They read your inbox.
They summarize reports.
They draft a few emails.

Nice productivity boost.

But saving someone eight minutes reading email is not a revolution.

The real shift is AI doing full jobs.

In this episode we break down what that actually looks like in the wild.

One large healthcare company deployed a single autonomous Cloud Employee to handle Tier 1 through Tier 3 support calls across their product lines.

Today it:

Handles 2,600+ support calls per day
Covers 65 different support skills
Operates in 15 languages
Logs 124 hours of talk time per day

The average human support call used to take 18 minutes.

The AI resolves them in about 3 minutes and 35 seconds.

When you convert that back to human time, this system replaces more than 600 hours of labor every day.

That is the difference most people are missing.

One version of AI helps you work slightly faster.

The other replaces the work entirely.

Most of the market is still building AI assistants.

We are focused on AI workers.

Learn more about Cloud Employees at Atonom
https://atonom.ai

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Most people interpreted that as AI simply helping humans research software faster. But the bigger implication is something very different.

What if the buyer itself isn’t human?

For the past two decades SaaS companies built everything around human limitations:

Dashboards to interpret data

Menus and interfaces to navigate complexity

Demos and sales calls to guide decisions

Marketing funnels and nurture campaigns to influence buyers

The entire SaaS ecosystem evolved around a biological decision maker.

But in an agentic world, that changes.

Instead of a human researching vendors, an AI agent could evaluate products, run tests, compare results, and choose the best option automatically.

That means:

No demos

No product tours

No nurture campaigns

No traditional buying journey

Just machine-to-machine evaluation.

If that shift happens, the companies that survive will design products and infrastructure that autonomous agents can consume directly.

Everyone else may spend the next few years improving interfaces that humans never use.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

You approach someone at an event, extend an arm… and suddenly you’re in the dreaded half-hug. Arms everywhere. Nobody knows what’s happening.

For years Gabe was firmly in the “hug guy” category. Then the pandemic introduced the safest diplomatic greeting humanity has ever created, the fist bump.

Now we’re living in a strange transition period.

Some people hug.
Some people handshake.
Some hover awkwardly waiting for the other person to decide.

It’s basically a real-time social negotiation.

In this episode Gabe shares his current strategy for avoiding the awkward half-hug and asks the real question:

Where do you land in the great workplace greeting debate?

Handshake
Hug
Fist bump
Or the dangerous “wait and see” approach.

Connect with Gabe Larsen
LinkedIn: https://www.linkedin.com/in/gabelarsen

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

The common narrative says AI will replace low-skill jobs first. Factory workers. Retail workers. Service workers.

But recent research from Anthropic shows something very different.

The jobs with the highest exposure to AI are overwhelmingly white collar, including:

Analysts

Marketers

Recruiters

Finance teams

Consultants

In other words, anyone whose job revolves around:

thinking

writing

summarizing

analyzing

These are exactly the types of cognitive tasks large language models are already extremely good at.

And before someone says the usual line… “AI will just augment us.”

Sure. For the top operators.

But historically, companies don’t adopt technology so everyone can keep their job. They adopt it so fewer people can produce dramatically more output.

Which raises a bigger question:
What happens when one strong operator with AI can do the work that used to require an entire team?

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Switching systems meant risk, downtime, broken integrations, lost data, and internal political battles. Most companies stayed not because the tools were perfect, but because leaving felt impossible.

That dynamic is starting to change.

We recently removed Salesforce and built our own CRM in Lovable. It wasn’t ideological and it may not be permanent, but it highlighted something important.

Most companies already have the people needed to build and maintain systems:

Salesforce admins

RevOps teams

Integration consultants

You’re already paying someone to manage and maintain the system.

The only question is what they’re building.

Are they maintaining someone else’s platform… or building leverage inside your own system?

At Atonom, we’re building Cloud Employees, AI workers that run real workflows across tools and systems. As AI begins to execute more of the work inside organizations, the economics of software start to shift.

Per-seat pricing makes less sense.
Interfaces matter less.
Control of systems and data matters more.

This isn’t the death of SaaS.

But it may be the end of passive SaaS, where companies outsource too much of their strategic thinking to vendors.

If AI is going to run more of the workflows in your business, you may not want to rent the operating system those workflows depend on.

Learn more about Cloud Employees at Atonom:
https://atonom.ai

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

But something interesting is happening.

OpenAI, the company building some of the most advanced AI models in the world, is hiring a Head of Sales Development.

If AI was going to wipe out SDRs entirely, you would expect the company building the technology to lead the way. Instead, they’re scaling the function.

The real shift isn’t the role. It’s the work inside the role.

AI will absolutely replace a lot of the repetitive motion SDRs handle today, including:

List building

Account research

First-touch outreach

Follow-ups

Scheduling

That’s the repetitive execution layer.

But the function of building pipeline doesn’t disappear. It evolves.

Someone still needs to:

Design the outbound strategy

Orchestrate humans and AI

Decide where automation works and where it breaks

Own pipeline creation

The companies that win won’t be the ones hiring huge SDR teams.

They’ll be the ones building pipeline machines, where AI runs the motion and humans run the strategy.

That’s exactly what we’re digging into at the AI SDR Summit on April 9.

No paid tickets.
No paid sponsors.
No paid speakers.

Just operators sharing what’s actually working.

https://atonom.ai/events/aisdr-summit?utm_source=gabe&utm_campaign=aisdr

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Most sales processes still look something like this:

Discovery

Demo

More demos

Contract

Close

But that’s not how buying actually works.

After the demo ends, the real process begins. Your buyer now has to go sell your product internally.

They need to convince finance, leadership, operations, and often multiple stakeholders that the investment is worth it.

And this is where most SaaS sellers fail.

They think they’ve “enabled the buyer” because they:

Sent over the same generic slide deck they use for every prospect

Forwarded a call recording that no one internally will ever watch

Said “let me know if you need anything” and disappeared

That’s not buyer enablement. That’s laziness.

Real buyer enablement helps your champion win internally.

Every internal purchase requires three things:

Current State
Help the buyer clearly explain what is broken today and why staying the same is risky.

Future State
Help them paint a compelling picture of how things improve with your solution.

ROI
Help them justify the investment with a clear return, even if the math requires some assumptions.

The buyer isn’t just deciding whether they like your product.
They’re trying to build a case that survives internal scrutiny.

If you don’t help them do that, deals stall or die.

Not because the product isn’t good.
Because the seller didn’t enable the buyer to win.

Great SaaS sellers don’t just demo features.
They help their champions become heroes inside their organizations.

https://atonom.ai/

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

80M+ views.
That’s ~1% of the world.

People didn’t share it because it scared them.

They shared it because it confirmed something they already felt.

The February 2020 analogy matters.

Flights were full.
Offices were open.
Life looked normal.

But the curve had already bent.

AI feels similar.

This isn’t 3.1 vs 3.2.
This is linear turning vertical.

What used to require:
• Back-and-forth iteration
• Multiple handoffs
• Days of refinement

Now happens in:
• One well-structured prompt
• One feedback loop
• One autonomous build cycle

The first industry to feel it?

Software engineering.

For 20 years, engineering was the safest bet in the economy.

Then AI went from:
Autocomplete
→ Code suggestions
→ Full scaffolding
→ Autonomous builders

In under two years.

Engineering wasn’t targeted.

It was simply closest to the blast radius.

AI labs optimized for code because code builds AI.

Once that loop worked, the capability expanded.

Now the compression spreads:

• Law
• Finance
• Consulting
• Customer support
• Revenue operations

Anywhere work = language + logic + structured process.

That’s why the article resonated.

One industry already compressed.

The rest are debating whether the shift is real.

This isn’t panic territory.

It’s attention territory.

The curve has already bent.

The only question is whether you see it while it’s happening — or after it hits your function.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Most service leaders will dismiss that.

They shouldn’t.

Traditional BPO is built on:
• Labor arbitrage
• Headcount scale
• Utilization math
• Margin on human throughput

That model worked when labor was the constraint.

AI changes the constraint.

If your business relies on:
• 1,000 agents answering tier-one tickets
• Teams updating CRMs
• Manual invoice processing
• Rules-based lead qualification
• Repetitive back-office tasks

You are operating a temporary data-processing layer.

And AI eats temporary layers.

The opportunity isn’t to shrink.

It’s to redesign.

The next-gen BPO likely looks like:
• 50 high-skill operators
• 5,000 AI workers
• Outcome-based pricing
• 24/7 execution
• No training lag
• No attrition

This is no longer about cost per FTE.

It’s about orchestration per outcome.

Nearshore will still matter for complex, judgment-heavy workflows.

Offshore will still matter for scale.

But the dominant layer becomes “Smartshore”:
BPOs that specialize in AI agent orchestration and Cloud Employee management.

The fork in the road:

Defend seats and optimize headcount math.

Or rebuild around output and orchestration.

Comfort vs inevitability.

The market won’t reward comfort.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

• $2 per conversation
• $0.10 per action
• $125+ per user per month

One product. Three models.

Why?

Because SaaS was built on seats.

But AI replaces seats.

If your AI works, customers need fewer humans.
If customers need fewer humans, they need fewer licenses.
If you charge per seat, your best product eats your own revenue.

That’s the revenue paradox every incumbent is staring at right now.

So companies are experimenting in public.

The PricingSaaS 500 tracked 1,800+ pricing changes last year across the top 500 B2B and AI companies. That’s 3.6 pricing shifts per company in a single year.

Nobody has conviction yet.

We’re watching three camps form:

Per-seat incumbents trying to protect predictable ARR.

Usage-based vendors aligning price to compute and API calls.

Outcome-based challengers trying to tie price directly to value created.

This is not a feature update cycle.

It’s a structural shift.

For twenty years, software was access.
Now AI is output.

And output doesn’t map cleanly to logins.

When software becomes labor, you price it like labor.

That’s the shift.

The companies that figure out how to align price with output, capacity, and business impact will win.

Everyone else is just adjusting knobs and hoping the spreadsheet holds.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Not toy benchmarks.
Not controlled prompts.
Actual client work.

Result:
Top agent success rate → 2.5%
Failure rate → 97%

Why?

Because real work is messy.

• Specs change mid-project
• Clients contradict themselves
• Quality is subjective
• Feedback is vague
• Files must actually function
• Iteration is constant

Agents don’t fail because they lack raw intelligence.

They fail because they need structure.

What the study also showed:

AI performs well in:
• Structured data tasks
• Simple visual generation
• Audio edits
• Report compilation
• Basic dashboards

Clear inputs. Clear outputs. Defined scope.

The insight:

AI struggles with ambiguity.
AI thrives on structure.

So the real opportunity isn’t replacing complex professionals.

It’s redesigning workflows.

Humans own:
• Ambiguity
• Judgment
• Taste
• Negotiation
• Changing context

AI owns:
• Repetition
• Standardization
• Throughput
• Clearly defined tasks

The companies that win won’t drop agents into chaos.

They’ll redesign work so machines handle the structured layer and humans operate at the messy layer.

Messy is still job security.

Structure is already automated.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

It’s not.

It’s about data integrity.

Since 2023, Salesforce has deployed $12B in acquisitions:

• ~$10B into data infrastructure
• ~$2B into the agent layer

That ratio tells you everything.

Why?

Because enterprise “truth” is a mess:

• Multiple versions of the customer
• Conflicting systems of record
• Permissions nobody fully understands
• Unstructured docs buried across drives
• Call notes trapped in tools
• Context locked inside rep brains

And yet companies think layering agents on top of that will magically fix it.

The real failure mode of AI agents isn’t intelligence.

It’s corrupted inputs.

An agent sitting on bad data is just a confident liar with permissions.

That risk scales fast.

So Salesforce is doing the unsexy work first:
Cleaning records. Normalizing systems. Reconciling data. Tightening governance.

The companies that win this cycle won’t have the flashiest demo.

They’ll be able to answer three questions every time:

Where did this data come from?

Can we trust it?

Can the agent act safely on it?

If you can’t answer those, you’re not deploying AI.

You’re accelerating dysfunction.

Before you spin up 500 agents, fix the foundation.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

• Lunch-and-learns
• Internal prompt libraries
• Innovation committees
• “We’re exploring” language

That’s not transformation. That’s risk management.

Accenture drew a line in the sand:
If you want to lead, you have to use AI in your real workflow. On real work. With measurable impact.

This is organizational rewiring at scale.

For decades, leadership progression was tied to:
• Revenue managed
• Teams led
• Tenure accumulated
• Political capital built

Now there’s a new variable: AI leverage.

If two leaders hit the same number, but one did it with:
• Less headcount
• Lower cost
• Higher throughput
• Faster cycle times

That person wins.

This is the shift from headcount-based management to output-based leadership.

Key Takeaways:

If AI adoption isn’t tied to compensation, it’s optional.

Optional initiatives don’t change behavior.

AI fluency is becoming a leadership competency, not a technical skill.

The leaders who don’t use AI won’t stay leaders.

We are moving from:
“Try ChatGPT.”
To:
“Show me how you used AI to move the business.”

The organizations that wire this into performance systems will compound.
The ones that don’t will quietly fall behind.

Leaders will either use AI… or report to someone who does.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

$2 per conversation

$0.10 per action

$125+ per user per month

One product. Three ways to charge.

That’s not clarity. That’s a market in transition.

For twenty years, SaaS charged for access. Per seat. Per login. Per user.

AI delivers output. And output replaces human effort.

If your AI works:

Customers need fewer seats

Fewer seats mean shrinking revenue

Your best product starts cannibalizing your core business

That’s the seat-based trap.

Across the top 500 B2B and AI companies, there were 1,800+ pricing changes last year. Everyone is experimenting because no one knows where durable pricing lands.

Seats protect predictability.
Credits protect margins.
Consumption lowers risk.
Outcome pricing bets on value.

This episode breaks down the real pricing war happening in AI and what it means when software stops being software and starts acting like labor.

Because when software becomes labor, you don’t price access.

You price capacity.

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/

Get my weekly breakdown of AI, GTM, and Cloud Employees:
https://atonom.ai/newsletter

Ready to hire your first Cloud Employee?
https://atonom.ai/