterate on storyboards and mockups, and collaborate on how to convince the team to adopt design changes that might wind up pushing the schedule but will result in a better customer experience.

pairing becomes a default instinct – not just among coders, but across the entire team

Your security SMEs no longer need to threat-model solo, working with their “pair” to identify attack surfaces, do black-mirror thinking, and come up with risk mitigations. Rather than suggesting ideas to the team around secret-scanning and code-injection prevention, they can work with their pair to modify the team’s AI guidelines, ensuring all future AI-written and AI-validated code will adhere to their recommendations.

Hypervelocity Engineering is not limited to Engineers. Hopefully some of the examples I’ve given above have convinced you that a multi-disciplinary AI Engineering Team can only achieve hypervelocity if the entire team reinvents itself in the context of AI. This may mean that some disciplines blend, and may mean entirely new disciplines are formed. Some of these may be short-lived (remember “prompt engineers”?) as AIs advance, while others may prove long-term valuable.

Getting Started with HVE

The transition requires more than just providing AI tools—it demands rethinking how teams collaborate, measure success, and deliver value. Begin by:

1. Identifying your team's highest-friction activities

2. Creating shared engineering standards that both humans and AI can follow

3. Encouraging cross-functional pairing with AI assistants

4. Measuring both immediate velocity improvements and long-term quality outcomes

We’re entering an evolving word, and it will be important for teams to embrace flexibility and curiosity without giving up the core principles making up well-built software systems that bring true value to their users.

What's your experience with AI-assisted engineering? Has your team discovered practices that boost velocity without sacrificing quality? I'd love to hear your thoughts in the comments.

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