Like it or not, vibe coders are the next software engineers.

3 years ago I made a prediction that triggered a mixed response:

Within our lifetime. We will see a YouTuber or streamer becoming head of a state.

Me (March 4, 2022)

Whilst I don't believe this prediction has come true there's been progress. In June 2024 a Cypriot YouTuber was voted to become a member of the European Parliament, he earned 19.4% of the vote and earned 40% of votes from the 18-24 age group.¹

The interesting thing about my prediction is that it seems that it's actually gone the other way. More politicians are becoming YouTubers and Streamers.

Could the same thing happen with vibe coders? Perhaps software engineers are the next vibe coders.

We like to bash

We see software engineers being dismissive at the content aimed at vibe coders. There's a new wave of people being introduced to coding and managing complexity; so most of the content is covering the basics. I.e. Write tests, compartmentalise and plan things out before you dive into the code.

This wave of programmers haven't had the time to digest The Mythical Man-Month to learn; upfront planning in software leads to a huge reduction in downstream costs. They are however learning the hard way, by hitting these challenges head on. (For better or worse).

How did you get here?

It's all a journey and we're at different stages of the process. A large overhead to programming is building up the vocabulary, this is the struggle for both early stage developers and vibe coders.²

Experienced programmers have been exposed to more language and can therefore provide more specificity when commanding the computer, vibe coders will get there. Perhaps this specificity makes the experienced programmer a better vibe coder. Maybe it's their keyboard.

No one was born with the knowledge of how the computer works, there's hurdles to overcome. It was only a decade ago we were cringing at someone stating they're full-time YouTubers or an Instagram influencer, and look, they've still got you glued to your screen.

76% of developers are either already using or plan to use ai assisted tools as part of their workflow, 82% of these developers cite an increase in productivity as the largest impact from using these tools¹

AI has integrated itself into our tooling, from search and planning to completely embedding itself into our development environment. There's a drive to apply LLMs into our workflow and it's important to see what works and what doesn't.

There's no doubt that these tools will be adopted by developers and we should figure out how best to use them, or risk being left behind.

The great bot cloud in the sky

We will never stop imagining machines taking over our lives. It's been 43 years since the release of 'Blade Runner' and 26 years for 'The Matrix'. However, more significantly, it's been 8 years since I released my existential twitter bot Dennis.

Dennis was essentially a cronjob that ran every hour. He would read through ~100 comments on roughly 10 subreddits covering topics on existentialism and philosophy. Pulling this knowledge from the depth of Reddit he was able to spew 40.5k tweets of garbage for about 5 years.

Dennis trains a Markov chain and would begin his sentences with a random starting word. So OpenAI and Github weren't the first to train language models using the data available to them on the internet. I get the feeling that I was onto something in 2017 and with enough funding I could have trained either a personal life coach or replicated teenage angst.

YC & a16z, you're missing out here.

Becoming a cyborg

We are now able to integrate these large probabilistic models directly into our code editors. Previously I've been using the tab key to autocomplete single words, with a copilot assistant I can write an in-line comment such as "func provides post-order iteration of a tree given root" and my copilot will suggest the entire function.

The code can be wrong, but in the end the developer is responsible for the code that gets checked into the codebase. It's easy to say "yup, looks good" especially when under pressure to ship. Despite this I find it a massive boost to productivity since I only need to fill in some gaps or slightly modify the code-spit. On occasion I find myself feeling like I'm walking in mud when I hand write the code without this autocomplete feature. There are also occasions when I disable the feature altogether because it's context switching in its suggestions.

Perhaps it's problematic not knowing where this code came from. At least with Dennis; I knew I could rely on his thoughts of existence due to having control over his training data, but the code that a copilot provides me...

🤷 who knows...

Is it a star sign or an LLM?

Search has been disrupted. The only people that were thinking more than Dennis about their own existence was the Google board when OpenAI released ChatGPT. Just kidding, Google came in second, first place goes to Stack Overflow.

A big chunk of the job in software engineering is trying to discover if anyone else has faced a similar problem and if they've solved it. We are also tasked with understanding an API or how a library works or if it can be integrated in order to solve something or provide a new service.

Google was good for this, but it's slowly being consumed by adverts and medium articles. Giving straight forward answers doesn't seem to be Googles' focus. Stack Overflow is an alternative, but you're not going to plug your homework into a question and get someone else to do it for you, nor will anyone provide free assistance so that you can earn a salary, but they're helpful in ways none the less.

Claude, Copilot and ChatGPT are stark improvements in this area as you can do a bit of back and forth, provide clarity on what you're looking for and they'll happily bend it to your use-case, without referring their mate for the job because they get paid for ad-clicks. (Well not yet).²

The rocket is taking off 🚀

AI integrated environments for coding are certainly on trend and we are already rolling eyes at the phrase "Vibe Coder".

Entire classes of university students are scoring 100% on homework assignments, so professors are having to rethink how they assess their classes. I recall in high school a student questioned why we couldn't bring our calculators into an exam when we'd have access to this in the real world. There's a cohort of graduates coming into the workforce that's going to be more dependant or more adept at using AI tools so it's worth getting a sense for how these tools might be used.

While having access to Cursor I have found that I can have a working solution to a small problem that I'm facing in about an hour. Before cursor I might have taken note of my idea and then forgotten about it or spent the whole weekend putting it together. It's even better when you have a clear vision of how you wish the solution to look. The models don't do very well if your ideas are vague and you're relying on them to apply their best guess. It's multiple times more beneficial when you know what a good result should look like.

We will see more bespoke software. This could be a good thing, since we might find something better to match our preferences when it comes to tools. However due to the increase in productivity I've found myself leaning more towards, let me make that myself, instead of looking for existing solutions. This could be more pronounced in inexperienced software engineers as they will be less familiar with the tools that exist and they might power down their own path of reinventing the wheel.

With more bespoke software comes fewer people familiar with said software and perhaps we will be running into problems that have already been solved in the past. This is certainly a cyclical part of learning, so we might see us enter a new cycle of how we manage our server deployment, but this time with a Gen Z finesse.

Just because you can

I've also found myself questioning how I should dedicate my time more, if I can spit something out in an hour I'm question my design and interface far more. Maybe it's because that's actually the fun part of programming and the coding is going to take less time so I've got more time to assess if I'm thinking about the problem correctly.

I've found this not to be the case with less experienced developers, as they'd prefer to use this time to fit one more feature into their service without questioning if that feature makes holistic sense.

Are thing going badly?

There are weaknesses with AI tools in their current form, such as solving an entire problem from A-Z instead of using the library I wrote to solve A-M and focusing on N-Z. I've also found weaknesses when searching for things that are case sensitive. Seems like Google is still on top when I provide a search in quotations.

Now that I've been using it for a while I have developed some intuition on the strengths and weaknesses, despite this I still find myself hand writing code line by line without any assistance. Mostly in more complicated areas of the code base, spaces I'd like be familiar in. Having an understanding of the system is valuable when you want to contribution to discussion, help stake holders and make decisions independently.

It's still valuable to build intuition and understand how others might use these tools. Building on this knowledge could help create solutions that were previously out of reach, or just get you a seat at the table of decision making. In the end it's good be be familiar with the tools, it might help you avoid the odd looks when you bring out your dusty book of log tables.

Further Reading:

• Martin Fowler: Exploring Gen AI

Footnotes