PRODUCTHEAD: Move fast and break things – AI edition

Jock Busuttil

10 months ago

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curated by Jock Busuttil.

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tl;dr

What happens when anyone in your org can build and ship product?

GenAI is creating a productivity disparity between companies using it by default and those which are not

Investors are increasingly valuing companies based on ‘revenue per employee’ (≈ efficiency)

hello

GenAI tooling means organisations already set up to succeed can do so more quickly. The same is true for those already set up to fail.

“Here, when someone wants to build something (anything) – from internal tools, to marketing pages, to writing production code – they turn to AI and… build it. That’s it.”
‘The rise of the AI-native employee’ by Elena Verna, Elena’s Growth Scoop, 28 June 2025 (accessed 5 July 2025)

What would happen to your organisation when anyone can build and ship stuff rapidly?

Imagine it: the marketing team can set up and run a new tracked campaign with a call-to-action embedded right in the product whenever they need to – no need to trouble the dev team to help.

Do you see this as way to empower teams to solve their own problems and to shorten time to value?

Or do you see this as the path to fragmented initiatives, inconsistency and chaos?

I think it all rather depends on the organisation. As a thought experiment, let’s take two extremes:

A. Clear strategic intent and principles, high autonomy

There are organisations with a clear vision and strategy, understood and embraced by everyone working there. Present anyone in the organisation with an opportunity and they’ll probably have enough context to decide whether it lines up with the goals (the intended direction of travel) and principles (how competing trade-offs are resolved) of the organisation.

These are the kind of places where people are trusted to make good decisions for themselves, and to let the right people know what they’re doing, why and what results they achieved. Because of this, senior management is comfortable delegating to them the responsibility to build what they need, when they need it. As a result, these organisations can be relatively nimble.

B. Unclear / vague intent, low autonomy

On the other hand, there are organisations where vision and strategy are unclear, poorly understood, or so vague that anything looks like it would line up with them. In the absence of clear strategic intent or guiding principles to resolve conflicts in approach, people will default to doing whatever serves their own immediate interests. Everyone will move in different directions and chase different goals, often with the best of intentions.

In these places, autonomous decision-making can still happen, although the results will be unpredictable and fragmented. Teams are islands, only sharing information when forced to. Competing initiatives spring up and partially cancel out each other’s benefits. Senior management reacts to the divergence and inefficiency by retaking control over all lower-level decision-making, creating bottlenecks and drag.

In type A organisations, being able to react to an opportunity more quickly amplifies each autonomous team’s existing capabilities. Good context and alignment help them to make better decisions more often, and good communication maintains coordination between the teams.

In type B organisations, teams suddenly having the power to ship whatever their hearts desire means the effects of ill-informed decision-making are felt more frequently, and speed of delivery combined with the underlying lack of alignment only accelerates the existing fragmentation of initiatives.

Elena’s article has been giving me a feeling of déja vû. As she puts it: ‘“Move fast and break things” is back – but this time with 10x output and 1/10 the mess.’ It’s like we’ve shot back a decade and are speedrunning the Agile and Lean Startup years all over again. GenAI-powered delivery is going to accentuate the differences between the type A and B organisations at each end of the spectrum, just as Agile and Lean did.

In my view it’s not the tooling or the frameworks or the methodologies alone that seemingly give type A organisations superpowers. Their organisational culture already sets them up to succeed, only now they can do so more quickly.

The same is true for type B companies already set up to fail. Everyone building whatever they want with genAI will either accentuate their own dysfunction, or they’ll become so bogged down in red tape to exert control over the process that they’ll be left for dust by everyone else.

Of course, A and B are two ends of a broad spectrum. Most organisations will sit somewhere in between these two extremes. Even then, some parts of the organisation may be closer to types A or B than others. Becoming more type A depends on a clear strategy, and on establishing trust and transparency in both directions – from senior management to the people on the ground and back again.

For you this week #

Elena Verna is experiencing the tornado of growth at her AI-powered app building startup. Even with a background in B2B SaaS growth, the pace has taken her by surprise (and she sounds like she’s been drinking the Kool-Aid a bit).

It’s undeniable that demand for this kind of productivity boost is stratospheric right now, so it’s understandable that fast-moving companies are making hay while the sun shines. However the ubiquity of genAI tech creates a comparatively low barrier to market entry, and there are already many competing platforms doing similar things. Market saturation will happen eventually.

According to Leah Tharin, a factor that’s partly fuelling this demand is how investors are using ‘revenue per employee’ as a new proxy measure for SaaS company efficiency. Investors are looking at these AI-native startups achieving astronomical annual recurring revenue (ARR) with a handful of employees and asking why other companies in their portfolio seem so bloated by comparison.

Given salaries are still a significant proportion of a company’s cost base, the investors’ mandate to CEOs is simple: generate more revenue with fewer people. As Leah puts it: ‘… it’s a reflection and reminder of the purpose of a company: to generate value for shareholders, ergo dollars. Not to employ people.’

Speak to you soon,

Jock

what to think about this week

The rise of the AI-native employee

I’ve been at Lovable for five weeks and yeah… I’m not in Kansas anymore. This company operates on a completely different level – and as someone who’s spent my entire career in traditional tech, I’m seeing a very different pattern here that’s worth sharing.

Lovable is blowing past crazy revenue milestones: $1M ARR in just 8 days post-launch, $17M in 3 months, $60M in 6 months, and $80M ARR in just 7 months. With ~35 people. That’s not a typo. That’s the new normal – if you’re AI-native. And I don’t just mean the product is AI-native. I mean the people are.

When you can move this fast, the cost of failure plummets

[Elena Verna / Elena’s Growth Scoop]

The 250k per Employee cultural KPI causing layoffs in tech

The short summary of what’s happening right now in SaaS:

SaaS companies above >$1M ARR in the US,

2020: ~5,000 (Pre-pandemic, pre-AI) (48% of those with PLG)
2025: ~15,000 (Post AI, PLG Efficiency) (72% of those with PLG)

Or if you want to have it even more condensed:

There’s more and more SaaS companies trying to get money of the same cake and there are no signs of that slowing down.

Why our jobs will always be an uncertain bet

[Leah Tharin / Leah’s ProducTea]

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