The State of AI + Software: Where It’s Going - Fast

1. Key Themes

AI Native Companies Are Radically More Capital Efficient Despite Higher COGS

The data reveals a counterintuitive reality: AI companies have higher token costs but grow so quickly that their burn multiple is actually much better. "At 100 million ARR, the burn multiple falls to 0.4x for AI native companies versus 1.6 for classic SaaS companies. They're four times more efficient, four times more efficient in adding ARR." [00:07:26] This explains why VC money has flooded into AI despite concerns about gross margins - the speed of growth outpaces the cost of AI infrastructure.

The Death of Traditional Sales Team Scaling

AI companies are achieving $50M+ ARR with skeleton sales teams compared to historical benchmarks. Harry describes a conversation with a former team member: "At 50 million, you'd probably have at least 100 sales reps in the old days because at 50 million, you want to go to 100 million. So that's net 50 million of bookings, 500K net per rep with scaling and turnover. And that's pretty good. So you would need 100 sales reps at 50 million. He's going to have five in a team of AIs." [00:12:16] This person has "10,000 plus inbound leads a month" with just a five-person team, demonstrating how product-led growth combined with AI automation is fundamentally changing GTM efficiency.

Forward-Deployed Engineers Are the New Sales Hire

The biggest hiring trend in AI B2B is forward-deployed engineers, not salespeople. "Post sales is 31% of AI native companies versus as low as 22% in traditional SaaS." [00:17:04] Harry emphasizes that AI tools "do not work out of the box. This is one of the biggest lies in a lot of AI B2B applications that they magically work out of the box without training. They don't." [00:19:19] Companies are shifting resources from sales reps who "frequently don't know the product whatsoever" to technical people who can ensure customers are "trained and onboarded extremely well" before going live. [00:19:31]

Insane Inbound Demand Drives Magic Number Superiority

AI companies achieve profitability on sales and marketing spend in 6 months versus 2 years for traditional SaaS. "Look at what happens traditionally at scale and SaaS, 0.5X. That means traditionally at scale and SaaS...it takes you two years to go profitable in a customer, two years." [00:11:10] In contrast, AI companies hit 1.6X magic number at $100M ARR because "the best AI B2B companies just have insane demand...because they do something that's not like a little bit better...they can do things you could never do before." [00:09:51]

AI Training Requires Massive Upfront Investment

The reality of implementing AI agents demands significant time and resource commitment. Harry shares his experience: "All took about three to four weeks to train. And then iteration daily after that. And that training, that onboarding, we did it all. Even us and we're pretty AI savvy...We still did it together with the vendor for weeks." [00:17:49] This contradicts the "set and forget" mentality many have with traditional SaaS purchases.

2. Contrarian Perspectives

AI Washing Doesn't Work - Being "AI-Enabled" Is Now Table Stakes

While most assume adding AI features creates competitive advantage, Harry reveals: "94% of public B2B companies now mention AI and say they have AI agents." [00:24:36] Adobe claims "$5 billion of AI influenced revenue" despite not being ahead of competition. The contrarian insight: "Everyone's an AI company. So what really matters is...have you built something that is so disruptive with AI that it will generate massive market pull because it wasn't done before." [00:25:22] Simply having a copilot or AI features is now completely undifferentiated.

The Traditional SaaS Funding Market Has Completely Disappeared

Harry delivers a brutal reality check: "There is no interest in classic SaaS companies from VCs growing at pretty good rates. If you're growing 80% at 20 million or 70% at 50 million...no one's gonna fund you." [00:40:13] Even more surprisingly: "Not only are VCs not interested in a company at 20 million growing 80 percent, there's something much worse...private equity firms aren't interested either." [00:40:44] For a decade (2012-2023), PE firms would buy decent-growth SaaS companies at 6-10x revenue, providing an exit path. "I don't see any of those deals. They've disappeared." [00:41:21]

All Traditional GTM Plays Still Work - The Playbooks Don't

Counter to the prevailing "outbound is dead" narrative on LinkedIn, Harry argues: "All the old plays work just the playbooks don't...Look at the leaders in AI and B2B they're doing webinars, they have sales teams...they're doing outbound...they're doing events, they're doing field marketing, they're doing demand gen, they're doing multi touch, they're doing content marketing." [00:38:17] The issue isn't the channel - it's execution quality. "Your terrible text messages to folks aren't responded to anymore, your generic emails with 11 fonts and four colors...don't work anymore." [00:37:38]

It's Not Too Late Despite Feeling Behind

While Harry previously pushed hard deadlines ("if you didn't get your AI product out by June 30, you were too late"), he now acknowledges: "When I look at where we're just getting going on agents and enterprises, it's not too late for anybody...For most of you, your customers are still early. Most restaurants and beauty salons and regulated industries and big enterprises, they've barely started." [00:32:42] The 2026 market will be "10x larger for AI B2B than it is this year, at least 10x larger." [00:33:13]

Employee Productivity Must Be 20-30% Higher or You're Behind

Harry presents stark efficiency data: "Startups as they scale up have ARR per FTE has gone from 182 to 237...while operating expenses has remained flat, even with inflation." [00:26:01] His blunt conclusion: "If you're not working at least 20 to 30% harder than you were 25 months ago, you're behind the curve. And if you feel like you're working 20 to 30% harder than 24 months ago, good because that is the minimum required to be successful in today's world." [00:26:33]

3. Companies Identified

Replet

Description: AI-powered coding platform enabling non-engineers to build applications Why mentioned: Harry has personally "launched eight applications on Replet" including an "AI valuation calculator has been used almost half a million times." [00:08:48] Demonstrates high stickiness despite some usage plateau - "We will never churn from Replet. I have eight apps in production. I'm not taking them down." [00:08:48]

Gamma

Description: AI-powered presentation creation tool Why mentioned: "Gamma makes slides with AI and you can just tell it what slides you want and it can pull data from all different data sources. And we use it so that every SaaStr sponsor gets a custom deck made in minutes...Gamma's ROI is so high to us. We found it and we paid for it." [00:10:26] Example of product with such clear ROI that it requires minimal sales effort.

Loveable

Description: AI development platform (competitor to Replet) Why mentioned: Example of capital efficiency - "Lovable getting to $100,000,000 with 45 employees." [00:23:10] Also mentioned as reaching high revenue with minimal team size.

Cursor

Description: AI-powered code editor Why mentioned: Extreme capital efficiency example - "Certainly less than $100,000 to $150,000,000" in revenue. [00:23:14]

11 Labs

Description: AI voice generation company Why mentioned: Example of AI company doing traditional marketing effectively - "doing a huge event" while also having strong inbound demand. [00:29:43]

Opus Pro & Higgsfield

Description: AI video generation companies Why mentioned: Examples of "pretty cash efficient" AI companies in video, contrasting with the narrative that all AI companies have high token costs. [00:06:27]

Delphi

Description: AI agent platform Why mentioned: Powers SaaStr's general AI support agent. "Go to saastr.com, click on the bottom right where we have our general AI agent from Delphi and talk to it." [00:33:36] Trained on "20 million words of content...every tweet I've ever written." [00:34:04]

Qualified

Description: AI BDR platform Why mentioned: "We've got an AI BDR from Qualified. The team from Qualified is going to share that really works later today." [00:03:20]

Momentum (from Attention)

Description: Sales activity tracking and analytics tool Why mentioned: When rolled out at SaaStr, "someone on our sales team quit the day we rolled it out...because now all his actions all his data were going to show up in real time in a report and in Salesforce. You couldn't hide." [00:45:09] Demonstrates how AI tools expose non-performers.

Mangle Mint

Description: SaaS for salons, spas and doctors offices Why mentioned: Portfolio company example where CEO was concerned about "scaring the SDRs" with AI implementation, but forward-thinking SDRs embraced it. Growing "triple digits at eight figures in revenue." [00:42:50]

4. People Identified

Mark Benioff (Salesforce CEO)

Description: CEO of Salesforce, pioneering B2B SaaS leader Why mentioned: Harry interviewed him on 20VC about AI strategy. Benioff said the "number one thing he was jealous of Palantir was...how they've done in forward deployed engineers. He said, what I would love at Salesforce is that everybody's Salesforce AI works before they even pay us." [00:21:16] Highlighted how even traditional SaaS leaders recognize the need for radical changes in implementation approach. Also noted "so many of their customers have just started with AI. They're so early compared to what a lot of us are doing." [00:31:37]

Elon Musk

Description: CEO of X (formerly Twitter), Tesla, SpaceX Why mentioned: His approach to efficiency at Twitter presaged broader industry trends. "People thought Elon Musk was crazy when he bought Twitter and laid off two thirds of the company...everyone was unleaned in 2021. Everyone had twice as many employees for a dollar of revenue than they do today people are twice as efficient as they were in 2021." [00:35:26] While controversial, his aggressive cost-cutting influenced other tech leaders.

Sam Altman (OpenAI CEO)

Description: CEO of OpenAI, leading AI company Why mentioned: Example of effective multi-channel marketing despite having massive organic demand. "Why are Sam Altman everywhere? That dude is everywhere...and OpenAI is the most successful startup of all times...it's because multi-touch works. You've got to...show up." [00:39:26]

5. Operating Insights

The "Try One That Works" Implementation Strategy

For skeptics of AI agents, Harry's advice: "Stop saying that this crap doesn't work and go try one that does work...Go to saastr.com or saastr.ai...and click on AI mentor at the top...Ask any of your questions...We have folks that are on this all day long...95% of folks say it's pretty good." [00:34:24] The tactical insight: don't form opinions based on failed implementations - experience a well-trained agent first.

The Real Cost Structure of Enterprise AI Tools

Based on SaaStr's spending: "We spend maybe $10,000 a year on Salesforce...we spend $500,000 on our AI agents across these 21 agents." [00:46:04] This 50:1 ratio reveals where budget allocation is shifting. Additionally, "These apps that need to be trained with forward deployed engineers...I don't think very many of them are less than 30 or $50,000 a year and a lot of them actually try to kind of have a price point that's approaching a hundred K." [00:46:52]

The Daily Iteration Requirement

AI implementation isn't a one-time deployment: training takes "three to four weeks...and then iteration daily after that." [00:17:49] Teams must commit to ongoing refinement, not treat it like traditional software purchases.

Don't Hide Mediocre Performers - AI Tools Will Expose Them

When rolling out AI tracking tools, expect attrition: "The day we rolled it out someone on our sales team quit the day we rolled it out...because now all his actions all his data were going to show up in real time." [00:45:09] This is actually beneficial - use AI transparency to identify and upgrade talent.

Every Executive Should Deploy One AI Tool Themselves

Harry's prescription for getting educated: "If you feel behind in AI and go to market...the real answer is be a part of a deployment. Go buy any tool...But don't just buy it. Be part of the deployment. Train it yourself. Be part of the onboarding. Be part of the errors and the issues and the daily iterations that you have to do to get it going." [00:04:29] Hands-on experience is the only way to truly understand capabilities and limitations.

6. Overlooked Insights

The Conversion Rate Differential That Changes Everything

Buried in the metrics discussion: "AI native B2B companies close 56% of their free trials to paid versus 32% of non-AI." [00:15:13] This near-doubling of conversion rates receives less attention than growth rates or burn multiples, but it fundamentally alters unit economics. Combined with higher magic numbers, this means AI companies need dramatically less top-of-funnel volume to hit the same revenue targets - explaining why they can operate with 5-person sales teams instead of 100.

The Globalizing-While-Centralizing Paradox

Two seemingly contradictory trends are happening simultaneously: "Almost all the hiring and net hiring in tech is in SF. It's twice New York and basically after SF and New York there is no net hiring...But at the same time, we're using distributed and international folks on our team even more than ever before...going into 2026, we have far more employees that are what they call off-shored...from 24% of headcount...up to 30%." [00:27:33] This suggests successful companies are concentrating AI/product talent in SF while globalizing execution roles - a more nuanced strategy than "remote vs. in-office" debates suggest.