A New Model Every Week, but You Still Have a Job
When inference cost drops below the critical point, SaaS pricing logic gets rewritten.
The Pace Problem
Every Monday brings a new foundation model. Claude gets smarter. GPT gets faster. Gemini gets multimodal-er. Open-source alternatives proliferate like bacteria in a petri dish. The benchmarks keep climbing. The demos keep stunning.
And yet — you still have a job. Why?
This isn't complacency. It's economics. The gap between "a model can do X" and "a model reliably does X in production at scale within your org's constraints" is enormous. That gap is where employment lives.
The Inference Cost Cliff
Let's talk numbers. In 2024, GPT-4 class inference cost roughly $30 per million output tokens. By early 2026, equivalent capability runs at under $1. That's a 97% cost reduction in 18 months.
This isn't Moore's Law. It's faster. And it has a specific economic consequence: when inference becomes effectively free, the bottleneck shifts from capability to integration.
Think about it. Electricity didn't eliminate jobs the year it became cheap. It took decades for factories to redesign around electric motors instead of steam-driven belt systems. The technology was ready. The organizations weren't.
What Actually Gets Automated
The pattern is consistent across every industry we track:
Automated quickly: Structured data transformation, template-based content generation, first-pass code review, basic customer support triage, document summarization, translation of standard text.
Automated slowly: Complex multi-stakeholder negotiations, novel system architecture, crisis management, creative direction, cross-functional strategy, anything requiring deep institutional context.
Not automated (yet): Physical presence requirements, high-trust relationship management, regulatory compliance judgment, aesthetic taste-making, organizational politics navigation.
The common thread? Tasks requiring context that doesn't exist in training data resist automation. Your company's unwritten rules, your team's communication patterns, your industry's regulatory gray zones — these are invisible to models trained on public internet text.
The SaaS Repricing
Here's the sleeper disruption: SaaS pricing. Most B2B software charges for access to features. But when an AI agent can replicate those features on-demand using APIs and raw compute, the value of bundled software collapses.
We're already seeing it: startups that replace $50K/year enterprise software with $500/month AI agent workflows. The agent calls the same APIs, uses the same data formats, and produces the same outputs — but without the vendor lock-in, the seat-based pricing, or the 18-month implementation cycle.
This repricing won't kill SaaS overnight. But it will compress margins, accelerate commoditization, and force every software company to answer: "What do we provide that an LLM with API access can't?"
The Real Moat
For individuals: your moat is judgment, institutional knowledge, and the ability to frame problems that models can't see.
For companies: your moat is proprietary data, regulatory trust, and integration depth that makes switching costly.
For everyone: stop watching benchmark leaderboards. Start watching deployment timelines. The model that wins isn't the smartest — it's the one that ships inside the workflow your org already uses.