Burn the Boats! We're Never Going Back Again (...again)
Artificial Intelligence has already broken everything, there's no going back. We live here now. Here's an incomplete list of what I'm holding on to and what I'm throwing away...
Thanks to some incredible work by a very small team (RW, GL, NS I see you guys, thank you and congratulations) we’re about to ship a very cool integration with OpenAI (yes that OpenAI). This project started as a vibe-coded prototype I built on my couch.
The industry is scrambling to predict what AI is going to mean, but it’s already too late. It’s already here. The ground has shifted…but we’ve been here before.
We’ve Been Here Before
Remember when we went from Client-Server to Distributed Computing (1995-2005). Remember the shift from SOAP to XML-RPC to REST (2000-2010)? Then “Cloud Native” (2010-2018). Or Mobile-First (2010-2014)? Or the great Javascript Wars from Server Side to Client-side rendering and then back to Hybrid (2010-2020). Each time, people feared obsolescence. Each time, the work changed but didn’t disappear. The people who adapted thrived.
Here is my incomplete list of everything that is already broken and everything I’m doing to embrace the change.
What’s Broken in How We Build
Kabuki Code Reviews
Pull requests and code reviews are largely performative. Linters and Intelli-sense style tools made syntax review irrelevant years ago. Now LLMs summarize logic changes into natural language and although human verification is still necessary your test automation and code coverage (which can also be generated) should provide better assurance than any human review.
The real review is: “Should we even be building this?” But most organizations haven’t caught up to this shift. They’re still doing line-by-line reviews because that’s what “rigorous engineering” looks like. It’s cargo cult behavior.
Documentation is Noise
ADRs, technical documentation, API specs, architecture diagrams - when AI reads and summarizes the code directly these become pointless. The documentation that matters is “why we’re doing this” not “how it works.”
Most of what you invested in writing is waste. If you’re in a heavily regulated industry where attestations and documentation is the currency…it’s time to start thinking about a new job.
Estimation is dead (but we’re pretending it’s not)
My team is using a lot of new tools these days. We’re exploring everything and keeping what works and throwing away what doesn’t. Velocity is spiky and unpredictable yet project managers and stakeholders still want estimates, so we go through the motions but everyone’s engaged in a shared hallucination (see what I did there?). Anything that takes longer than 6 weeks in my view needs a double-click.
What’s Broken in How We Hire & Develop
The Career Ladder is Breaking
AI is providing ways to fast track or even re-route on this path so what to do about it?
The same thing I would’ve said 10 years ago; Focus on Impact! Engineering has always been about impact. As a Manager you need to re-examine how you develop your team as the traditional playbook is obsolete. Junior and Senior engineers alike now have more opportunities to outperform each other and the gap between the rest-and-vest crowd and the bias-for-action get-sh1t-done community is only widening each day.
The Technical Interview is Broken (but we won’t admit it)
Assessing candidates through algorithm puzzles or system design questions assumed you were testing relevant skills.
The reality is we probably don’t know what to test for at this point. I’m placing increased focus on behavioral skills; Curiosity. An ability to learn. A bias for action. These traits coupled with a clear track record of delivery is what signals a hire to me. So how do we re-write the interview to test for those? Drop a note in the Comments if you’ve figured it out. I’m still working on it.
Team Size - who cares?
For me team size has never really mattered. It's always been about team impact and impact can come from the individual or from the team. AI is lowering the bar to produce results so you don't necessarily need an engineering degree. Access is being democratized and whatever engineering bits you do need to know you'll pick up along the way. For engineers, the productivity benefits are the largest gains. For Managers the ability to hire, onboard, and manage people matters less than the ability to ruthlessly prioritize and make fewer, better bets.
The net effect is the scales are tipping to favor more fully rounded individuals who can see about the product vision, understand their members, but also understand the engineering side and connect the dots to deliver value even more than ever before.
What Actually Matters?
Spoiler alert—it’s what’s always mattered
Good Taste
We are drowning in a sea of AI slop. When the tools will generate whatever you want, knowing what you want and what you don’t want becomes crucial. Understanding what a great user journey looks like, having the good taste and restraint to craft something elegant and memorable whilst solving the customer problem is what matters most.
Customer Understanding Isn’t Optional
No kidding, right? But here’s what’s changed: cycle time has collapsed. No more six-week user studies that lead to ambiguous results. You don’t have time. It’s a race and your competitors are already barking at your door.
You need a system for developing and testing hypotheses quickly with well constructed experiments in an efficient manner. Figure out what data supports your hypothesis and how to collect it efficiently. No new ideas here, this is product management 101.
Principles Over Process
A team needs to understand their mission, but understanding the rules of the road is just as important. Tenets and “Ways of Operating” have traditionally been cascaded to the team through basic management practices, stand-ups, retros, etc. but the truth is your “ability to manage” might actually be slowing things down.
Giving the team clear direction and then a clear set of tenets to guide their work, ideally baked into the culture, not through org-hierarchy and “Approvals” will unlock their true potential.
Data Informed, Not Data Driven
Jeff Bezos said it: “When the data and the anecdotes disagree, the anecdotes are usually right.” In a world of AI-generated everything, the ability to find patterns and synthesize intuition from data becomes crucial.
Numbers tell you what happened. Stories tell you why it matters and Human Storytelling becomes a super power.
What’s Not Broken Yet (But Watch This Space)
“The Hunch”
When an Incident happens tribal knowledge and human know-how is still the most effective path to mitigation. AI is providing tools to make that human insight quicker and more precise but we still need to know what to measure. Tooling to diagnose and root-cause in realtime are not far off but for now operational excellence, telemetry, observability and resiliency stills need the human touch.
Hardware is Still Hard
I mostly talk about software here, but it’s worth mentioning experience with physical device development, mechanical engineering, moving pieces of iron that work in sync with software is still very much the domain of the human engineer. I think for now we’re at the early stages but it’s exciting to see how AI can help shorten the development cycle for physical devices. Developers that understand this intersection will be at an advantage in the years ahead.
The Real Shift
The things that made you a valuable contributor are changing. Engineers are no longer the bottle neck. Prototypes can come to life in a weekend.
The skills that made you valuable were largely about managing scarcity—scarcity of labor, scarcity of compute, scarcity of time. AI is breaking all these constraints simultaneously.
It’s not about the lines of code you write. It’s about the code you DON’T write.
The next wave is all about Judgement. It’s about Influence and Risk. The choices you make and how you get to the the data-supported answer as quickly as possible. Measuring Quality by outcomes and impact versus approvals, processes and documentation.
I’m focusing on the fundamentals that don’t change. Customer problems. Clear thinking. Good taste. Communication. The technology stack has always been disposable. The fundamentals aren’t.
I’ve shared my list but what are YOU focused on? How has YOUR role changed? What tools are YOU throwing away and what new things are you picking up? Drop a note in the Comments and let’s help each other.
In a few days we will be live with OpenAI and we’ll start learning from our experiment. I’m grateful for the small but mighty team who drove our idea to this point. After almost thirty years for me in this game I still get a kick out of launching stuff and I’ve learned the more things change, the more they stay the same. It’s still smart, motivated and curious people doing the hard things. Driving change and bringing the Impact.
This isn’t the first time the ground has shifted. It won’t be the last. The only mistake is pretending it’s not happening.
Sorry, Francis, but this article rather highlights your incompetence in the area. Do you think any of the large events that you mentioned at the beginning of the article, like API format changes, Mobile and Cloud areas emerging, affected the optimal team sizes and the Dunbar number of inter-human connections? Checking basic algorithms during the interview or asking the candidate to do leetcode-type challenge instead of asking real work and experience-related questions has been criticized for decades. The documentation - you literally underlined that the good documentation should explain "why" and not "what" and then saying that AI can summarize the code, which can not explain the context in which this code was written or the solution created that ADR is supposed to capture. If we don't do estimations and the board comes to you and say - when are we going to integrate with Client X, are you also going to tell them that they are hallucinating?
Yes, AI is changing the industry; it has the potential to become revolutionary by increasing engineering productivity and potentially reducing time spent on coding in favour of solutioning, ensuring quality, and compliance. But in no way does it throw out the window the basics you mentioned in the article.
Francis. Those are the words of a wise craftsman who understands the inside, the outside, and the relationship between the two. Thank you for sharing this articulation. I agree with your assessment of what's broken, what will soon break, and what actually matters (on-the-ground) that has never and will never change. In my opinion, however, the most important point that you invite us to think about is: WHY are we building? In a world where AI is accelerating the DEGENERATION of LIFE itself through its gargantuan demand for energy and fresh water (OECD/Cornell estimate that AI uses one water bottle for 36 queries in the US), and through its negative impact on job markets (not mentioning any doomsday scenarios that the fathers of AI themselves are warming us about), we MUST consider the "why" more than ever. before. Like everyone else, I get involved with AI projects every day... and my answer to my "whys" are always "because its short-term benefits, for life or for humans, compensate for its potential degenerative costs.