Replacing Human Departments With AI Swarms

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The deck

Every slide, and what I say over it. The bubbles are the talk. The slides are the excuse.

1. 

   • (wait, what are they exactly?)

   What I say here

   AI swarms. It sounds like a sci-fi film. It is really just a team of agents, each with one job.

   By the end you should be able to draw one on a napkin.

2. 

   • Which of these is real
   • &
   • Which is AI?

   What I say here

   Two videos, same bedroom, same dance. One is a real person. One is generated.

   Pick one. Most rooms split close to half and half, which is the point.

3. 

   • These businesses took this opportunity in 2026
   • (They're selling AI Actors with over $15M ARR)
   • Create winning ads with AI
   • AI Actors
   • Create winning studio quality video ads with AI Creators

   What I say here

   And people are already selling that. AI actors for ads, over $15M ARR.

   Nobody on these sites is a person. The brands buying them do not seem to mind.

4. 

   • Music passed the Turing Test in October 2025
   • Let's test it out
   • Ethan Mollick: It looks like AI music is following the same path as AI text: people are only 50/50 in identifying older Suno vs. human songs (but 60/40 when two songs are the same genre)
   • It takes much less time to create a song in Suno v5 than to listen to a song.

   What I say here

   Music went the same way. People could only tell Suno from a human about half the time.

   Scan the code and try it yourself. I got it wrong more than once.

5. 

   • Textual-based Turing Test was passed in 2024
   • It's 2026 now and these agents can use tools like humans now
   • GPT-4 judged human 54% of the time
   • GPT-4.5 with persona judged human 73% of the time
   • LLaMa-3.1-405B with persona judged human 56% overall

   What I say here

   Text went first. GPT-4.5 with a persona was picked as the human 73 percent of the time. More often than the real humans.

   That was talking. What changed since is that they can now use tools the way we do.

6. 

   • The web, as we know it, is dying.
   • Agentic internet has already taken over. Even Google and Meta are pushing for it.
   • SAAS is dying. (SAAS as a service is the next big thing)
   • LLM Models > humans at non novel tasks.
   • Agents are already buying
   • Indian consumers embrace AI agent-led shopping, says Accenture

   What I say here

   So why do we still talk about AI like it is next year's problem.

   The web is changing, SaaS is changing, and agents are already buying things. Even here, Accenture says Indian shoppers are ready for it.

7. 

   • An AI consultant, generalist and coach. Taught AI to over 100k people.
   • Founder of Koso.ai - an AI custom development company
   • Founded, bought, sold, and currently working on 20+ digital assets
   • An AI creator (100k+) and a community builder (50k+)
   • An AI enthusiast
   • A part-time Domainer
   • OKAashish, Aashishpahwa

   What I say here

   Quickly, me. I run Koso, we build custom AI systems. I teach AI and I make videos about it.

   Most of what follows I run on my own work first, so the examples are mine.

8. 

   • There's a new B2B now
   • (Bot 2 Bot marketing)
   • 1 Awareness: be understood. Visibility optimization helps LLMs know what you sell and mention you.
   • 2 Interest: be cited. GEO helps LLMs cite you when customers ask for the best products.
   • 3 Desire: be validated. Human proof matters: communities, forums, and influencers.
   • 4 Action: be transactable. Optimize web assets so bots can take action. Coming next.

   What I say here

   There is a new B2B. Bot to bot. Your buyer's agent talks to your agent.

   The funnel is still AIDA. Every stage just has a different reader now.

9. 

   • Make agents find you first.
   • (Next year, it won't be just ChatGPT, Gemini, or Claude crawling the internet)
   • Is Your Site Agent-Ready?

   What I say here

   Awareness first. An agent has to understand what you sell before it can recommend you.

   And it will not be three crawlers for long. Every company will have its own agents reading your site.

10. 

    • Focus on citations
    • GEO is like SEO but without focus on just links.
    • SEO, GEO, Essential for both

    What I say here

    Interest means getting cited. GEO overlaps with SEO a lot.

    The difference is you are not chasing a link. You are chasing a mention inside the answer.

11. 

    • Human Validation
    • ORM and social validation matter more in the AI world than before.
    • Reddit's AI search influence goes beyond training data

    What I say here

    Desire is the ironic one. The bots trust humans. Reviews, Reddit threads, creators.

    Your reputation online matters more now, not less, because the model reads all of it.

12. 

    • Zomato built the bot-to-bot AIDA
    • Known → cited → trusted → transacted
    • Awareness: city, cuisine, dish, menu, and 'near me' pages make Zomato legible to search-powered LLMs
    • Interest: best-of collections, ratings, photos, and reviews create answer-ready evidence
    • Desire: reviews, memes, creators, and forums add human proof at internet scale
    • Action: official MCP: discover → menu → cart → order → track → QR pay
    • The full agentic commerce loop.

    What I say here

    Zomato has done all four. Pages LLMs can read, reviews they can quote, memes people share.

    And the last step is an official MCP. An agent can find a restaurant, fill a cart and pay without opening the app.

13. 

    • So yes, agentic commerce is (almost) here.

    What I say here

    So yes, agentic commerce is almost here. The almost is shrinking every quarter.

14. 

    • The Autonomy Spectrum
    • From fixed rules to networked agency
    • 1 Program: human sets the rules. AI follows.
    • 2 Assist: AI advises. Human decides.
    • 3 Assemble: AI builds the plan. Human approves.
    • 4 Authorize: human sets guardrails. AI executes.
    • 5 Autonomize: AI acts. Human is notified.
    • 6 Network: agents coordinate. Humans oversee.
    • More autonomy → less intervention → stronger guardrails

    What I say here

    Here is the ladder. Most companies are at step two. AI advises, a human decides.

    Swarms live at step six. Agents coordinate with each other and humans oversee. Notice the guardrails get stronger as you climb, not weaker.

15. 

    • But that's the macro part.
    • Let's get to the individual level.

    What I say here

    That is the big picture. Now the part you can do something about on Monday. Your own work.

16. 

    • OpenAI's Five-Level AGI Scale
    • 1 Current AI, like ChatGPT, that talks with humans.
    • 2 AI that can solve basic problems like a PhD w/o tools.
    • 3 AI agents capable of taking actions on a user's behalf.
    • 4 AI that can create new innovations.
    • 5 AI that can perform the work of entire organizations of people.

    What I say here

    OpenAI's own scale. Five levels. Look at the top one. The work of entire organizations of people.

    That is the same thing as this talk's title, and it is their stated goal.

17. 

    • June, 2024: level 1
    • May 2025, O4-mini: level 2
    • We are here: level 3
    • The scary stuff starts from here (conscious models): level 4

    What I say here

    Level one in June 2024. Level two by May 2025. We are at three now, agents acting for us.

    Level four is where it gets strange. It took under two years to climb two steps.

18. 

    • What is Agentic AI?
    • Memory, Tools, Goals → Agent → Actions → Environment → Observations

    What I say here

    So what is an agent. A model with memory, tools and a goal.

    It acts on the world, looks at what happened, and goes again. That loop is the whole idea.

19. 

    • Like this financial management agent-
    • Dexter: your AI assistant for deep financial research.

    What I say here

    Like Dexter. You ask it a finance question and it goes off, pulls the data, checks itself and comes back with research.

    Same loop. Goal, tools, act, look, repeat.

20. 

    • I have a mix of Openclaw, Hermes, Claude Code, and custom agents..
    • My LinkedIn accounts – posting, replying, commenting, outreach, etc.
    • My emails – outreach, negotiations, and even lead nurturing.
    • My brand collaborations – outreach, inbound, negotiations, contracts, etc.
    • My content department – research and scripting for videos; writing, posting, and updating content on websites, etc.
    • Micro SAAS – Ideating, building, and managing microSAAS projects.
    • Social media – Carousels, Videos (Even editing)

    What I say here

    This is what my agents do today. LinkedIn, email, brand deals, content, micro SaaS, even video edits.

    It is not one tool. It is Openclaw, Hermes, Claude Code and some I built. Each one has a job.

21. 

    • But let's understand technical stuff first.

    What I say here

    Before swarms, two minutes of plumbing. I promise it is short.

22. 

    • Automation → Workflows → Agents
    • The difference is how much judgment the system can use.
    • 1 Traditional automation: follows fixed rules. Best for repetitive, predictable tasks. Example: copy form data.
    • 2 AI workflows: adds judgment inside a process. Best for classification and flexible decisions. Example: triage support tickets.
    • 3 AI agents: pursues a goal across many steps. Best for open-ended, multi-step work. Example: research and prepare a brief.
    • Preset instructions → adaptive decisions → goal-directed action

    What I say here

    Three things people mix up. Automation follows rules. A workflow puts a bit of judgment in the middle.

    An agent gets a goal and works out the steps itself. The difference is how much judgment you hand over.

23. 

    • A simple AI Agentic Workflow looks like this

    What I say here

    This is a simple one. A message comes in, an AI agent with a model, memory and a couple of tools handles it.

    Boxes and arrows. Nothing magic.

24. 

    • A complex one can replace your teams
    • This agent makes me ~$2k – 3k every month at the cost of $50.

    What I say here

    This is my inbox. Every form submission here gets read, sorted and answered by an agent.

    It makes me about $2k to $3k a month and costs $50 to run. That used to be somebody's job.

25. 

    • But honestly, it's like solving a puzzle.

    What I say here

    Honestly, building these is a puzzle. You take a task a person does and break it into pieces a machine can hold.

    The skill is not coding. It is seeing the pieces.

26. 

    • Puzzles become Systems.
    • And systems, when attached with appropriate tools, can replicate human working.
    • When I receive a reply to my newsletter.
    • Reply like Aashish
    • A long-term memory of How Aashish Talks
    • Here are all the Gmail commands
    • Make them join the WA community

    What I say here

    One puzzle, solved. Someone replies to my newsletter. The agent answers the way I would.

    It has a memory of how I talk, the Gmail tools, and one goal: get them into the WhatsApp community.

27. 

    • Like My Personal VA System

    What I say here

    Put enough of those together and you get a personal assistant. This is mine, sorting my mail as it arrives.

28. 

    • Fyxer is selling exactly this for $270 PA
    • At a valuation of $60 million.
    • Your assistant for writing replies

    What I say here

    And Fyxer sells exactly this. $270 a year, at a valuation of $60 million.

    So the puzzle you solve for yourself might be someone else's company.

29. 

    • Using Claude or ChatGPT connectors (easy but prone to fail. Plus it's just time based)
    • Technical AI Personal Agents like Openclaw & Hermes (scary black code screen)
    • Non technical AI Personal Agents like Open Human or Me.bot (I prefer them)

    What I say here

    Some of you do a version of this already. Who uses Claude or ChatGPT connectors?

    Three ways in. Connectors are easy but break. Openclaw and Hermes are powerful but it is a black code screen. I prefer the non-technical ones.

30. 

    • You can't trust a memory you can't read

    What I say here

    This is Open Human. It checks my calendar and tells me honestly when it cannot reach something.

    The rule I use: if I cannot read what the agent remembers about me, I do not trust it.

31. 

    • But they are single agents.
    • And a single agent suck at multitasking.
    • (Even with a long term memory)

    What I say here

    Here is the catch. All of these are one agent. And one agent is bad at doing many things at once.

    Give it five jobs and it gets confused, even with a good memory. Same as a person, really.

32. 

    • So we make a team, and we call it an…
    • AI Swarm
    • Many specialists. Shared signals. One adaptive goal.
    • 1 Specialize
    • 2 Coordinate
    • 3 Emerge
    • The group solves more than any one agent.

    What I say here

    So you do what companies do. You hire a team. That is a swarm.

    Each agent specializes, they share signals, and together they solve more than any one of them could.

33. 

    • Let's simplify it.
    • (The visual way though)
    • First team research for AI news from Telegram, Reddit, HackerNews etc and adds it to google sheet.
    • Agent verifies it and writes newsletters using the same
    • Agent verifies it and writes video scripts using the same

    What I say here

    Here is one I run. One agent reads Telegram, Reddit and Hacker News for AI news and puts it in a sheet.

    Two more pick it up. One checks it and writes the newsletter. The other writes video scripts.

34. 

    • One agent researches daily AI news ->
    • One agent writes the newsletters
    • One agent writes the video scripts

    What I say here

    Same thing with real screens. The research digest at the top, the newsletter and the scripts underneath.

    That used to be a content team of three.

35. 

    • Earlier I used AI Workflows for the Same

    What I say here

    Last year I built it as a workflow. Look at it. Every box is a thing that can break.

    It worked, but I was the one fixing it every week.

36. 

    • But that was 2025.
    • (human in the loop)
    • Today, you can spawn multiple agents in Claude itself.

    What I say here

    That was 2025. Today Claude can spin up several agents itself, with you approving along the way.

    No canvas, no wiring. You describe the team.

37. 

    • But what these chatbots can't do well is
    • Orchestrating.

    What I say here

    But there is one thing chat apps still do badly. Orchestrating.

    They can start a team. They are not good at running one.

38. 

    • Orchestration is when an Agent can manage other Agents
    • Without human intervention
    • CEO: Claude
    • CMO: OpenClaw
    • CTO: Cursor
    • COO: Claude
    • CodexCoder, Engineer: Codex
    • ClaudeCoder, Engineer: Claude

    What I say here

    Orchestration is one agent managing the others, without a human in between.

    An org chart where the CEO is Claude, the CTO is Cursor, and the engineers are Codex and Claude. That is a department.

39. 

    • A (really) Simplified Agentic Orchestration Demo

    What I say here

    A really simplified one. An orchestrator in the middle writes articles by handing work to sub-agents and checking what comes back.

    The orchestrator decides who does what next. Not me.

40. 

    • Now that we're talking about agents
    • How are you using agents for internal productivity?

    What I say here

    Your turn. Hands up if you already use agents inside your company. Not for customers, for your own team.

    Here are a few we have built.

41. 

    • Like Automating Back Office

    What I say here

    Back office first. It is the boring work, which makes it the best place to start.

    Counts, invoices, status. Agents are good at boring.

42. 

    • Or an orchestrator to replace a team

    What I say here

    Or an orchestrator that runs a whole team of writing agents. You give it the brief, it gives you the finished work.

43. 

    • Or inbound lead management using calls
    • Never miss a call. Ever again.

    What I say here

    Or voice. An AI receptionist that picks up every inbound call, answers questions and books the lead.

    Never miss a call again. That one is a department too.

44. 

    • Let's Talk More
    • (I run koso.ai)

    What I say here

    If you want one of these for your own company, this is where to find me. I run Koso, and this is what we build.

45. 

    • You can find me here
    • OKAashish
    • Aashishpahwa
    • @OKAASHISH

    What I say here

    And I am okaashish on most places. I post what I build, usually the week I build it.

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Questions

The AI here has read every slide and my notes, and it checks the web when a question needs it. Ask it what you would have asked me in the room.