Spot on. All those years of slinging code and debugging gave me and others the judgement and eye to check on all the AI generated code. I now wonder often about what hiring looks like in this new era. As a small startup, we just don't need junior engineers to do the day to day implementation.
Do we instead hire a small number of people as apprentices to train on the high level patterns, spot trouble areas, develop good 'taste' for clean software? Teach them what well organized, modular software looks like on the surface? How to spot redundancy? When to push the AI to examine an area for design issues, testability, security gaps? Not sure how to train people in this new era, would love to hear other perspectives.
Most places I worked at, seniors were expected to do the junior work, only faster. All the actual senior stuff (architecture, refactoring,code quality, you name it) is usually done either against management or as a concession to humor the devs.
Now that our ability to go fast has been supercharged, I suspect we're just going to see a massive lowering of quality across everything. We seem to be already seeing it in windows, osx, iOS, azure...
Either the market stops accepting that lowering and we see a counterpush, or people become content with 97% availability. Considering how normalized it is nowadays to have data leaks, I think the frog's already half boiled.
I don’t think this is limited to coding. We’ll eventually see it across all products and services. It’s just a matter of how much customers are willing to accept.
I unfortunately agree with this. Look how many senior engineers are already abdicating using their "judgement and eye to check on all the AI generated code" in favor of leaving an agent running all night and maybe skimming through 30k lines in the morning.
> As a small startup, we just don't need junior engineers to do the day to day implementation.
> Do we instead hire a small number of people as apprentices...
Are you not just re-describing what a junior engineer is? Someone with potential you hire to have them learn and grow on your team?
I don't understand what is different. Pre-LLM, companies seemingly expected juniors to know a ton about coding. Post-LLM they'll expect them to know a ton about LLMs.
> Are you not just re-describing what a junior engineer is?
Basically, the salary expectations are out of whack for expected output.
The 25th and 50th percentile TC for an entry level SWE in the US are $100K and $140K respectively [0].
Meanwhile, the 90th percentile TC for an entry level SWE in Canada is US$115K [1] and US$120K [2] in the United Kingdom.
Why should I hire an entry level CS major from Sacramento State or UMass Dartmouth when I can hire a UWaterloo/UBC or Oxbridge/Imperial CS grad who is guaranteed to have done multiple internships?
CoL is the same in most of US Canada, and UK if not more expensive in Canada+UK. And the excuse of "healthcare" doesn't hold either - both Canadian and American employees pay the same in healthcare fees and benefits, even including COBRA during a layoff or an ACA plan. And increasingly in the UK, our PortCos have started offering private healthcare plans becuase of NHS issues.
What is happening is globally, new grad hiring will be significantly reduced with hiring occuring at target programs where curricula and student quality is already well understood.
On the other side of the bell curve, the bottom half of apprentices globally will be trained by Accenture/Deloitte/PWC or WITCH or FPT type companies who tend to pay bottom barrel new grads around $6K TC starting salaries (which is roughly the same one could earn farming or as an automation engineer on a factory line in India, the Philippines, or Vietnam) but force them to study on the job at their education and university programs and will merge their output with GenAI platforms.
The kind of organization that viewed software as a loss leader before AI still doesn't have an incentive to hire internally even with AI. Meanwhile, companies who view software as critical to their operations will continue to expand GCCs and pick-and-choose the top tier of talent to incubate internally.
IF you are a new grad in North America, this means you need to move to a Tier 1 tech hub like SF or NYC ASAP - these are the only hubs with the right density of talent and self sustaining software hiring markets that can ensure you will find your next job if you get laid off or need to find an entry level role.
IF you are a new grad and already have a role - UPSKILL ASAP. A decently regarded online MSCS like GT or UT Austin doesn't cost more than $10K total, and other programs like UIUC's MCS or Dartmouth's MEng cost in the $20k-40k range in their entirety which is worth it. Additionally you will have to self-skill in your free time as well.
I remember in college when we got taught that early economists thought capitalism and increasing productivity from innovation would lead to less work and effort needed from people and not more.
It must have been nice to be that optimistic and not having to see how it’s actually playing out.
Economists typically point to this phenomenon when people talk about the relatively stable working hours over the last 50-60 years. I've seen some of them argue it's an issue of supply/demand, and that if people truly wanted to work less we'd see more of demand for such careers. I think this ignores that retirement/medical benefits are almost exclusively tied to jobs expecting you to work 40 hours a week.
> we got taught that early economists thought capitalism and increasing productivity from innovation would lead to less work and effort needed from people and not more
Keynes wasn't wrong. The issue is macro-level productivity is orthogonal to personal effort and productivity. And what Keynes was talking about was macroeconomics, not individuals.
For example, it takes 10% the workforce it took in early 20th century to produce the same amount of agricultural output in the US in the 21st century.
Similarly, end-to-end automotive manufacturing via industrial robots has reduced the need for a line worker who's job was to screw in a door on an assembly line.
The economy is much more productive and efficent today than it was a century ago, but automation leads to a subset of workers specializing and a larger set of workers deskilled or unemployed because they didn't upskill when they had the chance.
It's interesting to watch the same class of people who told coal miners "they should learn to code" back in the early 2010s now getting the same comeuppance.
Frankly, American SWEs got lazy and lost their competitive edge especially during the early 2020s.
>It's interesting to watch the same class of people who told coal miners "they should learn to code" back in the early 2010s now getting the same comeuppance.
There are millions of software engineers in the US alone. Don't put all of them into a single bucket.
> It's interesting to watch the same class of people who told coal miners "they should learn to code" back in the early 2010s now getting the same comeuppance.
When I told people to learn to code in that situation it was with pity and I would talk to them about how I felt forced to do so after I graduated with a useless degree during the Great Recession.
It was more of a “here’s one of the few growth areas left that are feasible to self teach”, rather than contempt for people not being on the same class as me.
> Frankly, American SWEs got lazy and lost their competitive edge especially during the early 2020s.
If “competitive” edge at this point means needing to get a masters on top of needing to train unpaid on your free time I think it’s more that corporations in America have gotten to the point of wanting increasingly rare or expensive to acquire skills in their labor force, while simultaneously deciding that they will be paying approximately $0 in any and all training costs.
AI is only accelerating that as every manager and exec is drooling at the mouth at the idea of never hiring juniors again. It’ll be some other assholes problem like their future self who has to deal with what happens after the lack of people in training finally catches up to the industry.
> It was more of a “here’s one of the few growth areas left that are feasible to self teach”, rather than contempt for people not being on the same class as me
> If “competitive” edge at this point means needing to get a masters on top of needing to train unpaid on your free time...
Doesn't it suck when being asked to completely retool and reskill in the middle of your career.
I never said it didn’t. I also wanted tech workers to unionize while we had the power because I expected this the second it was feasible, but alas we have no more leverage.
> I also wanted tech workers to unionize while we had the power
You could, but that does nothing to prevent job losses, as can be seen with Hollywood completely offshoring to the United Kingdom [0][1] despite SAG-AFTRA and WGA dominating the entertainment industry.
Or even the loss of the entirely unionized coal industry.
The economics of IP-driven industries require an entirely different approach from manufacturing industries.
You can't ignore economics. This is what globalization looks like.
Yea, I remember hearing corporations say that threat and then everytime they tried to outsource overseas they got trash that they had to live with or throw out and redo.
It is global, which means that talent that was good enough already was able to command high prices. If we had unionized we could have at least extracted some worker protections or stop shit like the Jobs anti poaching cabal.
The coal industry was lost because the technology has been nearly obsoleted, but if you're going to cast tech work as being part of an IP-driven industry then please don't mix in resource extraction to the conversation.
I'm not opposed to unionizing - in fact I think cooperative models are underutilized in the tech industry.
What I am saying is unionizing wouldn't stop offshoring. The reality is the world in 2026 is much more developed than the world when you graduated (I'm guessing 2009).
As such, skills that were worth a premium in 2011-16 aren't viewed as differentiators and more as table stakes knowledge.
Essentially, Unionization doesn't help if it doesn't also increase barriers to entry.
I have no incentive to hire someone in NC or IL for a premium who isn't actually a top performer, when I can gladly hire someone of better caliber for less in the UK or Switzerland even with supposed workers benefits (though in action, they're largely comparable for SWEs across North America and Western Europe).
Frankly, I would much rather hire a CS new grad from TU Munich over a CS new grad from Random State University even if this means I have to deal with German labor laws, because the calibre I am getting from the TUM student is on par with what I'd get out of Stanford or MIT for a fraction of the cost.
Additionally, most countries are expanding their subsidizes to incentivize companies to build R&D centers and bring IP-driven industries there. American state and local governments have largely quit that game in order to concentrate on culture war politicking (my experiences with NC's state government left a really bad taste in my mouth after attempting domestic inshoring in the late 2010s and early 2020s).
So in this kind of world where companies are perfectly fine decamping to other jurisdictions who give the red carpet, what is the solution? It isn't unionization (it isn't a blocker from a hiring perspective for IP-driven industries but it also doesn't stop offshoring), but it's about stable governance and risk management at the state and local level, which is where companies interface the most.
It will also require accepting that developer salaries have to reduce significantly - I can't justify training someone from scratch at US$100K TC, but I can at US$75K TC.
When your senior developers retire, and if the LLMs haven't caught up to their level by that time, where do you think new senior developers will come from?
We've actually been here before with higher languages. Assembly is actually a higher language, performance is much worse than machine code. It cant really self modify or do code generation. To squeeze all of the wine from the rock you do need 100 times more effort. C is luxurious compared to assembly. Python is even more productive. We don't use html/css/javascript because it is so fast, it's gawd aweful slow. I can however get something up and available to the world in less than a minute.
Then we pretend to be optimizing our websites for performance but we have no idea what code is triggered by our instructions. If the button responds in 0.2 seconds we are good. You know, the time it takes for the cpu to do 1-2 trillion instructions?
As much as I get push back by saying since AI, I never look at the code and I can still be sure it meets the functional and non functional requirements, no one has been able to dispute my methodology.
For functional requirements I review both the unit and more often the integration tests and make sure they align with the requirements.
For security, I validate the API endpoints can’t be accessed without authentication and these days I use Amazon Cognito.
The host environment - lambda, EC2, Docker runtime (ECS/EKS) have IAM roles attached with least privilege.
Then someone asked about multi tenancy and RBAC. Since mostly I deal with B2B clients with large business customers, each customer gets their own AWS account.
For RBAC, DynamoDB and Postgres at least on AWS both have Row level security that you can tie to a user or group - again authorized by Cognito or IAM. Even if the code does miss something - it’s still protected.
The database itself doesn’t have access to the outside world and if I can, I don’t even assign a user to the database and use the AWS Data API when possible that uses the AWS Control plane and IAM. If I do end up using a database use - it again has leash privilege.
Of course UX and UI testing has to be done manually.
I do carefully review the “works on my machine” and “works with small dataset” footguns - like concurrency implementations and I also have scalability tests.
Solo here, no devs to fire. What's changed is the bar for "worth building" dropped massively. Stuff I'd have shelved as too small to justify the time, I just do now. The risk is that you end up with a lot of half-finished things if you're not disciplined about finishing.
People tend to forget that big businesses have been bootstrapped with worse stuff than some MVP vibe code website.
I have a vague memory of a hotel reservation "app" that was just putting lines in a Google Sheet where the founders grabbed the info, called the hotels themselves and reserved the room.
When they got too many requests to keep up, they slowly automated the bits they could one by one.
Been looking for something like this for a side project. The embedded mode with no external deps is the killer feature for me, hate dragging in a server just to do graph traversal. Going to give it a shot.
Sign extension bugs are the worst. Silent for ages then suddenly everything is on fire. Spent a lot of time in C doing low-level firmware work and ran into the same class of issue more than once. Nice writeup, congrats on the patch.
Went indie after a long run at a big company and this hits. The hardest thing wasn't the work, it was getting comfortable with the silence between shipping and seeing any signal. Still working on it tbh.
You are correct, the data is not client side encrypted. Data entry is either manual, or for many major brokerages we offer a drag/drop import of CSVs. There is also a generic CSV upload capability.
Good valid points here though on being in the privacy 'middle ground'. There is some trust involved with having your transaction data live on our server, and that we execute correctly on our promise of only extracting transaction data from the (optional) CSV upload process.
Your comments have sparked some ideas on how we could go deeper in the privacy direction, so thanks for taking the time!
EDIT: Noted on the landing page having more emphasis on the full asset type support, thanks for the feedback.
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Do we instead hire a small number of people as apprentices to train on the high level patterns, spot trouble areas, develop good 'taste' for clean software? Teach them what well organized, modular software looks like on the surface? How to spot redundancy? When to push the AI to examine an area for design issues, testability, security gaps? Not sure how to train people in this new era, would love to hear other perspectives.
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