Oct 22, 2025 · 48m · saastr
The State of AI + Software: Where It’s Going - Fast
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this SaaStr presentation, Jason Lemkin analyzes the profound economic and operational shifts from traditional SaaS to AI-native software, highlighting how hyper-lean teams, Forward Deployed Engineers, and superior capital efficiency are reshaping enterprise go-to-market strategies. Through data-driven benchmarks and real-world playbooks, he demonstrates how B2B companies must adapt to an AI-driven software landscape.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Jason holds 99.2% of the talking time here. How this is scored →
Jason as informed peer 1.7 Guest teaching 0.0 Guest disagreement 0.0 Jason pushing back 0.0
speaking balance: gold is Jason, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 42:28 Amelia questions sales team disruption
In a completely collaborative webinar format, the highest friction point is Amelia raising the awkward issue of team panic around AI SDR deployment.
Hardest push from Jason ▶ 44:25 Jason rejects comforting underperforming repsJason pushes back against the instinct to placate nervous SDRs, arguing transparent performance tracking inevitably leads poor performers to quit.
Biggest teaching moment ▶ 12:08 Amelia clarifies sales role divisionAmelia interjects to clarify that one of the few allocated sales headcount positions is dedicated strictly to managing the AI systems.
Jason holds their own ▶ 45:50 Jason details AI spend versus CRM costsJason demonstrates direct financial expertise by contrasting SaaStr's $10,000 CRM budget with their $500,000 annual expenditure across 21 AI agents.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Jason as informed peer | Guest teaching | Guest disagreement | Jason pushing back | Why |
|---|---|---|---|---|---|---|
| Event Announcement: SaaStr AI London 2024 | 0 | 0 | 0 | 0 | This segment consists of housekeeping, event announcements for SaaStr AI London, and an opening monologue by Jason with only a single affirmative check-in from Amelia. | |
| Iconiq Report: Token Costs vs. Superior Burn Multiples | 0 | 0 | 0 | 0 | Pure solo presentation segment where Jason breaks down the Iconiq Growth report metrics regarding AI token costs versus burn multiples. | |
| Efficiency Metrics: Magic Numbers and Hyper-Lean Sales | 4 | 0 | 0 | 0 | Jason presents data on AI magic numbers and lean sales teams, briefly conversing with Amelia to confirm an anecdote about a mutual colleague running a lean sales team. | |
| Funnel Conversions: High Free-to-Paid Ratios in AI | 0 | 0 | 0 | 0 | Pure monologue by Jason explaining free-to-paid trial conversion rates and the rise of forward deployed engineers for model onboarding. | |
| Lean Scaling: Single Products and Minimal Headcount | 0 | 0 | 0 | 0 | Solo presentation segment detailing hyper-lean headcount structures driven by single-product architectures. | |
| Market Reality: Why AI Washing and Copilots Fail | 0 | 0 | 0 | 0 | Monologue analyzing why cosmetic AI additions fail and reviewing trends in revenue per employee and distributed engineering headcount. | |
| Venture Polarization: $377B Inflows into AI vs SaaS | 0 | 0 | 0 | 0 | Solo presentation tracking $377B in venture capital inflows into AI companies versus traditional SaaS stagnation. | |
| Go-To-Market Execution: Modernizing Classic Marketing Plays | 0 | 0 | 0 | 0 | Monologue advising go-to-market leaders to modernize core multi-touch marketing plays rather than abandoning them. | |
| Fundraising Diagnostics: SaaStr AI VC Benchmarking Tool | 0 | 0 | 0 | 0 | Jason delivers a solo wrap-up discussing private equity dynamics and introduces the SaaStr AI VC diagnostic tool before transitioning to Q&A. | |
| Q&A: Introducing AI SDRs Without Team Panic | 7 | 0 | 0 | 0 | Amelia asks a direct audience question about introducing AI SDRs without causing panic, and Jason provides deep operational domain knowledge citing first-hand portfolio examples. | |
| Q&A: Estimating Annual AI Costs and Final Thoughts | 8 | 0 | 0 | 0 | Amelia asks about budgeting for AI, prompting Jason to provide concrete budget breakdowns from SaaStr's 21 AI agents and forward deployed engineering expenses. |
Statements from this episode (27)
SaaStr Scaled From Zero to 20 AI Agents in Less Than a Year
“I mean, just on our little team, you know, at the end of Q one, we had no AI agents in production. We had nothing. Then we added a general agent for support. And now we've got almost 20 agents. We've got Four different AISDRs running. We just rolled out Salesf…”
Set-and-Forget AI Deployments Teach Operators Absolutely Nothing
“Don't just set and forget and buy. You will learn nothing. You will learn absolutely nothing. Be part of a deployment to see how it really works. Otherwise you'll never really learn.”
AI-Native Burn Multiples Fall to 0.4x at $100M ARR
“At a hundred million ARR, the burn multiple falls to 0.4 X for AI native companies versus 1.6 for classic SAS companies. They're four times more efficient in adding ARR.”
Top AI B2B Companies Achieve 1.6 Magic Number and 6-Month Payback
“Remember, you know, a magic number better than one means you're making your money back on sales and marketing in less than a year. And look at the ones that are rocketing at a hundred million in ARR or rather above one, they're at 1.6, they're going profitable…”
Public SaaS Companies Take Two Full Years to Break Even on CAC
“Traditionally it's scale in SAS. And this is true across almost all public SAS and B to B companies takes you two years to go profitable on the customer two years.”
Lemkin Deployed Eight Production Apps Using Replit Without an Engineer
“Have I been able without an engineer to put eight apps into production that have been used half a million times?”
AI-Native B2B Converts 56% of Free Trials to Paid Versus 32%
“And if you look here, it's especially prominent scale north of a hundred million AI native B to B companies close turn. 56% of their free trials to paid. Versus 32 of non-AI.”
Post-Sales Accounts for 31% of Headcount at AI-Native Software Companies
“Host sales is 31% of AI native companies versus as low as 22% in traditional SAS.”
B2B AI Tools Do Not Work Out of the Box
“This is one of the biggest lies in a lot of AI B to B applications that they magically work out of the box without training. They don't.”
Forward Deployed Engineers Are the Strongest Hiring Trend in B2B Software
“The forward deployed engineer is by far the strongest hiring trend in the last 12 months.”
Marc Benioff Is Envious of Palantir's Forward Deployed Engineering Model
“He said, this is the number one thing he was jealous of Palantir was one of it was how, how, how, how well they charge their customers, but he was most jealous of what they've done in forward deployed engineers. He said, what I would love at Salesforce is that…”
Public SaaS Revenue per Employee Benchmark Has Doubled to $400K
“Not only has AI changed the world, but we're never going to live in a world where 200,000 dollars per employee is tolerable when you're public. It's now 400,000 and up 400,000 to 500,000.”
Lovable Is Scaling Toward $100M ARR With Only 45 Employees
“Lovable getting to a hundred million with 45 employees.”
94% of Public B2B Software Companies Claim to Have AI Agents
“94% of public B to B companies now mention AI and say they have AI agents.”
Lemkin Calls Adobe's $5B AI-Influenced Revenue Metric 'Malarkey'
“Adobe on its last earnings called Adobe is, I mean, it has some AI tools in, in create creative, but it's not ahead of a lot of the competition. I think it said it had five billion of AI influenced revenue. What malarkey.”
Startup ARR per Full-Time Employee Climbed From $182K to $237K
“Startups are going, as they scale up, have ARR per FDE has gone from 182 to 237. Again, once you're public, it's more like 400 to 500. But this is materially more significant while operating expenses has remained flat even with inflation.”
Net Tech Hiring Is Now Negative in Austin and Miami
“SF, almost all the hiring and net hiring in tech is in SF. It's twice New York. And basically after SF in New York, there is no net hiring. It's net negative in Austin. It's net negative in Miami.”
AI Venture Funding Hit $377B in the First Half of the Year
“Last year was three hundred and sixty three billion, which was a massive jump from 20, 23, as you can see, but already this year, It's exceeded all of last year, just in the first six months alone, three hundred and seventy seven billion in the first six month…”
SaaStr's Internal AI Agent Is Trained on 20 Million Words
“Saster's AI, which is trained on twenty million words of content. It's trained on every tweet I've ever written. It will be trained tomorrow on this video automatically. Everything I say will be ingested into That AI, AI tomorrow”
Tech Companies Are Twice as Efficient Today as They Were in 2021
“Everyone had twice as many employees per dollar of revenue than they have today. People are twice as efficient as they were in 20, 21.”
VCs Have Zero Interest in Classic SaaS Companies Growing 80%
“There is no interest in classic SAS companies from VCs growing at pretty good rates. If you're growing 80% at twenty million or 70% at fifty million, it's not cool, dude, but there, no one's going to fund you. Nobody.”
PE Acquisitions of Classic SaaS at 6-10x Multiples Have Disappeared
“Private equity firms aren't interested either. And there used to be from 2012, 2013 until 2023. So there was a decade where if your growth was decent, but not great in SAS, but your burn rate was low and your NRR was high, maybe VCs wouldn't touch you, but a p…”
A SaaStr Salesperson Quit the Day AI Tracking Was Deployed
“Even at Saster on our little team, we rolled out a tool called momentum for AI or from attention. They're great too. Even our little team, the day we rolled it out, someone on our sales team quit. The day we rolled it out.”
SaaStr Notionally Spends $500K Across Its 21 Internal AI Agents
“Notionally, we spend 500,000 dollars on our AI agents across these 21 agents, 500,000.”
Effective Enterprise AI Apps With FDEs Cost $30K to $100K Annually
“These apps that need to be trained with forward deployed engineers and work. I don't think very many of them are less than 30 or 50,000 dollars a year. And a lot of them actually try to kind of have a price point that's approaching a hundred K like 60 K a year…”
Cheap AI Tools Shift Massive Training Burdens Onto Software Buyers
“Be wary of super cheap apps. Be wary of super cheap. It's not that we're not getting there. It's not that everything isn't going to get better, but it's, you can't cut the corner on training. So if instead of 50 grand a year, you're buying a solution that's 50…”