Economics and politics are determined by the most compelling stories and occasionally it is hit by reality. This is one of the challenges when following reactionary discourses on platforms like Linkedin, Reddit and Twitter.

When advice comes from board members and VCs I am reminded that they scroll the same threads on reddit that I do. Their opinion is often shaped by the highest voted comment in these forums.

Unfortunately we aren't gripped by stories that are filled with the context and the caveats that exist in the real world and this has shifted us into more extreme political leanings. Why should we fill our content with details when this jeopardizes the opportunity of going viral.

False narratives get the clicks and impact stocks because they're entertaining and persuading. We are willing to sit still and listen to the stories that are novel and gripping. 14 years ago Elon said we would land people on Mars in 10 years. (15-20 years in the worst-case).

including caveats all the time makes articles too awkward to read and buries your actual point

• Sean Goedecke

No one finds being moderate sexy.

The boring parts of software

The engineers that have the largest impact are the ones that read the most documentation. Getting through software specs is tough, they're not filled with fluffy prose and they're often dense and technical, but nothing delivers value like saying: "We don't need to do that work, that feature already exists in the API".

Similarly, the largest mistakes I've seen made in software come from conclusions being made too quickly after reading a single page of documentation. Engineers also avoid reading documentation by jumping on a band wagon of agreement without verification.

This extends to how reactionary takes tend to mold their opinions. Life is easier when someone does the work for you. If things go belly up, hey that wasn't my misinterpretation.

Where's it shifting?

This doesn't just apply to engineer's, Steve Eisman had this to say about the finance industry in 2008, but this applies to everyone.

I think one of the hardest things for all human beings, me too, to deal with our paradigm shifts. You know, you exist in a paradigm. It's been around from a very, very long time. Your whole career is based on that paradigm. you've made a lot of money in that paradigm and then it turns out that the paradigm is either changing because of technology or maybe the paradigm was actually wrong because it was based on continuously increasing leverage which is what the financial services industry's paradigm was based on human beings have tremendously difficult time dealing with paradigm shifts. Tremendous.

It's like a nightmare. They don't want to deal with it.

• Steve Eisman A.k.a Steve Carell in The Big Short

r/ExperiencedDevs is an echo chamber degrading Ai slop and crafting existential dread in the industry. They assert that Ai can never be as detailed or accurate as they are when it comes to writing code, at the same time they're shaking in their boots about the future of their industry. When there's commentary on the benefits of AI these comments tend to be downvoted or deleted and the blame is put on automated bots making up pro-ai agitprop.

Software engineers tend to be defensive when it comes to generative code and state that most of their job wasn't code to begin with.¹ These engineers aren't out of the "Ai makes mistakes" phase or they're moving goal post to take the target off their back.

The latest cohort of uni students are winning hack-a-thons without knowing how their applications works. r/ExperiencedDevs needs to come to terms with the unknown and as long as this industry has been around we've never actually known how projects work in their entirety. We've never read every line of code in our dependencies, not knowing how something works and being able to make contributions is not a new phenomenon. Those that have succeeded in this industry can wade through the unknown and that will continue to be the case.

It's normal to have strong feelings about the grads picking something up in a short amount of time that may have taken you longer to hone in on. It's the classic "back in my day" reaction.

Extremes

Unfortunately avoiding nuance leads us to extremes.

The truth is; software engineering looks different and requires different skills at different stages of the business and stack. These complaints might just be a misalignment with the business.

Ousterhout describes in his book "A Philosophy of Software Design" the difference between strategic coding and tactical coding. Where tactical coding relies on hacks to get the job done and strategic coding has a more long term view on the code base. He vouches that software should be done strategically but I believe we need to pick our battles.

There are some entities and functions that are core to engineering companies, but they become core at different times and we should make an attempt to recognise when this change occurs.

It is nice to pretend that your code is an integral part to the business's existence. The truth is a project is always initially an experiment and over time it is rewritten to more accurate specification as we learn and the business learns. Importance is discovered. Your first attempt will be done when you know the least about the topic.

Eventually it might become a core service to many teams within the company and at that point it's worth getting serious about engineering practices. Front loading your engineering standards is making your experimentation more expensive.

If you're treating every project like a personal flower garden you'll struggle to recognise when code is dead weight. Thank it for its cycles, praise it for the outage it caused, the war story and what you have learnt. Then delete it.

Software is about discovery. Code generation enables us to prototype and discover how things work. Prototype for your own education not just for the customer. The pace of learning has increased and we can discover and experiment quicker than ever.

Information workers can no longer build their career on asymmetric process knowledge. The person in the office that knew how to work the niche corporate workflow to get their way will be replaced by documentation and an MCP server.

A large overhead for new employees is understanding processes at a company or relying on someone to inform them how things are done. The archetype of that odd senior taking pride in the ability to recall the weird edge cases of the internal admin portal hasn't left yet.

Products will change

Products that avoid allowing their user to voice demand, intentions and needs; will soon feel like exploring a webpage built in the 90s, cute, quirky and stuck in the past.

The need for an experienced power user has been replaced by a Claude or a Gemini. If the LLM can't do it, should we deprioritise it and focus on the things it can do? We've never had time to explore product features and it doesn't help when the UI isn't sorting the buttons by their relevancy to the problem I am having, there's a gap here and your competitor might be offering me a quicker route to market.

UX design and effort is built around imitating interfaces that are already familiar to the audience, for example we still use keyboards optimised to avoid jamming the hammers in a typewriter, a problem modern keyboards no longer have. There's a learning curve that needs to be overcome when using systems, new and old.

The interface can create friction.

We are shifting our interactions with the product to be ask and you shall receive.

If you aren't empowering the ordinary to become power users over night you continue to have an under utilised product with features that customers need but can't find. Search is more relevant than ever.

Don't leave features on the table. Create the power customers.

As an aside my grandmother no longer needs me to teach her how to upload images to instagram. It would appear her ChatGPT assistant has taken my job.

Learning will change

Software has had the most prevalent increase in power users; here the iteration cycle for learning has diminished significantly. If you've built your career around building products through proof, developed learnings and iteration, that muscle now allows you to run micro tests 500 times a day.

The gap self learners and builders are creating will only increase as we've exposed them to a tutor with 24/7 office hours. Some might argue that the models are not deterministic but I'd lean more towards a computer that has read the entire corpus of human experience for references than a tutor that hit their Goodreads target of 12 books this year.

Anecdotally; the cycle time of learning to code used to be about 20mins of searching or syntax look ups. That has been cut down to seconds and if OpenAI is giving us garbage we can test if it works quicker than it can write. There are few industries that can give feedback on a decision as quick. In some industries you have retired by the time a decision unravels.

The speed at which adopters can upskill has plummeted but that's only if they're conducting a piecemeal development approach and not generating an entire slop product or forgetting why there are files in a folder called src/.

As with science there's no way to determine what change led to what outcome if we are changing everything all at once. Don't replace ceteris paribus with prompt-and-pray.

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