The Painful Gaps Between AI-Native and Traditional B2B Startups Today. Here’s a Checklist To Help. Which Are You … Really?

Anyone investing (including me) has been spending a lot of time with both AI-native startups and traditional B2B companies lately. We see what the newcomers are doing, what the hypergrowth players are doing, and how the incumbents are handling the AI Age.

The differences are becoming stark—not just in product, but in how they operate, how they’re structured, and frankly, how much energy is in the building.

Let me share what I’m seeing on the ground. And I challenge you to be honest — which are you, really? No matter how “AI” you claim you are, which playbook are you running?

Non-AI Startup Playbook (Yes, Even If You Have an AI Offering):

Here’s what I’m observing at non-AI startups right now:

Customer Success has become a crutch, not a growth lever. These companies are still loading up on CSMs rather than Forward Deployed Engineers (FDEs). The problem? CSMs manage relationships, or just as often these days, just upsells. FDEs solve AI agents problems and drive adoption. When your product requires a lot of hand-holding to deliver value, you’ve got a product problem masquerading as a support strategy.
AI features are not truly, honestly disruptive. Many traditional vendors have “AI” somewhere in the product now. But it’s slow. It’s not truly great. And most tellingly—it’s not the #1 focus of the company. The CEO isn’t waking up every morning obsessing over model improvements. It’s a feature, not the foundation.
Market share is slipping. I’m watching incumbents lose deals to AI-native upstarts that didn’t exist 18 months ago. Not because the incumbents are bad—they’re just not moving fast enough. The market is repricing what “good” looks like.
The honest truth about AI upsells: not truly earned. When traditional B2B amd SaaS vendors try to charge 2x-10x more for an AI agent layer on top of existing products, customers push back hard. Why? Because in many cases, the AI isn’t that great yet. It’s incremental, not transformational. Customers can tell the difference.
Shipping velocity has stalled. These companies still have large teams grinding through quarterly planning cycles for major releases. That cadence made sense when software was the product. It doesn’t work when AI is the product.
SMB churn is brutal. Smaller customers increasingly see traditional SaaS as expensive for what they get. They’re not wrong. When an AI-native alternative can deliver more value at a lower effective cost, why wouldn’t they churn?
There’s anxiety, but not urgency. This is maybe the most telling signal. Leaders at these companies know something is shifting. They’re worried. But that worry hasn’t translated into action. The urgency dial is at medium-to-low. They’re still operating like they have time. They might not.
RTO is half-hearted. Most are doing 2-3 days a week in practice. Nothing wrong with hybrid—but it’s often a symptom of a broader intensity gap. When the building isn’t buzzing, it’s hard to move fast.

Basically everyone in B2B that has seen growth slow is running the playbook above.

True AI-Native Startups Are Operating on a Different Planet. But In B2B, It’s Not Because They Have Magic LLMs or APIs.

The contrast couldn’t be sharper:

$700K ARR per employee. Read that again. The best AI-native startups are achieving revenue efficiency numbers that would have seemed impossible three years ago. When your product does the work that used to require a services team, your unit economics look completely different.
ACVs are unusually high—and customers pay willingly. Here’s the thing about agents vs. traditional SaaS: an agent replaces work. SaaS organizes work. When you’re actually doing the job instead of just providing the tools to do it, you can charge like it. Customers understand they’re paying for outcomes, not seats.
Major releases every 60 days. Not minor updates. Major agentic improvements. The pace of iteration is relentless. This is what happens when you have small, focused teams that ship fast because the AI itself handles complexity that used to require engineering armies.
Yes, inference costs are high. No, it doesn’t matter. I hear this concern a lot from investors stuck in traditional SaaS margin thinking. But here’s the math that matters: if your revenue growth is 3x faster and your headcount is 5x lower, you can absorb higher COGS and still build a better business. The leverage is in the operating model, not the gross margin line.
In enterprise, deep support for onboarding with many FDEs. CSMs are smaller role players. The best AI-native companies are pouring resources into onboarding with Forward Deployed Engineers. They know that time-to-value is everything. If an enterprise customer sees results in week two instead of month six, you’ve won. The investment in FDEs pays for itself many times over.
PLG actually works. These products are hyper easy to adopt. You don’t need a 45-minute demo and a three-month implementation. Users try it, see value, expand. That’s been the PLG promise for a decade. AI-native products are finally delivering on it.
Low anxiety, very high urgency. This is the inverse of what I see at traditional companies—and it’s fascinating. These teams aren’t anxious. They’re confident in where the market is going. But they’re moving with extreme urgency anyway. They know the window to win is now. They’re not worried about whether AI matters. They’re worried about shipping fast enough to capture the opportunity.
Full RTO, and it’s not a mandate—it’s a choice. Most of the AI-native teams I’m spending time with are back in the office five days a week. Not because some policy requires it. Because they want to be there. The energy is palpable. When you’re shipping major releases every 60 days and the market is moving this fast, being in the same room matters.

How many of these are true at your company? Be Honest.

Which Are You? Be Honest With These 2 Checklists

If you’re building a traditional SaaS company right now, you need to have a real conversation with yourself and your team:

Is AI a feature or the foundation? If it’s a feature, you’re probably going to get outcompeted by someone for whom it’s the foundation. That’s just how technology transitions work.

What’s your release velocity? If you’re still doing quarterly major releases, you’re bringing a knife to a gunfight. The best AI-native companies are shipping meaningful improvements every few weeks.

Where’s your leverage? Revenue per employee is becoming the defining metric of this era. If you need 200 people to get to $20M ARR, and your competitor needs 30, you’re not in the same business.

Are you charging for the right thing? Seats made sense when humans used software to do work. It makes less sense when AI does the work. The pricing model has to match the value delivery mechanism.

Is your anxiety converting to urgency? A lot of founders I talk to are worried about AI disruption. Worry is cheap. What matters is whether that worry is driving real changes in how you build, ship, and operate. If you’re anxious but your team is still on a quarterly release cycle and working three days a week from the office, your actions don’t match your concern.

For Fundraising: You Know This, But The Bar Has Changed

I’m being much more selective about non-AI investments now. Not because traditional SaaS is dead—it’s not. There will be good companies built without AI at the core for years to come.

But the bar for “great” has shifted. A great SaaS company in 2025 looks different than a great SaaS company in 2021. If I’m writing a check, I want to see:

AI as the core, not the garnish
Release velocity measured in weeks, not quarters
A path to $500K+ ARR per employee
FDE-driven enterprise motion, not CSM-heavy support
Pricing that reflects agent economics, not seat economics
Urgency that matches the opportunity

Saying You Are “AI” But Not Running the AI Playbook Doesn’t Work in B2B Today

We’re watching a generational shift in how software companies are built and operated. The AI-native startups aren’t just building better products—they’re building better businesses. Higher revenue per employee. Faster shipping. Better enterprise adoption. Easier PLG. And a level of intensity and urgency that traditional companies aren’t matching.

Traditional SaaS vendors can adapt. Some will. But the window is narrowing. The startups that figure this out now will compound those advantages for years. The ones that keep running the 2019 playbook are going to find themselves increasingly uncompetitive.

The market is telling us something. We should listen.