💼 AI Business
Going to market with AI. Pricing, GTM, build-vs-buy, moats, enterprise adoption, vertical agents, ROI stories.
The workflow
flowchart LR
A[Market wedge] --> B[Workflow being<br/>automated]
B --> C[Build vs buy<br/>foundation model]
C --> D[Pricing<br/>seat / usage / outcome]
D --> E[GTM<br/>PLG / sales-led]
E --> F[Moat<br/>data, distribution, depth]
The startups winning sell outcomes, not models. The moats are workflow data and distribution.
Key takeaways
Videos (86)
Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company
AI coding tools deliver individual 10x gains but enterprise impact stalls at 5–15% until Agile-era team processes are redesigned around AI throughput.
AI changes *Nothing* — Dax Raad, OpenCode
AI accelerates coding but the marketing, UX taste, and retention work that actually determines product success is unchanged and underappreciated.
Every company should have a Brain — Garry Tan, Y Combinator
Encode organizational knowledge and routing into executable workflows while keeping deterministic operations in software.
#define AI Engineer - Greg Brockman, OpenAI (ft. Jensen Huang)
The AI engineer mindset is first-principles speed plus customer obsession — challenge every arbitrary process constraint, especially now that AI compresses development cycles further.
State of Startups and AI 2025 - Sarah Guo, Conviction
AI value creation is outpacing every prior wave but the AI summer is still early — bet on reasoning, agents, voice and the 'cursor-for-X' application pattern.
Dispatch from the Future: building an AI-native Company – Dan Shipper, Every, AI & I
AI-native companies win by codifying every iteration's lessons into prompts so each feature makes the next feature easier to build.
Forward Deployed Engineering 101 — Kevin Bai, Anthropic, ex Palantir & Rippling Founding FDE
Use forward deployed engineers to translate a reusable platform into customer outcomes when technical adoption is the barrier.
Structuring a modern AI team — Denys Linkov, Wisedocs
Hire AI generalists matched to your bottleneck rather than dream-team researchers; most production value comes from integration and adoption, not frontier modeling.
Can you prove AI ROI in Software Eng? (Stanford 120k Devs Study) – Yegor Denisov-Blanch, Stanford
AI productivity gains in software are real but median (~10%) and bimodal — top adopters pull away while heavy token-spenders without practices regress.
Agents Don't Do Standups: Building the Post-Engineer Engineering Org — Mike Spitz, PFF
Redesign coordination around fast agent delivery, but judge success through customer outcomes and adopt the workflow gradually.
The New Lean Startup — Sid Bendre, Oleve
AI tooling plus disciplined operating principles let a 4-person team out-build the bloated startup of yesterday — profit and KPI focus replace headcount.
Most Enterprise Agentic Projects Are Doomed, Here's Why — Jess Grogan-Avignon & Jack Wang, Accenture
Enterprise agent adoption requires changing delivery infrastructure and funding models alongside the technology.
AI Consulting in Practice – NLW, Superintelligent, @AIDailyBrief
Enterprise AI adoption is real and accelerating, but ROI measurement is the binding constraint and traditional KPIs don't capture it.
The Signal Layer: What to Build When Anything Can Be Built — Lena Hall, Akamai
Use AI to execute a direction grounded in firsthand customer knowledge, then preserve that specificity in how you communicate it.
AI tools for Forward Deployed Engineering — Vasuman Moza, Varick Agents
Enterprise AI delivery depends on understanding and redesigning customer workflows while integrating with their existing systems of record.
Forward Deployed Engineering at Cursor — Pauline Brunet
Build an FDE function around high-impact customer transformation and fit its hiring and engagement model to the product and buyers.
How Forward Deployed Engineering is done at Cognition — Jia Wu
Effective forward deployment both solves customer workflows and turns field evidence into improvements to the core product.
The 1,000x AI Engineer: Swyx
Bet your career on AI engineering now and compound your impact by teaching and building networks — the timing and demand/supply imbalance favor this generation.
How to Leverage Domain Expertise — Chris Lovejoy, Notius Labs
Choose how domain experts shape evaluation and improvement, rather than treating their knowledge as occasional advice.
How AI is changing Software Engineering: A Conversation with Gergely Orosz, @pragmaticengineer
Tech giants are measuring engineers by AI token spend, recreating the discredited lines-of-code metric and warping behavior even as the underlying productivity gains are real.
What We Learned Deploying AI within Bloomberg's Engineering Organization – Lei Zhang, Bloomberg
At enterprise scale the win is uplift/incident agents on a paved path, not just developer copilots — and reviewer bandwidth becomes the new bottleneck.
The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra
Define customer-facing engineering roles by their actual technical work and accountability, rather than the forward deployed title.
OpenAI for VP's of AI + Advice for Building Agents
Enterprises should sequence AI adoption from workforce literacy to internal automation to product infusion, anchored in business strategy and one or two scoped pilots first.
Anthropic in the Enterprise — Alexander Bricken & Joe Bayley
Get back to the core user problem, invest in evals and architecture, and use interpretability-aware models for trust-critical enterprise workflows.
Beyond the Lethal Trifecta: Agentic Commerce on the Open Internet — David Levine, Kiduna Club
Levine proposes combining agent identity, legal standing, and composable governance to enable an open agent economy.
The Next Unicorns: 7 Top AI startups from the HF0 Residency
HF0 residency showcase: bets are converging on AI-native consumer hardware (smart speakers), creative tooling, and adaptive personalized web experiences.
Small AI Teams with Huge Impact — Vik Paruchuri, Datalab
Small teams of senior generalists with AI leverage and boring tech can ship state-of-the-art models faster than traditional org charts.
The 2025 AI Engineering Report — Barr Yaron, Amplify
AI engineering in 2025 is heterogeneous, evaluation-bottlenecked, and dominated by RAG plus a surprising amount of fine-tuning.
Hiring & Building an AI Engineering Team: Dr. Bryan Bischof
Hire AI engineers as senior product-minded SWEs comfortable in Python and TypeScript who can ship to real users, not ML researchers chasing publications.
Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa
Treat go-to-market as an engineered knowledge system that combines internal signals, web data, and agents capable of acting on both.
Real ROI: Lessons from Enterprises that have already succeeded with LLMs at Scale: Raza Habib
Enterprise LLM ROI comes from domain experts in the loop, simple 4-component apps, and rigorous up-front evaluations — not exotic architectures.
Monetizing AI — Alvaro Morales, Orb
Treat AI pricing as a continuously experimentable surface — pick the right value metric for your audience and prepare to reprice as model costs swing.
How to Hire AI Engineers when EVERYONE is cheating with AI — Beth Glenfield, DevDay
Hiring must shift from leetcode-style screens to simulations of working alongside AI teammates, since the skills that matter are collaboration, judgment, and delegation.
The AI emperor has no DAUs why most devs still don't use code AI: Quinn Slack
The code-AI revenue pyramid is wildly narrower than the hype — usage must grow 10–100x for current valuations to make sense, so adoption UX is the real product problem.
Small Bets, Big Impact Building GenBI at a Fortune 100 – Asaf Bord, Northwestern Mutual
In risk-averse enterprises, ship GenAI as many small, evaluated bets that progressively expand scope rather than a single high-stakes launch.
Paying Engineers like Salespeople – Arman Hezarkhani, Tenex
In the AI era, paying engineers per accepted story point directly aligns them with using AI tools well — sales-style variable comp for builders.
Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation
Organization-wide impact requires embedding reusable agent workflows into repositories rather than relying on individual tool adoption.
Navigating AI's Frontier in 2025 - Grace Isford, Lux Capital
Agents will only ship when teams attack compounding error directly — through data curation, eval, and step-level reliability, not just smarter base models.
Insights on Building AI Teams — Heath Black, SignalFire
Hire AI talent on body-of-work in the right geographies and watch retention/funding data — credentials and Twitter narratives are misleading.
Rethinking Team Building: how a 30-person Startup serves 50 Million Users — Grant Lee, Gamma
AI makes the lean generalist team the new default — 30-person org serving 50M users is the proof point against blitzscaling.
Building a 10 person unicorn - Max Brodeur-Urbas, Gumloop
Tiny AI-native teams win by hiring obsessively, eliminating meetings, automating internally with your own product, and recruiting customers as employees.
Which AI startups actually land enterprise contracts? — Brian Lewis, Millennium
Enterprise AI vendors win by solving a specific problem while fitting the buyer’s operational and security requirements.
From Tokenmaxxing to Trusted Throughput — Mingsheng Hong, Ironclad
Optimize AI spending for trusted delivered outcomes rather than token volume or indiscriminate cost reduction.
How to Fail at AI Strategy: Hamel Husain & Greg Ceccarelli
Invert the playbook: clear diagnosis, plain language with domain experts, customized scorers, and process-not-tool fixes are what actually make AI strategy work.
State of Data — Sean Cai, Independent / State of Data
Authentic workflow data and credible verification are central bottlenecks in extending AI into expert work.
Bolt.new: How we scaled $0-20m ARR in 60 days, with 15 people — Eric Simons, Bolt
Small high-context teams with grit can ride a vertical AI product to $20M ARR in two months — runway and focus beat headcount.
The Price of Intelligence - AI Agent Pricing in 2025
Pricing should match audience and use case: outcome-based for confidence, simple/predictable always, and acknowledge that AI usage scales over time so encourage the workloads you want.
How do you diffuse AI into the real world? — Varun Shenoy, Long Lake
AI deployment succeeds through operational change and gradually earned autonomy inside real businesses.
Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)
AI productivity gains are real but wildly variable — leaders must measure with telemetry+sampling+surveys and invest in psychological safety, not adoption mandates.
How Forward Deployed Engineering is done at Factory — Eno Reyes
Forward deployed engineering scales when customer work improves reusable product capabilities.
"The biggest challenge in your stack? Evals, Evals, Evals" - 2026 State of AI Engineering results
AI engineering is becoming a multimodel production discipline shaped by quality, agent capabilities, and cost.
Mastering AI Pricing — Mayank Pant, Stripe
AI pricing has to evolve as fast as the product — adopt hybrid/outcome-based models and treat each price as a revisable hypothesis to protect margins.
The ROI of AI: Why you need Eval Framework - Beyang Liu
Pick an explicit ROI evaluation framework matched to your business context — naive 'roles eliminated' math doesn't survive contact with software engineering reality.
Your AI Product Will Fail Unless You Can Explain It - Veronica Hylak, Hey AI
Explain an AI product by identifying the pain, making the solution immediately understandable, and showing what changes.
How Forward Deployed Engineering is done at Kepler — Vinoo Ganesh
Embed engineers in customer workflows to discover the real problem and feed concrete solutions back into product strategy.
Does GenAI "belong" to data scientists? — Phil Hetzel, Braintrust
Organize agent development around product understanding and evaluation rigor rather than automatically inheriting traditional ML ownership.
The AI Engineer's Guide to Raising VC — Dani Grant (Jam), Chelcie Taylor (Notable)
Raise VC early on conviction and vision, build warm relationships via specific casual asks, and let potential sell better than a half-finished product.
How AI Agents Let GTM Teams Scale — Justin Joyce, Cloudflare
Encode business context into reusable skills and deliver actionable insights directly into sales workflows.
Frontier Feud: Anthropic, Google DeepMind, Meta FAIR, Thinking Machines — Barr Yaron, Amplify
Lighthearted survey-based panel surfaces that practitioners pick models on cost first, are tired of AGI hype, and expect on-device + multi-bot conversations to dominate.
Agentic Enterprise - What your CEO must know about AI - Hubert Misztela
CEOs should map workflows + employee personas (not just roles) to identify where agents automate, augment, and reorganize entire enterprise workflows.
Rewiring the State — Eoin Mulgrew, No. 10 (Downing Street)
Government AI adoption needs empowered technical teams working directly with the people delivering services.
Government Agents: AI Agents Meet Tough Regulations — Mark Myshatyn, Los Alamos National Lab
Government AI agents require layered FedRAMP/DoD/NIST compliance plus continuous monitoring — vendor trust is the gating constraint, not model quality.
GTM Is You - Victoria Melnikova, Evil Martians
A founder's authentic public presence can become a practical distribution advantage for a developer tool.
Reverse-Engineering the AI Buyer — Aliisa Rosenthal, Acrew Capital
Design buying around self-service and automation, then add people where customers actually need help.
Open Source Is Dead. Long Live Open Source. — Saoud Rizwan, Cline
Open source's economic value and model choice may endure even as AI disrupts its traditional community workflows.
Notion's Token Town — Sarah Sachs, Notion
Sustainable AI products need deliberate model economics and durable product value beyond reselling tokens.
The Building Blocks of GTM Orchestration — Arman Vaziri, Ramp
Go-to-market orchestration depends on consistent customer context and reusable execution workflows before broad automation.
How Forward Deployed Engineering is done at Ramp — Leo Mehr
Effective forward deployed engineering combines disciplined problem scoping with incremental automation of the entire delivery workflow.
Why Bolt.new Won and Most DevTools AI Pivots Failed - Victoria Melnikova
DevTools wins with AI by combining a real moat (WebContainers for StackBlitz) with a reinvented workflow — not by sprinkling chatbots onto existing products.
Cooking with fire without burning down the kitchen: Dominik Kundel
A small dedicated emerging-tech unit lets enterprises pursue disruptive AI bets at speed while keeping the core product and customer trust intact.
How Forward Deployed Engineering is done at Decagon — Sunny Rekhi
Forward-deployed engineering scales when customer-specific learning becomes maintainable product capability.
The Agent Native Company — Rick Blalock, Agentuity
Agent-native companies treat AI as the engine of every department, requiring flatter org charts, conductor-style hires, and workflows that collapse without agents.
AI That Pays: Lessons from Revenue Cycle — Nathan Wan, Ensemble Health
The biggest near-term AI ROI in healthcare isn't diagnostics — it's killing the billing/denial friction that bleeds hospitals at every error-prone administrative step.
How to build an AI-Native Health Company — Dan Feng, Maven Clinic
Becoming AI native requires redesigning incentives and delivery practices alongside adopting models and tools.
Reverse Conway's law and GenAI: How agents will take over the organisation - Patrick Debois
AI agents will reorganize teams and orgs around intent and review skills, not coding, with humans focused on failure handling and judgment.
AI Copilots for Tech Architecture: The Highest-ROI Use Case You're Not Building — Boris B., Catio
Architecture decisions drive 9-figure outcomes — adding an architecture copilot with a live dependency map and ROI-scored recommendations is the highest-leverage AI investment most teams haven't made.
From Hype to Habit: How We're Building an AI-First SaaS Company—While Still Shipping the Roadmap
AI-first transformation is multi-dimensional and continuous — invest in ritualized discovery and process-as-product rather than chasing single feature wins.
Where AI is superhuman: The right jobs to automate with LLMs
AI is superhuman where volume kills humans and only rules-based legacy software competes — target SOC, customer engagement, supply chain ops.
Privacy First Enterprise AI: Building AI Agents that Never Leave Your Security Boundary
Deploy enterprise AI agents inside the existing identity/email/audit stack instead of new portals — agents are employees, IT becomes their HR.
The AI Pivot: With Chris White of Prefect & Bryan Bischof of Hex
Pivot to AI through ruthless prioritization and a small dedicated team, treating LLMs as a new data interface inside your existing product workflow.
Revenue Engineering: How to Price (and Reprice) Your AI Product — Kshitij Grover, Orb
AI pricing must be revisited continuously as costs swing — pick a value metric aligned to your audience, design the pricing page to signal the use case, and treat margin as a structure not a number.
Unlocking Africa's Potential with AI — Thabang Ledwaba
Africa's AI opportunity is real but requires homegrown, constraint-driven innovation tied to local problems rather than imported solutions.
From PM at Stripe to Building an AI startup, a recent founder's journey - Mounir Mouawad
Founding an AI startup feels like a Soulslike game — user problems are emergent, so abandon long roadmaps and iterate exploratorily.
Stop Ordering AI Takeout A Cookbook for Winning When You Build In House - Jan Siml
In-house AI wins by going deep on one revenue-tied job, using cheap models on good data, and being proactive instead of waiting for chat queries.
The Billable Hour is Dead; Long Live the Billable Hour — Kevin Madura + Mo Bhasin, Alix Partners
Professional services are being reshaped by AI compressing ingest work and enabling 100%-of-corpus analysis — but enterprise productivity needs domain partnership, not just deployment.
AI Templates: Gabriela and Aishwarya
Microsoft's Founders Hub plus open-source AI Templates lets early-stage startups stand up production-grade Azure AI apps in minutes with up to $250k in credits and dedicated expert guidance.