Signals

Founder & CEO

Silicon Slopes

Silicon Slopes is a 501(c)(3) nonprofit organization empowering Utah's tech community to learn, connect, and serve.

Co-Founder | Board Member

Utah Tech University

Board of Trustees

InsideSales.com

InsideSales.com offers the industry’s leading sales acceleration platform built on Neuralytics, a predictive and prescriptive self-learning engine that drives revenue growth by delivering an optimized experience for both salesperson and buyer. The platform fuels sales rep performance and provides buyer personalization with breakthrough innovations in predictive sales.

Founder and CEO

American Association of Inside Sales Professionals

The American Association of Inside Sales Professionals ("AA-ISP") is dedicated exclusively to advancing the profession of Inside Sales.

Advisory Board Member

Integr8ted Technology Solutions

Co-Founder

MeraQuest Capital

Associate

Deutsche Bank

Analyst in Consumer & Business Services Industry Investment Banking team. Advised primarily restaurant, retail, internet, and manufacturing clients on sell-side assignments, mergers and equity offerings.

Financial Analyst

David Elkington is the founder and CEO of Elkington Electric (EE), an electric vehicle company building the e1— an electric sports car developed on a disruptive modular electric platform that is reinventing modern vehicle design and delivery. 

Previously, Dave founded InsideSales.com, where he set out to transform the sales industry by empowering teams with intelligent tools, insights, and advanced technology. Under his leadership, the company raised over $300M in funding to support expansion and helped some of the world’s largest organizations digitally transform their sales. His work played a key role in launching the sales acceleration category and reshaping how sales organizations operate. 

A key leader in Utah’s tech scene, Dave co-founded Silicon Slopes and serves on the board of trustees at Utah Tech University. He’s been recognized by Ernst & Young, AA-ISP, and Utah Business, and his work has appeared in Harvard Business Review, MIT, Kellogg, and Forbes. 

He holds a BA in Philosophy from BYU with minors in Business, History, Japanese, and Hebrew, and studied at The Hebrew University of Jerusalem. A Utah native and father of two, he spends his free time running, skiing, hiking, and climbing.

SaaS isn’t slowing down. It’s getting eaten alive ... cannibalized.

Look at the Aventis Advisors growth chart. The trend isn’t subtle, it’s a cliff.

We went from 36 percent growth to 12 percent. Almost a decade of down and to the right.

With forecasts pointing to 11 percent … and falling, this feels like a pretty big structural shift. SaaS is starting to look like utilities and pipelines, durable and necessary, but no longer where the real upside lives.

And the reason is pretty simple. AI is cannibalizing the very work SaaS used to monetize. 

Here’s what the chart doesn’t show, but every operator feels.

1) SaaS used to sell “workflows.” AI sells “outcomes” (OaaS). Agents do the work inside the tool, so the tool stops being the product. The labor becomes the product. 

2) Budgets (and investors) are leaving SaaS and flowing to digital labor. CFOs aren’t buying more seats. They’re buying fewer humans. AI fits. SaaS doesn’t.

3) Feature parity killed differentiation. Entire categories are indistinguishable. CRM, CX, marketing automation … all the same. AI exposes how thin the moats always were.

4) Enterprises hit peak-SaaS years ago. Now they’re consolidating and cutting 20 to 40 percent of their stack. AI accelerates that purge.

5) AI startups are growing at speeds SaaS can’t touch. When companies hit nine figures in months, not years, investor expectations reset. SaaS looks slow, expensive, and operationally bloated.

6) Value is moving down the stack. The action is in compute, data, agents, and orchestration. SaaS is becoming a UI layer that AI sits on, not the engine driving the work.

The growth-rate collapse isn’t a mystery,  it’s more of a transfer of value. SaaS is maturing into a stable, cash-flow asset class. 

AI is becoming the new growth engine of the enterprise.

That means founders have a choice, build SaaS and optimize it like infrastructure, or build AI agents that replace the workflows SaaS was built to capture.

One path gives you stable multiples, the other gives you growth.

David Elkington

Founder Signals | Co-Founder Silicon Slopes

AI isn’t destroying the university. It’s exposing it.

The Current Affairs piece on “AI destroying the university” is worth reading. Not because it proves AI is dangerous, but because it shows how hollow the current educational model already is.

  - Students use AI to write papers
  - Professors use AI to write slides and grade
  - Admins cut budgets and buy shiny tools
  - Vendors call it “innovation”
  - Everyone pretends learning is happening

AI didn’t break that system. It just turned the lights on.

And students aren’t lazy. They’re rational.

For years we’ve told them:

  - Your GPA is your future
  - Admissions, scholarships, internships all run on the same score
  - One bad grade can cost you the “right” job or grad school

So of course they’ll use whatever tools they can. If the game is points, you play for points.

When education is a badge, not a process, “cheating” stops looking like a moral failure and starts looking like strategy and risk management. Society and universities built that environment long before AI showed up.

  - If a student can outsource the whole assignment to ChatGPT, that says more  about the assignment than the student.
  - If a professor can outsource lectures to a model, that says more about the class design than the professor.
  - If leadership thinks a campus-wide AI license = “innovation,” that tells you where the priorities moved.

Blaming AI misses the point. We turned education into a badge-collection game and then got shocked when everyone started speed-running it, ... and especially now with AI.

Keeping AI out of the classroom is the wrong fight. The real question is: What kind of learning AI can’t imitate in the first place?

  - Apprenticeship on real, live problems and projects
  - Writing where your own voice and stakes are visible
  - Work that ships into the world, not just collecting badges 
Judgment, experience and creativity, … not just output

In that world, AI isn’t a shortcut. It’s a force multiplier. It handles the boilerplate so humans can spend more time thinking, building, creating, and arguing about things that actually matter.

Universities won’t stay relevant by rolling out campus-branded chatbots and calling themselves “AI empowered.”

They’ll stay relevant by rebuilding around the one thing a model can’t own:
The formation of human reasoning and judgment.

AI isn’t destroying real education.
It’s revealing how much of what we called “education” was just GPA farming.

This morning, AI didn’t take over the world.
It just stopped working.

Cloudflare went down and took half the internet with it: X, ChatGPT, Claude, Character AI, Spotify, Uber, Canva, games like League of Legends and Valorant… even Downdetector couldn’t tell you what was broken because it was broken.

One provider has an “unusual traffic spike” and suddenly the AI era is offline.

We talk a lot about AI becoming too powerful ... Today showed the opposite: it’s still too fragile.

A few simple truths:
AI isn’t magic, it’s a dependency chain. CDN → DNS → APIs → models. One weak link and your “digital brain” goes dark.

We’re over-centralized. Too much of AI runs through a tiny set of vendors. When they sneeze, everyone else gets pneumonia.

The real near-term risk isn’t Skynet. It’s brittleness. Agents, support bots, workflows … all dead in the water over a routing issue.

If you’re betting on AI in your company, you have to ask:
 - What happens when our model or infra provider goes down?
 - Do we have multi-provider or fallback plans?
 - Can we fail gracefully to humans or simpler systems, instead of just showing a 500 error?

Today wasn’t a warning about superintelligence.
It was a warning about building the future of work on infrastructure that can still be taken out by a single bad hour.

The Adobe acquisition of SEMrush for $1.9B and looks like smart expansion move ... but its really a distress signal.

Adobe didn’t buy a search tool, they honestly bought a lifeline. Look at their chart. Down almost thirty percent this year. That’s not noise, that’s the market telling them the old game is over.

For twenty years Adobe owned the creative stack, and it was pretty untouchable. Then generative AI kicked the front door in. Now the moat isn’t Photoshop or Premiere. It’s the data underneath the work.

SEMrush gives them what they desperately need: real performance signals. Search intent. Behavioral data. The raw material for the next generation of creative agents. 

And that’s the real story. Not the acquisition. The shift.

Software categories aren’t expanding, they’re collapsing. Tools are becoming features. Features are becoming agents. Everything gravitates toward the platform with the most distribution and the deepest data.

Adobe sees the writing on the wall. If they don’t own the signals behind the work, they become a UI on top of someone else’s intelligence.

This deal isn’t about SEO. It’s about not becoming irrelevant. The era of “best-of-breed” point solutions is ending!

The platform wars just entered the consolidation phase. And the companies that survive will be the ones that own the data that trains the agents that run the workflows.

Adobe is fighting to stay on that list.

Everyone is treating Gemini 3 like it’s just another model upgrade. It’s not even close ... 

It’s Google making its first real move to own the AI workforce.

Forget the benchmark charts, those are table stakes now. Every model will have some row where it dominates and some row where it faceplants. 

What actually matters is where Gemini 3 lives.

Google didn’t ship a smarter chatbot. They plugged an agent framework into the backbone of the internet.

- Search.
- Workspace.
- Android.
- Vertex.

And now Antigravity, an IDE built for agents instead of humans. Think about that, Google is turning its entire ecosystem into an operating system for digital labor.

- Microsoft is trying to do the same thing with Office and Windows.
- OpenAI is trying to do it from a browser tab.
- Everyone else is pretending this is a model race.

It isn’t. It’s really more of a distribution race. 

If agents are the future of work, whoever owns the rails those agents run on ends up owning the workflows, the data, and the economics.

And here’s what I was debating with some founders and GTM leaders last night: This launch accelerates the death of the traditional SaaS UI.

- Agents don’t click your menus.
- They don’t navigate your onboarding flows.
- They don’t care about your beautiful left nav.
- They call your APIs, do the job, and move on.

That’s the threat and that’s the opportunity. The model war is slowing and the agent platform war just started. And Google showed up ready to play.

Everyone’s worried AI will automate their job. We should be more worried it will expose how much of our job was fake work.

I just read a Linkedin post mocking AI email ... everywhere people blame AI for “ruining” hiring, sales funnels, spamming customers.

But AI didn’t invent these bad workflows.
It just put them under a spotlight.

For 20+ years we optimized knowledge work for activity, volume and tracking, not outcomes.
Now we’ve handed that system a tool built for infinite speed and scale. Of course it snapped.

Hiring:
We turned talent into an SEO game:
  - Candidates blast 500 applications a day.
  - Recruiters rely on filters that judge formatting over skill.

Add AI and suddenly you get infinite polished resumes and a totally clogged funnel.
 That’s not an AI problem. That’s a workflow problem.

Sales & CRM:
We told reps the job was “log everything.”
AI shows up and auto-logs, auto-emails, auto-sequences.
Now the CRM is full… and nobody trusts what’s in it.
Again, workflow problem.

Same story in support and marketing:
If your strategy was “more volume,” AI just put it on steroids.

Here’s the pattern:
  - If a process can be abused by pushing a button 1,000 times, it was never measuring real work.
  - If AI junk slips through, your system was never measuring quality—only compliance.

AI is not the villain. AI is the stress test.
It ripped off the duct tape and showed us what was underneath.

The next model of work isn’t “everyone use AI tools.”
It’s agents and Cloud Employees doing the parts of the job humans were never built for at this scale:

  - Screening, qualifying, summarizing, routing in real time
  - Reading signals across systems and surfacing the 3 things that matter, not the 300 that just happened

Not to replace judgment ... but to clear out the fake work that prevented humans from using it.

The real fight isn’t AI vs jobs.
It’s broken workflows vs rebuilt ones.

Google wasn’t supposed to win the AI race.
It was supposed to be the dinosaur that missed it.

Instead, sometime between spring and now, Google turned into AI’s landlord — and everyone else started paying rent.

Alphabet is up ~70% at $3.8T passing Microsoft. The “did Google miss it?” debate is over. The question now is: who just lost pricing power because Google flipped its stack on?

Here’s what changed:

1: They wired AI into the cash machine, not around it
  - Gemini 3 isn’t a side project. It’s wired into Search, Android, and Workspace.
  - AI Overviews don’t kill search, they drive more of it and keep you inside Google.

2: They built a parallel Nvidia inside the company
  - TPUs were a science project. Now they’re product.
  - Google trains on its own chips, then rents those same chips through Cloud while a lot of “AI infra” startups just resell compute.

3: The antitrust bomb fizzled
  - Worst case was: break up Search and nuke default deals.
  - Instead, Google got a slap on the wrist and kept the engine. In an AI world, that distribution is lethal.

4: Buffett called it “real”
  - Berkshire didn’t buy AI hype. It bought Alphabet.
  - That’s conservative capital saying: this is core infra with a cash hose attached.

5: “AI risk” turned into “AI gravity”
  - AI was supposed to wreck search economics.
  - Instead, it’s making it harder for anyone else to peel off users or ad dollars while Google funds the whole experiment with its own profits.

Now this runs straight into Black Friday.

Tomorrow a big chunk of shoppers won’t start on a retailer site or even a search bar, they’ll start by prompting an AI.

And in a lot of cases, that AI is still Google.
The interface changed. The landlord didn’t.

Zoom out:
  - SaaS is getting de-rated into a slower, utility-like asset class.
  - Google is getting re-rated as the full AI stack:
       Chips → Cloud → Models→ Platform → Distribution → Cash flow.

American's are arguing about AI like we have a backup plan. We don’t.

Every debate about AI has turned into a belief system. 
1) You’re either convinced AI is a bubble that will crash everything 
2) Or you believe it’s the engine that will save the economy

And everyone defends their position with the same intensity people used to reserve for religion or politics.

But strip out the emotion and look at the data sitting in front of us. Seriously.

  - Credit delinquencies are climbing
  - Consumer sentiment is scraping 15 year lows 
  - Views of personal finance were the lowest since 2009
  - 4 year college grads can’t get hired (25% unemployment)
  - Auto loan delinquencies at highest level on record (since 1994)
  - Outside of government, job growth is shrinking

Honestly, you could basically pick any economic indicator you wanted and it would not be good. I don't think this is noise. It’s the early innings of a recession. Now look at the other side of the ledger.

• Trillions of dollars are flooding into the AI Economy ($3–7T in AI infrastructure  by 2030 alone)
• Data centers, power grids, chip fabs, sovereign AI deals, federal contracts
• Amazon just announced a fifty-billion-dollar buildout for government AI infrastructure
• Nvidia, Alphabet, Microsoft, Meta, Oracle, Amazon, Tesla, and half the Fortune 500 are spending at historic levels

Here’s the part everyone keeps skipping over. If you remove AI, the U.S. economy is already underwater. AI isn’t inflating the economy. It’s propping it up. 

And this is where the poverty math matters. When Michael Green recently recalculated the real cost of simply participating in American life, the floor wasn’t $31K. It was closer to $140K. The median household makes $80K. Which means half the country is already below the poverty line before AI even enters the conversation.

So here’s the paradox.

Many of the most skeptical of AI are the same people most exposed to a collapsing cost structure. And the technology they distrust happens to be the only force with enough scale to bend that cost structure back down.

 - Better productivity.
 - Smaller companies with bigger capability.
 - Lower admin burden in healthcare and education.
 - More entrepreneurship.
 - More meaningful work per person.
 - Less overhead per job.

AI is not a magic cure. But when every non-AI indicator is flashing recession, and AI is the only sector at this scale generating investment, hiring, and output, you have to ask ourselves... If AI fails, what exactly is left to catch us? 

That’s the wager. Not whether AI is perfect. But whether the alternative is even survivable.

OpenAI just declared “code red” as Google closes the gap. Everyone’s treating it like the model race is heating up again. But that’s the wrong takeaway.

The model race is ending and the operational race is beginning. 

We’re past the point where a few extra benchmark points decide winners. The future is something more fundamental, which companies can actually deploy AI into the bloodstream of their business.

Google catching up doesn’t threaten OpenAI’s models. It threatens the illusion that models are the bottleneck.

The real bottleneck is inside enterprises. 

Most firms still can’t run a single agent end to end. Workflows are brittle. Data is fragmented. Org charts are built for email, not autonomy. 

And while the labs are shipping weekly, the Fortune 100 is still supporting their AI strategy with slide decks and pilots.

I think that’s the real code red. To win, OpenAI can’t just ship models. They have to turn compute into workflows, workflows into agents, and agents into outcomes.

AI finally just hit the enterprise at real scale with a $10B valuation, following a $350M round.

Bret Taylor's (former Salesforce.com Co-CEO) Sierra hitting $100M ARR is the loudest signal yet that enterprise AI agents are not a toy market. 

They’re real. They’re funded. And the Fortune 500 is buying.

Look at the AI evolution stack right now.
  - AI GPU Chips (winning): Nvidia had one of the most explosive quarters in tech history. Google is pushing TPUs into the wild with Anthropic. Meta is following.
  - AI Data centers (winning): CoreWeave, Oracle, Amazon committing $50B for new government capacity.
 - LLM Models (winning). OpenAI and Anthropic defined the category, and Google just vaulted into first with Gemini 3, Opus 4.5 groundbreaking in coding capabilities.
 - Developer tools (winning). Copilot, Cursor at a $29B valuation, Lovable, all eating the software IDE.

All that momentum has been below the enterprise workflow. Infrastructure. Models. Dev tools.

Sierra is the first meaningful example of AI breaking through one layer higher. Not productivity or copilots but actual back-office, customer-facing, go-to-market work at scale.

That’s why this $100M number matters. It proves that the enterprise is finally comfortable letting agents handle real operating workloads.

Now, if you look under the hood, the magic fades a bit. A lot of this is still custom AI builds, heavy consulting, heavy integration. Impressive traction, but not a truly scalable stack yet.

But it’s the same pattern we saw in early cloud. Massive early wins built through custom work and consulting, then real platforms emerged.

This is why I love seeing Sierra’s rise even though they’re technically competitors. It validates the category. It confirms enterprise demand. And it exposes the next opening.

If I’m honest, I think Signals has already crossed that line :)

Everyone keeps saying America is winning the AI race. But the signals tell a little different story ...

NBC News just reported that a surprising number of U.S. startups are now building on free Chinese open-weight models. Not testing them, not tinkering, but building real products on them because the economics are impossible to ignore.

At the same time, Google’s own CEO, Sundar Pichai is on national TV warning that America’s fragmented AI laws make it harder for U.S. companies to compete globally.

Two signals. Same direction.

Startups are choosing speed and affordability. China is providing both. U.S. regulation is adding friction. China is removing it. This isn’t about geopolitics. It’s about competitiveness.

AI is becoming the operating system of every SaaS company. And right now the U.S. stack is expensive, difficult, and buried under conflicting rules. Meanwhile, Chinese models are cheap, open, customizable, and moving at a pace the Valley can’t ignore.

If the U.S. tech ecosystem wants to keep the lead, the answer isn’t fear. It’s friction ... Remove it. If we want to stay in first place, we have to make building  faster, cheaper, and more flexible than anywhere else.

McKinsey's new study shows 57% of today’s work hours are technically automatable by AI and robots.

Most people see that and jump straight to, “Half the jobs are gone.”
I don’t think that’s what’s happening.

This isn’t a layoff story, it’s a workflow story.

McKinsey is basically telling us:
  - Almost half of what we do is structured, repeatable, rules-based.
  - Agents and robots can do that work today with current tech.
  - The real question isn’t if they will… it’s who chooses to use them first.

The interesting part: the skills don’t disappear.
Something like 70%+ of today’s in-demand skills show up in both automatable and non-automatable work.

Translation: skills are being reassigned between humans and AI, not deleted.

That’s how I think about AI Agents/Cloud Employees.
You’re not replacing your team.
You’re stripping out the 57% of work that never needed a human in the first place:
  - The copy-paste admin.
  - The “check three systems and update the fourth” work.
  - The rote follow-ups nobody has time for.

What’s left for humans?
Judgment. Trust. Strategy. Ambiguity. Relationships.

If McKinsey is right, the winners won’t be the companies that “use AI.”
It’ll be the ones that rewrite jobs so humans and AI Agents/Cloud Employees are working the same role from two different sides.

If 57% of our team’s work can be done by AI today, which parts do we start with?

Okay, this week in AI was a turning point. 
- The fake work is collapsing.
- Creative pipelines are shrinking from teams to prompts.
 - AI is propping up an economy that would otherwise be underwater.
 - And enterprise buyers are finally choosing agents over software.

These aren’t isolated headlines. They’re the same story. The old model ran on coordination and screens. The next one runs on agents, autonomy, and real output.

I broke the entire shift down in this week’s newsletter. Worth the read.

What happens when AI turns the hardest job in farming into a one-person task?

In Phoenix, a single operator with an iPad now does the work twenty people used to do on their hands and knees. The farm brought in Carbon Robotics’ LaserWeeder, and the most brutal task in agriculture flipped overnight. It scans the soil, identifies weeds, and vaporizes them with a laser. No fatigue and no delays, just throughput.

Agriculture didn’t adopt this because it loves new tech. It adopted it because the old model failed. It was too slow, too costly, and too fragile. When the economics break, the work changes.

Here’s how it always plays out.
 - Automate the pain point
 - The system reorganizes
 - Workers move up the stack
 - The old model dies quietly

Farming hit this point because the pressure was physical and unavoidable. When a job becomes too slow, too costly, and too hard on the people doing it, the system has no choice but to change. That same pressure already exists inside every modern company, it’s just hiding behind email, approvals, and coordination layers instead of heat and hard labor.

If one operator can do the work of twenty in a field, what happens when every team gets the equivalent of a LaserWeeder for their workflows, their data, and their decisions?

The story isn’t about farms. It’s about the end of the old work model.

Once AI takes the hard part, you don’t optimize the system, you rebuild it.

I played with Nano Banana Pro this weekend and yeah, it’s an amazing creative tool. But that framing misses the real point by a mile.

This isn’t a tool. This is Google hiring your next creative employee for you.

Look past the demos. The WIRED review hints at the real story. It’s not the images that matter,  it’s the fact that it does the work creative teams usually grind through, and it now sits inside Ads, Slides, Workspace, and Search.

That is not a feature upgrade, it more like a platform taking over the work. 

For the last twenty years, enterprises, myself included, built entire teams around production.
- Designers
- Coordinators
- Agencies
- Revisions
- Approvals

Nano Banana Pro might just collapse that entire pipeline into a prompt. And if that’s true, the competitive gap is about to move faster than most teams are ready for.

It had me thinking. Maybe the winners in the next cycle won’t be the ones with the “best” creative AI. Maybe the advantage shifts to the companies that accept the deeper truth, that creative capacity is now effectively infinite, and reorganize their teams around that reality.

Because once the platforms start embedding agents directly into your workflows, the job has to change. You stop managing execution and you start orchestrating outcomes.

The era of tools is ending. The era of autonomous creative employees has begun.

Okay, this week in AI was interesting. SaaS is getting eaten alive while AI takes over the workflows it used to monetize. Apple is bumping into the limits of the Cook era. Google and Microsoft are busy building the operating systems for the AI workforce. Adobe is buying signals just to stay relevant. And the labor market is shifting work from people to software.

These aren’t random headlines. They’re the same story. The last era ran on seats and screens. The next one runs on agents, data, and autonomy.

I broke the whole pattern down in this week’s newsletter. Worth a read.

A few days ago I shared some thoughts on what’s really happening in SaaS. 

Growth isn’t just slowing. It’s collapsing. That post struck a nerve, and for good reason.

SaaS isn’t broken. It is being disrupted by the same logic that once made it dominant. 

AI is not being added onto SaaS. It is replacing the workflows SaaS used to monetize. When agents do the work, the tool becomes the interface. The value moves into the layers underneath.

This Friday, I am joining Gabe Larsen to dig into what this shift means for operators, founders, and GTM leaders.

We will cover why budgets are moving from seats to outcomes, how digital labor is becoming the new execution layer, and why orchestration is quickly replacing tooling as the next competitive advantage.

SaaS is still here. But it is no longer where the upside lives. Check it out here: https://lnkd.in/e3SpJ9se

SaaS growth didn’t just cool off. It fell from 36 percent to 12 percent in under a decade.

That kind of drop usually means the model broke. This time it means the value moved.

On Friday Gabe Larsen and David Elkington will break down:
- How AI agents are taking the workflows SaaS used to monetize
- Why budgets are shifting from seats to digital labor
- Where the value is consolidating in data, compute and orchestration
- What this reset means for GTM, product and enterprise spend


If you want a clear view of the next decade of enterprise software, start here.

#Tech #saas #futureofwork

Big news. Leah Johns, Head of the Global Consumer Lab at Bain & Company is keynoting the #AICXSummit. 

If you’re trying to understand where AI actually fits into CX, she’s one of the few people who has the data, not just the opinions.

Leah spends her time watching how real shoppers are already using AI to decide what to buy, where to buy it, and how much to spend. She sees the behavior shifts before they hit the dashboards and helps brands act while everyone else is still guessing.

At the summit, she’s breaking down how AI is reshaping the shopper journey, how consumer expectations are shifting, and what leaders need to do now to stay relevant in a market that’s moving faster than they are.

This one will set the tone for the entire event. Full agenda and RSVP in the first comment.

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The U.S. added 119,000 jobs in September … It's being called a “mixed report.” I see it differently.

I wouldn't say the labor market is cooling, I'd say it's rebooting.

Almost all the job growth is coming from three sectors: 
  - Healthcare
  - Social assistance
  - Leisure and hospitality

Government chipped in. Everything else shrank. Outside those pockets, nonfarm payrolls are down 151,000 jobs this year. That is an economy quietly rewiring itself.

Unemployment rose to 4.4%. Underemployment is over 10%. Millions of people are stuck between the economy they trained for and the economy taking shape.

Look at what’s shrinking. 
- Management
- Administration
- Information industries
- Temp agencies 

These are digital, repeatable, system-based roles. Exactly where AI lands first. Corporations aren’t announcing it, but the behavior is obvious: freeze hiring, cut layers, push the work into software. Not layoffs. Replacement.

Meanwhile, small companies are hiring at 50x the rate of large ones:
  - They move faster. 
  - They adopt AI faster. 
  - They treat a single headcount as a hub for multiple digital workers, not one human grinding through tasks.

This is the divide I'm seeing. Autonomous Organizations are rewiring how they run. Slow adopters are rewriting slide decks about “transformation.”

The BLS still measures jobs like it’s 1995. Instead of asking how many jobs we added, we should be asking a simpler question: who’s actually doing the work now, people, software, or a mix of both?

This report isn’t mixed. It’s the signal that the old labor market is fading and a new one is quietly taking its place.

Next week is the #AICXSummit. I'm really looking forward to this event.

Over 40 leaders will share how they’re using AI in their CX strategies.
 
We’ll cover:
 - Automation without friction
 - Agent experience that actually improves
 - Support orgs built to scale
 - How AI is redefining ecommerce and premium service

Full speaker lineup in the comments.

Thanks to everyone participating.

parcelLab NoFraud Hark OrderProtection.com

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