Everyone Gets A Software Company — Benjamin Guo, Zo Computer

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Everyone Gets a Software Company

Benjamin Guo presents Zo as a personal cloud: one AI-equipped server where an individual can keep files, run automations, publish websites and APIs, and replace parts of a fragmented SaaS stack. The larger question is who owns the cloud agents—and who benefits as those agents improve.

From a talk by Benjamin Guo

At a glance

Ideas worth remembering

  • A personal cloud combines files, AI, hosted services, and local-device access around one individually controlled server rather than scattering the workflow across unrelated SaaS products.

  • The retreat workflow shows the concrete advantage of consolidation: a website inquiry triggers a text, the owner calls immediately, Zo creates a payment link, and the business records remain in the same environment.

  • Zo spans a plain-language workspace and a developer-accessible Linux server, with storage, automations, integrations, browser use, arbitrary service hosting, SSH, API, and MCP access.

  • Direct publishing removes deployment friction for nontechnical users, but the talk leaves safety, testing, rollback, and operational reliability largely unspecified.

  • The strategic question for cloud agents is where accumulated intelligence goes: toward the provider renting the agent, or toward the individual or company that owns and publishes it.

A website built while waiting to speak

Benjamin Guo opens with the product already doing the thing he wants to explain. While waiting to present, he used his Zo Computer to build and publish a site covering the session’s speakers, complete with research, profiles, blogs, and useful links. The observable result is a live page behind the QR code on his slide—not a mockup or a project waiting for a deployment pipeline. This quick build establishes the talk’s working definition of a computer: a place where an agent can research, create files, and publish the result from the same environment. 0:12

Guo contrasts that ideal with the modern experience of navigating a sea of applications, sites, and services.
Guo contrasts that ideal with the modern experience of navigating a sea of applications, sites, and services.

That environment reflects Guo’s broader aim for human-machine interaction. He uses Susan Kare’s Finder icon—the human face and machine face meeting in “happy harmony”—as a picture of what good AI tools should feel like. The contrast is today’s ordinary experience: people move among applications, websites, devices, and subscriptions, while vendors raise prices for bundled AI features users may not have requested. The machine no longer feels like one coherent place that belongs to its user. 1:44

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0:12 · section reference included

The rent chain behind fragmented software

Guo calls the underlying structure technofeudalism. The user pays a SaaS provider; the SaaS company rents infrastructure from a cloud provider; the cloud provider depends on chip suppliers. Money and control move upward through that chain, while the user’s information remains split across services at the bottom. Each service controls its own product decisions, pricing, data model, and export paths, so assembling one personal workflow requires stitching together systems with different incentives. 3:17

Supports the lock-in argument: fragmented services control data, pricing, and product decisions.
Supports the lock-in argument: fragmented services control data, pricing, and product decisions.

The ownership problem is more important here than the medieval metaphor. If notes, scheduling, bookkeeping, websites, and customer records live in separate vendor silos, the user cannot easily connect them or replace the interface around them. A product manager at each vendor optimizes that vendor’s business; no one service is responsible for making the user’s whole system coherent. Zo’s proposed answer is a “real home on the internet” where those pieces can share storage, computation, and an agent. 3:47

One early user already runs multiple websites, invoicing, accounts, bookkeeping, scheduling, and notes in this shared environment. The important change is consolidation: instead of copying information among specialized products, the same personal system can access the relevant business files and operate across them. Guo describes the resulting experience as feeling clear, calm, and “fun”—a deliberately emotional standard for infrastructure that usually asks users to tolerate administration. 4:31

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3:17 · section reference included

What a personal cloud contains

A personal cloud is an individually controlled place in the cloud containing the user’s files, AI, and hosted services. In Zo’s implementation, the core is a personal server with AI built in. Because it is a server rather than only a chat interface, it can expose websites and APIs, store files, and run software continuously. The agent and the things it creates occupy the same environment. 5:02

Concise definition of Zo as a personal server with AI built in, paired with the direct-publishing model.
Concise definition of Zo as a personal server with AI built in, paired with the direct-publishing model.

The cloud server can also connect to local devices. Guo’s own setup combines the Zo server and agent with hosted websites, APIs, another personal agent, a laptop, a Mac Mini, and phone access. “Personal cloud” therefore names the whole topology, not one device: the cloud machine supplies an always-available home, while local machines remain interfaces and execution endpoints around it. 5:29

The development model intentionally resembles early web publishing, when changing a site could mean transferring files directly to the server. The agent hides deployment as a separate concern: a regular user asks for a change, and the same system edits the files and serves the result. That simplicity carries a real tradeoff. Direct production changes shorten the path from intent to output, but the talk does not describe staging, rollback, testing, or safeguards for consequential updates. 6:04

The intended alternative is a single environment for personal data, AI, and hosted software rather than constant navigation among separate assistants, devices, and SaaS products. This does not mean no external services are ever involved; later integrations and model APIs remain part of the workspace. The consolidation happens at the user’s operating layer: one server becomes the place where files, tools, and agent actions meet. 6:36

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5:02 · section reference included

From retreat interest to payment

A second early user shows what changes when the personal server becomes a small business’s software company. She replaced services including Squarespace and Calendly with Zo, hosts retreat and personal websites on a custom domain, and keeps the underlying business information together. Her workspace also reflects her own visual interests rather than conforming to a generic SaaS dashboard. 7:03

Guo explains the concrete sales loop: lead notification, immediate human follow-up, and an agent-generated payment link.
Guo explains the concrete sales loop: lead notification, immediate human follow-up, and an agent-generated payment link.

The clearest mechanism is the sales loop. A prospective customer expresses interest in a retreat; Zo texts the business owner the person’s number; she calls while intent is still fresh; she asks Zo for a payment link; and she closes the booking. The website is only the visible entry point. Behind it, the server coordinates the lead notification, human conversation, payment step, and retreat records. Guo says the retreat-business user is “on track to make $100,000 on Zo”—a revenue projection, not achieved revenue. He also reports that the sales loop produces more booking revenue and enough demand to make retreats difficult to book, but the supplied recording provides no baseline, attribution analysis, or controlled comparison. 7:09

What must happen between initial interest and a completed booking? The flow below makes the division of labor visible: Zo moves information and prepares the transaction, while the owner handles the high-value conversation.

How it fits togetherA personal server turns retreat interest into an immediate sales workflow

A prospective customer signals interest through the hosted retreat site.

The agent shortens the distance between customer intent and human follow-up, then records the resulting business activity in the same environment.

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7:03 · section reference included

One interface for chat, files, automation, and hosting

The retreat system contains a database even though its owner does not need to understand databases. It also keeps notes and accounting, and it can move retreat images onto the website when asked. This is the core abstraction: the user expresses an operational change in ordinary language, while the agent edits the files or data and serves the result from the user’s server. 8:33

Concrete personal-software example: Guo’s customized scheduling replacement and applicant-review rules.
Concrete personal-software example: Guo’s customized scheduling replacement and applicant-review rules.

Zo tries to span two audiences. Nontechnical users can ask it to build personal software, while developers receive a configured Linux virtual machine with root access, networking, SSH, API control, and MCP access. It can host personal agents and arbitrary services. That power is also a responsibility boundary: root access and arbitrary HTTP or TCP services expand what users can build, but they also expand the consequences of insecure software or over-permissioned agent actions, safeguards the talk does not detail. 9:32

The workspace demo groups several parallel capabilities around the same server:

  • Models and agentic file work. Users can bring an API key, chat with different models, and let an agent work against files stored on the server.
  • Storage and automation. The file system doubles as cloud storage, while scheduled tasks can run AI work automatically.
  • Integrations and browser use. Built-in integrations, skills, and a logged-in browser let the agent operate external sites; Guo jokes that texting Zo to buy things makes impulse purchasing dangerously easy.
  • Hosting. The server can expose arbitrary HTTP or TCP services and includes a personal website editable through chat.
  • Personal software. Guo’s scheduling replacement applies his preferred rules, including reviewing people before allowing them to book time directly.

These features matter together. A chatbot alone can suggest a scheduling policy; a server-backed agent can store the policy, present a booking interface, evaluate incoming requests, and host the resulting service. The value comes from closing the loop between conversation and persistent software. 10:33

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8:33 · section reference included

Whose cloud does the agent improve?

The closing argument widens from personal software to the structure of the internet. Companies already have domains, servers, APIs, and durable organizational presence. Guo wants individuals to have comparable homes from which they can interact directly with other people, companies, and company AIs rather than always passing through intermediary applications. 12:02

Supports the directional critique that user-supplied context and improvement can accumulate toward the provider.
Supports the directional critique that user-supplied context and improvement can accumulate toward the provider.

Agents make the ownership question urgent because many of them will run in the cloud. The user may experience a conversational assistant, but that assistant acts through conventional websites, APIs, files, and services, and its state must live somewhere. Some agents can remain local; many always-on agents will need remote computation and availability. “Whose cloud is it?” therefore determines who controls the agent’s context, operating environment, and accumulated improvements. 12:20

Guo points to a company-level cloud agent as an ergonomic pattern: many employees can interact with an assistant carrying company context. His objection is directional. If the organization spends time supplying context and refining behavior inside a provider-owned system, he argues that the provider captures the compounding improvement. He calls this intelligence feudalism: the organization uses the intelligence, but the intelligence “bubbles up” somewhere else. 12:52

Which direction does improvement travel? This comparison exposes the difference between renting an agent and publishing one on infrastructure the individual or company controls.

Compare the ideasTwo directions for accumulated intelligence

Supplies context, workflows, feedback, and end usage.

The distinction is not merely where inference runs. It is whether usage improves a provider-controlled service or an agent owned by the individual or organization publishing it.

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11:32 · section reference included

Publishing agents as owned software

Zo’s proposed future lets an individual or company publish an agent, serve end users, and have those interactions improve the agent they own. This extends the personal-server idea into a software-company model: the owner controls the environment, the service, and the learning loop rather than only subscribing to somebody else’s application. Guo says Zo has a beta version of this agent-publishing platform. 14:05

Final alternative: individuals and companies publish and own agents whose end usage improves their own system.
Final alternative: individuals and companies publish and own agents whose end usage improves their own system.

That ending sharpens the talk’s title. “Everyone gets a software company” does not just mean everyone can generate a website. It means an individual can own a persistent server, combine data and AI there, turn repeated work into hosted software, and eventually publish agents whose improvement flows back toward their owner. The unresolved work lies in making that ownership operationally real: safe permissions, reliable deployment, backups, portability, evaluation, and clear control over what an agent learns remain necessary even when the interface becomes plain language. 14:05

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13:35 · section reference included

Resources

  • A longer Benjamin Guo presentation explaining his AI-assisted planning, implementation, review, and codebase-feedback workflow. It is useful for readers who want the development process behind the rapid software creation demonstrated here.

Read the complete timestamped transcript
  1. 0:01

    [music]

  2. 0:12

    >> Hello everybody. I'm Ben from Zoho

  3. 0:14

    Computer. I just posted on X from my

  4. 0:16

    Zoho.

  5. 0:18

    You know, I'll be giving away $100 in AI

  6. 0:19

    credits and I do all sorts of stuff with

  7. 0:21

    my Zoho.

  8. 0:23

    This is my computer. My Zoho is my

  9. 0:25

    computer. And before I get started, I

  10. 0:27

    made the site just now while I was

  11. 0:29

    waiting to get started presenting. This

  12. 0:31

    is a kind of overview of all the

  13. 0:33

    speakers in today's claw session. You

  14. 0:35

    can scan this QR code and check it out.

  15. 0:37

    I've got some useful links in here, but

  16. 0:39

    also I've got like some deep research on

  17. 0:41

    everybody presenting including their

  18. 0:42

    recent thoughts, links to their profiles

  19. 0:45

    and

  20. 0:46

    websites, blogs, etc. So, scan this.

  21. 0:49

    It's super useful. I just made it just

  22. 0:51

    now. This is my computer that you see

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    right here, my Zoho computer. I'll be

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    talking about it and this talk is about

  25. 0:56

    kind of Zoho and a lot of things. It's

  26. 0:59

    also about kind of personal agents,

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    personal cloud agents in particular,

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    which Zoho is one instance of and kind

  29. 1:05

    of the future and where I see all of

  30. 1:07

    this going.

  31. 1:09

    So,

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    a little bit about me.

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    I'm the co-founder of Zoho. My name is

  34. 1:15

    Ben. I started my career on the early

  35. 1:17

    Venmo team back in 2013 and um

  36. 1:20

    I met my co-founder Rob at Venmo and I

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    left Venmo to work at Stripe quite

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    early. I was one of the first I was the

  39. 1:26

    80th engineer and my co-founder Rob, he

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    went on to build Substack. He was the

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    first engineer there. And a final fact

  42. 1:33

    about me as you can see is that I really

  43. 1:35

    love computers. I'm dressed up as a

  44. 1:37

    computer.

  45. 1:39

    And I like to start with this like fun

  46. 1:41

    fact about this finder icon. So, this

  47. 1:44

    icon was designed by Susan Kare, an

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    early graphic designer at Apple Computer

  49. 1:48

    and many people don't know the story

  50. 1:50

    behind this icon, but it actually

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    represents the human, this white face,

  52. 1:55

    and the machine, this blue face, and

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    they're kind of like in happy harmony.

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    And I think that is what we should

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    aspire to as we build AI tools for

  56. 2:04

    people. Like humans love tools, humans

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    have always loved machines, and like

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    when we feel at one with our machine, we

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    feel like this. We feel happy.

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    So, who like me misses the way that

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    computers used to feel? Raise your hand,

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    right? Like the early internet, like

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    your first website, your first PC.

  64. 2:22

    Right? It used to feel different, right?

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    Today,

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    the internet and our computers feels

  67. 2:29

    more like this. We are swimming in the

  68. 2:32

    sea of applications and sites and

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    services and

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    it's not great.

  71. 2:38

    And on top of all of that,

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    they're jacking up the prices on these

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    things that we use, often because of AI

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    features that we didn't ask for.

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    And

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    I think quite reasonably, consumers or

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    I'll just call them regular people are

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    upset or confused or scared of AI.

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    So, Computer is based in Brooklyn, we're

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    in Bushwick actually, kind of like the

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    heart of hipsters in New York, and you

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    know,

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    there's a lot of this out there. I see a

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    lot of this kind of vandalism over AI

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    ads in the city.

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    On top of all of that, this is what our

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    industry looks like.

  88. 3:17

    But the deeper root of why

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    this is bad is is a structural thing.

  90. 3:23

    It's what we call techno-feudalism.

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    So, basically, feudalism was this like

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    kind of shitty system back in the day,

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    and though it no longer exists, it still

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    is alive and well in our digital lives.

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    So, we are still the peasants, we pay

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    rent to the SaaS providers who pay rent

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    to the cloud providers who pay rent to

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    the kings who are, you know, like

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    probably Nvidia these days.

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    And because we are peasants, our lives

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    are shitty. We are fragmented between

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    all these different tools. There are

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    various services that we use that lock

  104. 3:55

    us in and and shitify over time. Like

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    that PM at that product that you use is

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    not incentivized to make your life

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    better. They're incentivized to improve

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    their bottom line, to monetize your

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    attention, to take your data, lock it

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    in, and sell it back to you. And because

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    you don't own anything, you are a

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    peasant.

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    So Zo, our mission has always been to

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    solve this by giving people a real home

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    on the internet.

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    And we believe that most people in the

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    world are kind of living in the Matrix,

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    and we're trying to show people

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    a better way.

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    So Zo is alive and well. We've been out

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    for about a year, and real people are

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    using it. Real regular people like

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    Charlotte, this LA-based private chef

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    and life coach. She hosts her websites,

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    she has multiple websites now, on Zo. Zo

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    manages her invoices and her accounts

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    and bookkeeping and her scheduling and

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    her notes. And it's just like amazing

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    system. It makes her feel like clear and

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    calm and on top of the world, and it's

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    fun. As you can see this text

  132. 4:59

    that she sent me.

  133. 5:02

    So what is Zo? Zo is a personal cloud.

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    We've never really had a personal cloud

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    before, but it's quite simple. You have

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    your own home in the cloud. You're not

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    using other cloud services. You have

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    your own thing. You put your stuff in

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    it. You put your AI in it. And because

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    it is

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    a place in the cloud, you can host stuff

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    in it. You can host services like

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    websites or APIs, and they're all yours.

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    And I think for people in this room, if

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    you're like interested in clouds and

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    personal agents, like your personal

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    cloud might look something more like

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    this. You might have like a suite of

  149. 5:36

    local devices. Um and for me, I have

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    like this like Zo computer, this like

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    cloud computer as well, which has my Zo

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    agent on it. It also has a Hermes on it,

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    and it has my services. I have many

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    websites and APIs and things that I've

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    built, all hosted in the same place, and

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    it can also talk to my laptop or my Mac

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    mini, or I can use it on my phone.

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    And that, collectively, is my personal

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    cloud.

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    So, Zoe is a personal server with AI

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    built in. It's It's quite simple.

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    And it hearkens back to a simpler time.

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    So, back in the day, this is actually

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    how we built and hosted websites. We

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    just FTP'd our files onto a server, and

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    that updated our website. You were just

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    shipping to prod. You're like always

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    doing it live. And that's actually, I

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    think, how most people should build. I

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    don't think it's necessary for a regular

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    person like Charlotte to think about

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    deployment.

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    Unfortunately, for many of us in this

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    room, this is probably what your life

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    looks like, actually. You're kind of

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    just navigating between these different

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    AIs and devices and SaaS services, and

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    it's very confusing.

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    Zoe is meant to be a simpler way. It's

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    one place for all of your stuff and your

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    AI and the stuff you host, all of your

  182. 6:59

    personal stuff in the cloud.

  183. 7:03

    Here's another case study that I'd like

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    to talk about. This is Anthia. She was

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    one of our early users. She's totally

  186. 7:08

    non-technical. She's a free-diving

  187. 7:09

    instructor, and she's already on track

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    to make $100,000 on Zoe. She used to use

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    all of these different SaaS services to

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    run her business and her life, like

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    Squarespace, like Calendly, etc. And now

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    she's replaced all of that with Zoe.

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    She's canceled all of those SaaS

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    subscriptions. She is no longer a

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    peasant.

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    Anthia went from this,

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    kind of being very confused and having

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    her data in all of these different SaaS

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    silos, and not really being able to

  200. 7:39

    connect them very easily,

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    to this. So, Anthea not only hosts her

  202. 7:45

    retreat website on Zo on a custom

  203. 7:47

    domain. She has many different websites

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    now. She has her personal workspace that

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    looks like this. She's really into

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    fairies and like drawing and mushrooms

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    and she's like expressing her whole self

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    and her creativity online in this new

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    way that we kind of really haven't had

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    as regular people for a long time since

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    like the '90s.

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    And under the hood, like all of those

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    sites are just the tip of the iceberg.

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    So, when Anthea gets somebody who's

  215. 8:12

    interested in a retreat, Zo texts her

  216. 8:15

    with their number.

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    Anthea now immediately calls them at

  218. 8:19

    that moment of intent to buy. She texts

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    her Zo, "Give me a payment link." And

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    she closes the deal right then and

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    there. And that is how she is actually

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    booking much more revenue than before

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    with these retreats. Like her retreats

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    are now like actually kind of hard to

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    book because there's too much demand.

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    And that's amazing. And under the hood,

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    she has a database for all of her

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    retreats. She doesn't really know what a

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    database is, but she has one. She has

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    all of her notes, all of her accounting,

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    and it's all in one place. If she has

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    like some images from a retreat and she

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    wants to put those on her website, she

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    just tells her Zo to do it and it's like

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    right there. She can like host files

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    directly from her personal server. And

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    she is self-hosting everything.

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    So, Zo is made to be simple enough for

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    anyone, for people like Anthea, for my

  240. 9:01

    parents, for other people's parents.

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    People are always telling me on like

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    LinkedIn or X that like they sign up

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    their parent for their parents to Zo and

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    their parents are like five coding up a

  245. 9:10

    storm. It was awesome to see. I just got

  246. 9:12

    this message on LinkedIn the other day.

  247. 9:14

    Our caterer uses Zo um to kind of manage

  248. 9:16

    his staff at his restaurant. Our

  249. 9:18

    recruiter uses Zo as well. He's really

  250. 9:21

    into it and he's like kind of building a

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    whole He's like rebuilding his his stack

  252. 9:24

    for his recruiting agency on Zo. Our

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    marketing agency has become Zo-pilled.

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    So, it's made for everybody.

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    But it's also powerful enough for the

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    audience in this room.

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    It can be the home for your Open Claw or

  258. 9:36

    your Hermes. You can have a really

  259. 9:38

    nicely set up server to host your

  260. 9:41

    personal agent in if you use one of

  261. 9:42

    those tools.

  262. 9:43

    You can bring your codex or Gemini or

  263. 9:46

    etc. subscriptions and you can build and

  264. 9:48

    host anything. You have like root access

  265. 9:50

    to your own server to this very nicely

  266. 9:52

    set up Linux VM where like the

  267. 9:54

    networking is also like very well set up

  268. 9:56

    and you don't really have to worry about

  269. 9:57

    any of it. You can SSH in and you can

  270. 9:59

    control your Zo via API or MCP. So, very

  271. 10:03

    flexible.

  272. 10:05

    So, scan this QR code if you haven't yet

  273. 10:07

    uh to get $100 in AI credits. Um

  274. 10:11

    just try Zo. I I think you'll like it.

  275. 10:13

    And I'm going to do a little demo and

  276. 10:15

    then I'll have a little bit more to talk

  277. 10:17

    about after that. Um

  278. 10:19

    >> [snorts]

  279. 10:20

    >> So, really quick demo of Zo. You already

  280. 10:22

    saw it in the beginning, but Zo is this

  281. 10:24

    like nice kind of cloud workspace. I

  282. 10:26

    made this website just now. I make all

  283. 10:27

    sorts of websites on the fly.

  284. 10:29

    Um

  285. 10:30

    you can chat with really any model as I

  286. 10:32

    mentioned. You can bring your own API

  287. 10:33

    key. You can even talk to Claude code in

  288. 10:35

    here.

  289. 10:36

    >> [snorts]

  290. 10:36

    >> You've got this nice file system,

  291. 10:38

    built-in cloud storage because it's your

  292. 10:39

    server. You can just like use it as a

  293. 10:40

    Dropbox.

  294. 10:41

    Um and you know, you can obviously like

  295. 10:43

    work agentically in the chat with any of

  296. 10:45

    your files.

  297. 10:47

    You've got automations. So, you can kind

  298. 10:49

    of run tasks on a schedule with AI.

  299. 10:51

    >> [snorts]

  300. 10:52

    >> You've got tons of built-in

  301. 10:54

    integrations. You don't have to set any

  302. 10:55

    of this up.

  303. 10:56

    We have [snorts] lots of skills, a large

  304. 10:58

    skill library. There's a built-in

  305. 10:59

    browser, so you can log in to sites and

  306. 11:02

    let your Zo like buy things for you or

  307. 11:05

    do whatever. Actually, I always text my

  308. 11:06

    Zo these days to buy stuff on Amazon.

  309. 11:08

    It's like really dangerous for impulse

  310. 11:09

    purchasing. I like see something I'm

  311. 11:10

    like, let's buy it.

  312. 11:12

    >> [snorts]

  313. 11:12

    >> Um and as I mentioned, you can host

  314. 11:14

    stuff. So, you can really host anything,

  315. 11:16

    any arbitrary service, HTTP or TCP. And

  316. 11:20

    we also give you a really nice built-in

  317. 11:21

    personal website. This is my Zo space

  318. 11:24

    and this is all set up nicely and it's

  319. 11:26

    got like, you know, nice kind of like

  320. 11:28

    live coding features. Um and you just

  321. 11:29

    chat with it to edit it. And I've built

  322. 11:31

    all sorts of cool things inside of here.

  323. 11:34

    Um this for example is like my Calendly

  324. 11:37

    replacement. People can book time with

  325. 11:38

    me and it has just like this custom

  326. 11:40

    setup that I like. Um and my Zo can like

  327. 11:42

    review the people that are interested. I

  328. 11:43

    don't want anybody like booking time

  329. 11:45

    with me directly.

  330. 11:46

    Um

  331. 11:47

    and so that's kind of like what Zo is

  332. 11:49

    and I want to conclude by just talking a

  333. 11:51

    little bit about the future. So

  334. 11:54

    the reason we're building Zo is to build

  335. 11:56

    a future and better internet, one that

  336. 11:59

    looks more like this where

  337. 12:01

    everybody, [snorts]

  338. 12:02

    every individual has a real home, a

  339. 12:04

    presence on the internet. And [snorts]

  340. 12:06

    companies already have this, but like we

  341. 12:08

    should interact directly with other

  342. 12:09

    people on the internet without

  343. 12:11

    middlemen. And we should also interact

  344. 12:13

    with like the various companies and

  345. 12:14

    their AIs. Um and it should be like more

  346. 12:17

    direct.

  347. 12:20

    Another way to look at this is that like

  348. 12:22

    in the future we will probably just be

  349. 12:24

    interacting mostly with agents and the

  350. 12:27

    agents will kind of interact with the

  351. 12:28

    cloud, like these like software 1.0

  352. 12:31

    things like websites and APIs.

  353. 12:35

    But [snorts] if you dig a little deeper

  354. 12:36

    into here, what's probably actually

  355. 12:38

    going to happen is that most of these

  356. 12:40

    agents that we interact with will also

  357. 12:41

    be in the cloud. Some of them will be

  358. 12:43

    local, but many of them will be in the

  359. 12:44

    cloud. And when those agents are in the

  360. 12:46

    cloud, the question again is like whose

  361. 12:49

    cloud is it?

  362. 12:51

    >> [snorts]

  363. 12:52

    >> So

  364. 12:53

    Claud tag

  365. 12:54

    is newly out and and Claud tag is

  366. 12:56

    actually, if you think about it, a cloud

  367. 12:59

    agent. It's this like cloud Claud. It's

  368. 13:02

    like a company level Claud that has like

  369. 13:04

    all the company's context and [snorts]

  370. 13:06

    anybody can interact with it. And you

  371. 13:08

    know, it's it's an interesting and very

  372. 13:09

    like ergonomic model. Town and Victor

  373. 13:11

    are also kind of similar instances of

  374. 13:13

    this.

  375. 13:14

    >> [snorts]

  376. 13:15

    >> But

  377. 13:16

    the problem with Claud tag is this.

  378. 13:19

    Whose cloud is it? Whose Claud is it?

  379. 13:21

    It's not really your company's Claud.

  380. 13:23

    And as you kind of improve your Claud

  381. 13:26

    tag setup, like what really improves is

  382. 13:29

    Anthropic, right? Like the the arrow is

  383. 13:32

    pointing the wrong way.

  384. 13:35

    And [snorts] this is intelligence

  385. 13:37

    feudalism.

  386. 13:38

    The intelligence now is bubbling up the

  387. 13:41

    wrong way.

  388. 13:42

    And you are still a peasant again, or

  389. 13:44

    your company is still kind of

  390. 13:47

    a peasant in a way.

  391. 13:49

    >> [snorts]

  392. 13:50

    >> And at Zoe believe

  393. 13:52

    really strongly in changing the way that

  394. 13:55

    things work and giving people and

  395. 13:57

    companies more [snorts] ownership over

  396. 14:00

    their cloud and over their intelligence.

  397. 14:02

    So, we believe that the future should

  398. 14:04

    actually look more [snorts] like this.

  399. 14:07

    Anybody, a company or an individual

  400. 14:10

    should be able to publish agents and own

  401. 14:12

    them and have end users and like have

  402. 14:14

    the end usage of their agent improve,

  403. 14:17

    like self-improve their own kind of

  404. 14:19

    published agent. And that is the future

  405. 14:21

    that we're building. We have [snorts] a

  406. 14:23

    kind of beta version of this. So,

  407. 14:25

    um I'm going to go back to to this Zoe

  408. 14:27

    space uh and scan this QR code finally

  409. 14:30

    if you want to talk to me, get AI

  410. 14:33

    credits, sign up for the beta of this

  411. 14:35

    kind of new agent publishing platform.

  412. 14:39

    >> [snorts]

  413. 14:39

    >> And uh yeah, also you can check out all

  414. 14:41

    of the speakers that are presenting on

  415. 14:43

    this Zoe space page. Um I'm Ben from Zoe

  416. 14:46

    computer. Thank you.

  417. 14:48

    >> [applause]