In 13 Businesses for the Age of AI, Greg Isenberg argues that founders should own something valuable even when agents make software and content cheaper to produce. His list ranges from agent-assisted bookkeeping to sauna clubs, window-cleaning robots, energy, and security. It is a useful map of possible advantages, not a proven list of the only businesses that can survive AI.
Agentic businesses can win by delivering a specific job more efficiently or by owning a scarce input: buyer relationships, an audience, permissioned data, trusted workflows, physical capacity, or security. Neither advantage appears just because a model is available. The first test is still whether a reachable customer will pay for a result.
Watch Greg Isenberg's Full Episode
Credit: Greg Isenberg for the original 6 October 2026 episode and the 13-part framework. The descriptions below separate his forecasts and examples from the smaller tests a new founder could actually run.
1-5: Work, Distribution, Data, and Harnesses
- AI-native service firms. Agents prepare much of a repeatable deliverable, while qualified people review exceptions and remain accountable. Greg's example is bookkeeping for one type of business. Start small: sell one tightly scoped monthly service, track human review time and error rates, then automate the stable steps. The constraint is quality, privacy, and professional responsibility, not merely model speed. Watch at 01:15.
- Offline businesses. People still buy a place, a ritual, and time with others. Othership's sauna and ice-bath locations are the example. Start small: run a licensed pop-up or partner event before taking a lease; measure repeat attendance. The constraint is operating cost, safety, and local permits. Watch at 02:57.
- Distribution and organic audiences. Greg's audience-community-product sequence starts by serving a niche repeatedly, then learning what it wants to buy. Start small: publish a useful weekly briefing for a specific buyer and interview the most engaged readers. The constraint is attention and trust; the ad-click prices he recalls in the episode are illustrative, not a verified market average. Watch at 05:41.
- Proprietary data sets. Useful, current, permissioned information can be more valuable than another generic interface. Start small: compile one niche data product from sources you can legally collect and license, then ask buyers which decisions it changes. The constraint is provenance, consent, freshness, and whether the data is genuinely difficult to reproduce. Watch at 07:34.
- Domain-specific harnesses. A model becomes useful in an industry when tools, workflow rules, memory, checks, and approvals surround it. Greg points to legal AI platforms such as Harvey. Start small: support one bounded workflow for one kind of firm, with a human decision gate. The constraint is system access, domain knowledge, and auditable reliability. Watch at 09:08.
6-9: Robots, Products, Infrastructure, and Care
- Robotics and physical AI. Sell a real-world outcome rather than assuming you must invent the robot. The window-cleaning example appears to be Skyline Robotics' Ozmo, which the transcript calls "Osmo." Start small: investigate a distribution, operations, or maintenance partnership for a proven device. The constraint is equipment cost, site safety, servicing, and liability. Watch at 12:16.
- Physical products with a fan following. Greg cites Fellow's specialty-coffee gear and Minimal Company's phone as niche-led product examples. Start small: show a functioning prototype to the community, secure realistic deposits, and test returns and support costs. The constraint is manufacturing, inventory, and fulfillment; an AI-generated design is not a manufacturable product. Watch at 14:01.
- Compute and energy. Agents depend on chips, electricity, cooling, sites, and maintenance. Start small: look for a narrow service around deployment, monitoring, power efficiency, or procurement before buying infrastructure. The constraint is capital intensity, contracts, permitting, and volatile utilization. Watch at 16:34.
- Health, longevity, and care. The durable job is helping people, especially groups that a generic app underserves. Start small: test a supervised, clearly scoped service with a qualified practitioner and track adherence or satisfaction. The constraint is clinical claims, safeguarding, licensing, privacy, and staffing. AI may help with administration, not replace professional judgment. Watch at 17:31.
10-13: Networks, Real Assets, Agents, and Security
- Marketplaces and social networks. A valuable network can match a specialized supply with recurring demand. Start small: make ten matches manually in one local or professional niche before building a platform. The constraint is liquidity: both sides must show up at the right time, and trust and dispute handling do not automate away. Watch at 19:30.
- Real assets. Greg's example is small industrial workshops rented to local trades. Start small: verify occupancy demand and tenant needs before buying or leasing property. The constraint is location, financing, zoning, maintenance, and vacancy risk. An agent can help search, but cannot create a well-placed workshop. Watch at 21:04.
- Vertical agents. Give an agent responsibility for one recurring job, such as drafting restaurant purchase orders after checking stock and supplier terms. Start small: run in recommendation-only mode and compare every suggestion with a buyer's actual decision. The constraint is permission, exception handling, integration, and measurable accuracy. A vertical agent often depends on the domain harness in category five. Watch at 21:46.
- Security. More agents with access to business systems create a need for least-privilege permissions, audit trails, and human approval. Greg imagines controls for an accounting firm that may read invoices and draft entries but cannot move money. Start small: audit one workflow's permissions and demonstrate an enforceable approval boundary. The constraint is that security promises require rigorous engineering, testing, and trust. Watch at 22:18.
How to Choose Without Betting on All 13
Choose the category where you already know a buyer and can test a painful, repeated job cheaply. A service may be the fastest way to learn; an audience or permissioned data set may take longer to build but become an asset; property, robotics, and infrastructure generally require more capital and operational expertise. A domain harness or vertical agent is compelling only when the workflow has clear inputs, measurable outcomes, and access the customer will actually grant.
| Ask before building | Evidence to seek |
|---|---|
| Can I reach the buyer? | Five specific conversations, not a large hypothetical market. |
| Will someone pay for the outcome? | A paid pilot, deposit, or purchasing process with clear next steps. |
| Can I deliver it reliably and legally? | Permissions, review points, failure handling, and repeatable unit economics. |
Greg's categories are a thesis about what stays valuable as agents spread. Examples such as Othership and Ozmo show that these models exist; they do not establish that a new entrant will achieve the same economics. The smallest useful move is a buyer conversation and a bounded test, not a full product or lease.
Find a Business You Can Actually Test
This prompt turns the 13-category map into three buyer-specific options, then forces one small validation plan. It works in a general AI assistant and needs no new agent platform.
Pick one durable advantage
Compare three categories, then validate one offer in seven days.
Video Chapters
| Time | Topic |
|---|---|
| 00:00 | Intro |
| 01:15 | AI-native service firms |
| 02:57 | Offline businesses |
| 05:41 | Distribution and organic audiences |
| 07:34 | Proprietary data sets |
| 09:08 | Domain-specific harnesses |
| 12:16 | Robotics and physical AI |
| 14:01 | Physical products with a fan following |
| 16:34 | Compute and energy |
| 17:31 | Health, longevity, and care |
| 19:30 | Marketplaces and social networks |
| 21:04 | Real assets |
| 21:46 | Vertical agents |
| 22:18 | Security |
| 24:08 | Closing thoughts |
Sources and Useful Links
- Creator and framework: Greg Isenberg, 13 Businesses for the Age of AI (video and supplied transcript).
- Offline experience: Othership's current locations and experience overview.
- Window-cleaning example: Skyline Robotics and Ozmo. This is the company and robot consistent with the episode's high-rise cleaning-as-a-service description.
- Specialized products: Harvey, Fellow, and Minimal Company. These links show the examples, not independent proof of the episode's revenue or valuation claims.