How to Build Vertical AI Businesses Fast with Ryan Carson, Builder in Residence at Sourcegraph

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Oct 30, 2025

Ryan Carson, founder and entrepreneur, shares his journey from coding on an Apple IIe to becoming Builder-in-Residence at Sourcegraph's AMP. He reveals his take on why AI tools struggle financially, emphasizing the importance of token economics. They discuss the exciting rise of vertical AI businesses tailored for niche markets. Ryan outlines a practical approach to software development with AI agents, stressing the need for structured planning and self-testing. Finally, he offers advice for raising AI-native kids in this transformative tech era.

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INSIGHT

Token Costs Drive True AI Economics

"Tokens are expensive" is a core practical observation: LLM outputs cost real money per token.
Many AI products hide these costs via funding or subsidies, which will cause a future reckoning.

ADVICE

Use Ads To Make Pro AI Tools Free

Do consider ad-supported models to subsidize expensive token usage when offering AI tooling.
Ryan describes AMP's free tier funded by targeted developer ads as a workable alternative.

INSIGHT

Many AI Startups Are Subsidizing Usage

Most AI tooling businesses are currently unprofitable because token costs exceed typical subscription prices.
Deep-pocketed firms can subsidize usage now, but unit economics will shift later.

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Ryan Carson (ex-Treehouse, Intel; now Builder-in-Residence at Sourcegraph’s AMP) shares his origin story and a practical playbook for shipping software with AI agents. We cover why “tokens aren’t cheap,” how AMP made pro-level coding free via developer ads, a concrete workflow (PRD → atomic dev tasks → agent execution with self-tests), and why managers should spend time as ICs “managing AI.” We close with advice for raising AI-native kids and a perspective on this moment in tech (think integrated circuit–level shift).Timestamps

00:00 – The beginning of intelligence: how LLMs changed Ryan’s view of computing

00:23 – Apple IIe → Turbo Pascal → Computer Science: the maker bug bites

03:20 – DropSend: early SaaS, Dropbox name clash, first acquisition

04:30 – Treehouse: teaching coding without a CS degree; $20M raised, acquired in 2021

05:02 – The “bigger than a computer” moment: discovering LLMs

06:15 – Joining Intel: learning GPUs and the scale of silicon (“my adult internship”)

07:09 – Building an AI divorce assistant → joining AMP as Builder-in-Residence

09:38 – AMP vs ChatGPT/Claude/Cursor: agentic coding with contextual developer ads

11:09 – Token economics: why AI isn’t really cheap

17:27 – Frontier vs Flash models (Sonnet 4.5 vs Gemini 2.5) — how costs scale

21:31 – Private startup: vertical AI for specialized domains

22:36 – The new wave of small, vertical AI businesses

23:01 – Live demo: building a news app end-to-end with AMP

28:18 – How to plan like a pro: write the PRD before you build

30:02 – “Outsource the work, not your thinking.”

32:28 – Turning PRDs into atomic tasks (1.0, 1.1…)

35:50 – Competing in an AI world = planning well

36:28 – Managers should schedule IC time to “manage AI”

37:14 – Designing feedback loops so agents can test themselves

39:47 – “AI lied to me”: why verifiable tests matter

41:11 – Raising AI-native kids: build trust, context, and agency

43:59 – “We’re living in the integrated circuit moment of intelligence.”Tools & Technologies MentionedAMP (Sourcegraph) – Agentic coding tool/IDE copilot that plans, edits, and ships code. Now offers a high-end, ad-supported free tier; ads are contextual for developers and don’t influence code outputs.Sourcegraph (Code Search) – Parent company; enterprise code intelligence/search.ChatGPT / Claude – General-purpose LLM assistants commonly used alongside coding agents.Cursor / Windsurf – AI-first code editors that integrate LLMs for completion and refactors.Bolt / Lovable – Text-to-app builders for rapid prototyping from prompts.WhisperFlow / SuperWhisper – Voice-to-text tools for fast prompting and dictation.Anthropic Sonnet 4.5 – Frontier-grade reasoning/coding model; powerful but pricier per token.Google Gemini 2.5 Flash – Fast, lower-cost model; “good enough” for many workloads.Auth0 (example) – Authentication-as-a-service mentioned as a contextual ad use case.GPUs / TPUs – Compute for training/inference; token cost drivers behind AI pricing.PRD + Atomic Tasks Workflow – Ryan’s method: record spec → generate PRD → expand to dot-notated tasks → let the agent implement.Self-testing Scripts – Ask agents to generate runnable tests/health checks and loop until passing to reduce back-and-forth and prevent “it passed” hallucinations.Family ChatGPT Accounts – Tip for raising AI-native kids; teach sourcing, context, and trust calibration.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.