“The bargain we are being asked to ratify” – AI as technological bribery
Niche InternetRevenge of the junior developer | Sourcegraph Blog
with agents, you don’t have to do all the ugly toil of bidirectional copy/paste and associated prompting, which is the slow human-y part. Instead, the agent takes over and handles that for you, only returning to chat with you when it finishes or gets stuck or you run out of cash.
As fast and robust as they may be, you still need to break things down and shepherd coding agents carefully. If you give one a task that’s too big, like "Please fix all my JIRA tickets", it will hurl itself at the problem and get almost nowhere. They require careful supervision and thoughtful problem selection today. In short, they are ornery critters.
it’s not all doom and gloom ahead. Far from it! There will be a bunch of jobs in the software industry. Just not the kind that involve writing code by hand like some sort of barbarian.
But for the most part, junior developers – including (a) newly-minted devs, (b) devs still in school, and (c) devs who are still thinkin’ about school – are all picking this stuff up really fast. They grab the O’Reilly AI Engineering book, which all devs need to know cover to cover now, and they treat it as job training. They’re all using chat coding, they all use coding assistants, and I know a bunch of you junior developers out there are using coding agents already.
I believe the AI-refusers regrettably have a lot invested in the status quo, which they think, with grievous mistakenness, equates to job security. They all tell themselves that the AI has yet to prove that it’s better than they are at performing X, Y, or Z, and therefore, it’s not ready yet.
It’s not AI’s job to prove it’s better than you. It’s your job to get better using AI
Your "Per-Seat" Margin is My Opportunity
Traditional software is sold on a per seat subscription. More humans, more money. We are headed to a future where AI agents will replace the work humans do. But you can’t charge agents a per seat cost. So we’re headed to a world where software will be sold on a consumption model (think tasks) and then on an outcome model (think job completed) Incumbents will be forced to adapt but it’s classic innovators dilemma. How do you suddenly give up all that subscription revenue? This gives an opportunity for startups to win.
Per-seat pricing only works when your users are human. But when agents become the primary users of software, that model collapses.
Executives aren't evaluating software against software anymore. They're comparing the combined costs of software licenses plus labor against pure outcome-based solutions. Think customer support (per resolved ticket vs. per agent + seat), marketing (per campaign vs. headcount), sales (per qualified lead vs. rep). That's your pricing umbrella—the upper limit enterprises will pay before switching entirely to AI.
enterprises are used to deterministic outcomes and fixed annual costs. Usage-based pricing makes budgeting harder. But individual leaders seeing 10x efficiency gains won't wait for procurement to catch up. Savvy managers will find ways around traditional buying processes.
This feels like a generational reset of how businesses operate. Zero upfront costs, pay only for outcomes—that's not just a pricing model. That's the future of business.
The winning strategy in my books? Give the platform away for free. Let your agents read and write to existing systems through unstructured data—emails, calls, documents. Once you handle enough workflows, you become the new system of record.
Why A.I. Isn’t Going to Make Art
Why AI Will Save the World | Andreessen Horowitz
What is the testable hypothesis? What would falsify the hypothesis? How do we know when we are getting into a danger zone? These questions go mainly unanswered apart from “You can’t prove it won’t happen!” In fact, these Baptists’ position is so non-scientific and so extreme – a conspiracy theory about math and code – and is already calling for physical violence, that I will do something I would normally not do and question their motives as well.
Will A.I. Become the New McKinsey?
The Dawn of Mediocre Computing
I’ll take an inventory in a future post, but here’s one as a sample: AIs can be used to generate “deep fakes” while cryptographic techniques can be used to reliably authenticate things against such fakery. Flipping it around, crypto is a target-rich environment for scammers and hackers, and machine learning can be used to audit crypto code for vulnerabilities. I am convinced there is something deeper going on here. This reeks of real yin-yangery that extends to the roots of computing somehow.