Startups, E2E tests, and uncapped value💡
Monday Ideas – Edition #145
Welcome to a new edition of the Monday Ideas! Every Monday I send you a few engineering / management ideas to start the week on the right foot!
Let’s dive in! 👇
1) 🤖 AI dev tools are solving the wrong problem
This idea is brought to you by today’s sponsor, Unblocked!
Every year Stack Overflow releases their developer survey, and year after year the results remain the same: most developers feel that they are not as productive as they wish they could be. In the 2024 survey:
📚 Knowledge gaps — 53% of devs get blocked every day by knowledge gaps
🔍 Search for info — 63% spend more than 30 mins a day looking for info
💬 Help others — 49% lose more than 30 mins a day answering questions
This shows that the biggest challenge in software development isn’t writing code. It’s finding the context to know what code to write.
What you need is a way to find answers without having to search across a dozen tools or interrupt teammates.
2) 🚀 Should you start a startup?
We made a new video! It’s here 👇
We talked about ideas, startups, and VCs. Here are some takeaways:
🧩 The right person for the right idea — becoming a successful founder requires 1) being an expert in the problem you're solving and 2) knowing how to build a solution (through tech chops and/or good distribution).
🔍 Find your "mediocre soup" — many opportunities lie at unique intersections of unrelated skills that, combined, create a powerful advantage. Even if none of those skills are world-class on their own!
💰 VC money is for bottlenecks — raise venture capital only when you're obviously bottlenecked in a way that only money can solve, and you know exactly how that investment will accelerate growth.
🐢 Bootstrap as long as possible — delaying fundraising gives you better chances of success and better terms when you do raise. Modern AI tools also make it possible to go further with less investment.
🔋 Energy trumps everything — the number one factor for startup success is not giving up, and the key to not giving up is managing your energy, not your money or time.
Do you like this? Videos are still a new thing to me, so let me know in the comments! Either here or on Youtube.
3) 🔍 Why is end-to-end testing expensive?
It is common wisdom that end-to-end testing is hard — but why is that?
End-to-end tests are not particularly hard to write. Popular frameworks like Playwright and Cypress are widespread and easy to use. The cost of testing comes from other reasons:
Speed 🏎️
End-to-end tests need browsers / device simulators, which makes them slow. Very slow. To run them fast and in parallel, you need powerful infrastructure and a sophisticated pipeline.
Reliability 🩹
End-to-end tests generate a lot of false positives, also known as flaky tests.**
To put it in context, false positives are not much of a concern for unit and integration tests — but for end-to-end ones, you may have up to 20% false positives per run.
But why is that? The simple answer is that the higher the level of the test, the more things you have below that can fail. With E2E, this includes anything from network latency, to external API calls, browser quirks, and infrastructure issues.
So, every time a test fails, you have to figure out whether:
It is just a flaky test and a re-run fixes it, or
The product is fine but the test needs to be updated, or
It is an actual bug.
Only in the third scenario the test is useful — in the other ones it is a net negative.
All of this makes end-to-end tests high maintenance, which is what discourages many teams: especially early on, and if your team isn’t experienced, the time spent investigating tests is typically higher than doing manual QA.
I wrote a full piece about how to do modern QA properly a few months ago 👇
4) 🔧 AI will bring more tech work, not less
In all the tech companies I have known, there has never been a shortage of work.
There has never been a time, during quarter planning, or looking at the backlog, where someone said: “well, we don’t have much to do! This will be a light [quarter | month | week]”
That’s because tech and product have uncapped value: more engineering time just means more opportunities and more business.
Which means, if we equal the use of AI to more productivity, that the most likely outcome of that productivity is: we will just get more things done.
You can’t say the same for everything.
Think of a cleaning service: let’s say there are 3 people who come clean your office twice a week. If all of a sudden you were able to triple their productivity, would you still employ three of them to do three times the cleaning? Probably not, because cleaning has capped value — there is only so much you can do.
Tech is not like this. I am convinced software would be even more pervasive than it is today, probably by an order of magnitude, if it weren’t for the fact that 1) we don’t have enough engineers, and 2) code is extremely expensive to write and maintain.
So, what are companies going to do with more productivity? Are they going to lay people off to keep getting the same amount of things done as before, or are they going to do more with it? To me, it is safe to say they are going for the latter.
But this won’t be true for all companies. There exist many companies that see tech as a cost center with capped value. They believe there is only so much you can get from it, and in some cases, they may even be right!
Well, these are the cases where you may see layoffs. These are the cleaning cases, where tech is employed as a commodity. These cases exist, but they were probably bad jobs to begin with, so I think we shouldn’t focus on them.
My prediction is that good companies will use AI to do more, while bad companies will use AI to cut costs.
I wrote many more predictions in this recent piece from a couple of months ago 👇
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I wish you a great week! ☀️
Luca