On May 12, 2026, Jackson Oaks — founder of Recursion AI and the self-hosted AI platform Courier — joins the show. Jackson has been running open-source models in production for SMBs since early 2025, back when most of the industry still called local inference a hobbyist toy. Today his company runs production workloads on Apple Mac hardware, processes 600 to 800 thousand API calls a month per machine, and is profitable doing it.
His thesis: eighty percent of businesses have eighty percent of use cases that don't need Opus or GPT-5.5. They need a smaller open-source model, good systems, and predictable pricing. Frontier intelligence, or boring infrastructure? Per-token billing, or a flat rate on hardware you already own?
That's the cold open. Every fifth episode of Human In The Loop is a guest deep-dive — no Signal or Noise, no rotating segment, one topic, one expert. Oscar, Matt, and Jackson spend 58 minutes on what most AI bills are actually paying for, and close with three hot takes you won't hear anywhere else.
What we cover with Jackson Oaks.
Where local models win, where frontier models still matter, and how to tell which category your use case is in.
Time bought back, margin protected on AI products, conversion lift on AI features — and which one is nearly impossible to measure honestly.
Why M-series is the most underrated AI hardware story of the decade. 1/10th the cost of NVIDIA, 30 to 40x more power efficient. Apple has been positioning for this since the M1 dropped — the same week ChatGPT did.
600 to 800,000 API calls a month on a single Mac Studio. Flat-rate cloud pricing starting at $100/mo for 45 to 100K calls. The math on why predictable beats per-token for SMBs.
A Fortune client paid Deloitte $400,000 for what turned out to be a GPT wrapper with an unoptimized RAG database. MIT says 95% of business AI pilots can't measure ROI. We have theories.
The infinite hallucination loop that ate 10,000 requests in a week. Building hallucination detection from scratch. The fine-tuned 14B model that outperformed GPT-4o on a real production task because the data was good and the task was narrow.
Idempotency in non-deterministic systems. Observability. Redundancy. Why your AI feature needs the manual OCR backup you were about to delete.
Why the next wave of AI businesses isn't “AI for everything” — it's $200/mo agents for pressure washers, plumbers, landscapers, attorneys, accountants. Specialize down, charge real money, capture real value.
Two opinions, no disclaimers.
Matt“One of Anthropic or OpenAI buys the other — or merges with a foreign lab — inside three years. Or open-source overtakes both and they shrink to a fraction of their current valuations. Their burn rate is not sustainable.”
Oscar“Every home will eventually have a Mac Mini running a better Alexa, powered by open-source models — not closed-source ones. That future is closer than people think.”
Jackson“Better systems and structure with smaller open-source models beat throwing frontier models at unstructured problems. People are compensating for a lack of system with a bigger model. We're doing the opposite — we're building better systems so we can use smaller models.”