Before we begin
If you’ve read my previous post you’re already aware that my role was recently eliminated, as a budget saving measure. As a co-founder of multiple startups, I begrudgingly understand their reasoning and the math. As a human being, who at 55 years old will now have to try and navigate a rapidly evolving job landscape, it still hurts. Also bear in mind that I have an acerbic sense of humor.
Where are we now?
The thinking behind the current industry-wide layoffs, as far as I can tell, boils down to shrinking headcount in order to free up their budget so those that remain can continue their Tokenmaxxing to generate an unprecedented amount of new code and features. All of the Frontier Models (i.e. Anthropic, OpenAI and Google) they currently use to empower this are essentially giving away cloud compute in order to build up their customer base. So, in the short-term, this does make some kind of sense fiscally.
However, all of this new code is only as good as their engineering prompts. Sure, these LLMs have skimmed Uncle Bob’s books and understand the basics of SOLID methodology; however, they rarely consider YAGNI and often miss on DRY across multiple pull requests. Additionally, they will lack any tribal knowledge for why certain choices had previously been made unless that’s been included in the engineering prompt. To top things off, engineering teams are grappling with increasing Review Fatigue that is allowing more and more bugs to slip through into Production systems.
What are they are missing?
The corporate overlords (who are celebrating the latest cloud migration that was so successful thanks to their careful guidance) are now happily converting their on premise data centers into executive offices overlooking the cube farms where their underlings toil away. They have already forgotten what they should have learned long ago in college. This is a recurring pattern across multiple industries, and the good times aren’t going to last. The AI companies (who if not already traded on the Stock Market are busy planning their own IPOs) will soon have to recon with their shareholders, who will insist that they start raising their fees in order balance their books.
We saw this during the Dot-com Bubble of the late 90’s into 2000, as well as the Stock Crash of 1929 that lead to the Great Depression. The AI companies are currently burning through investor capital at an alarming rate, building new data centers for the anticipated surge in demand. To round things out, they are essentially passing Other People’s Money amongst themselves to shore up their balance sheets. Nvidia is investing their profits into the AI companies, who in turn hand the cash right back to them for more and more powerful GPUs to power these data centers.
At some point, a winner will be crowned in the AI Race, and all of the losers will be forced to declare bankruptcy in order to pay off their debts. And, if you believe the hype, this will happen before the close of this decade.
Where do we go from here?
As the fees escalate, this will cause development teams to look for cheaper alternatives. The laptops that most companies have issued their developers are often too underpowered for any form of local AI using Open Models. They usually have, at best, 16GB. This is woefully inadequate for the context windows required by any useful coding models. And, thanks to these new data centers, memory is only getting more expensive and increasingly in short supply. Those brand new executive offices may have to be converted back to data centers so that in-house servers and GPUs can be installed at an ever increasing cost.
And, who knows, soon I may be getting frantic calls to come back and help clean up their code now laden with AI generated tech debt.