Venture Capitalist at Theory

About / Categories / Subscribe / Twitter

2 minute read / Mar 21, 2024 /

AI SaaS Companies Will Be More Profitable

Will AI sofware companies operate with better or worse profitability than a classic SaaS company?

Initially, I thought worse since the expense of serving AI as a product is signficantly higher.

But now I’m not so sure. AI SaaS may be much more profitable than the -10% average net income margins of the current crop of public businesses.

Yes, AI inflates the cost to serve the product. Google queries may be 10x more expensive than standard search results. That’s an unfair comparison since Google has focused on classic query cost optimization for more than 20 years.

But let’s disregard that.

AI is also a deflationary force & a mighty one at that. There are 4 line items in a startup’s profit & loss statement that will be impacted by AI :

P&L Line Item AI’s Effect
Cost of Goods Sold Inflationary for infrastructure ; deflationary for support
Research & Development Deflationary : engineers are 50-75% more productive
Sales & Marketing Initially deflationary : sales people will be more productive
General & Administrative Deflationary : more productivity in legal & finance

COGS will both increase because AI is expensive but decrease because AI could halve customer support (CS) costs. Klarna slashed their CS spend by 66% with AI. Let’s declare this tug-of-war a wash.

Building products will cost half-as-much as in the past because of AI. Microsoft & ServiceNow have seen the impact on their teams at scale. Deflationary.

Sales & marketing will initially see a boost from AI as the early adopters reduce cost-of-customer acquisition through novel techniques. But over time, everyone will use AI & the efficiencies will be competed away.

General & administrative should see productivity gains across legal & finance. The impact isn’t yet broadly known but likely mildly-to-significantly deflationary.

On average AI-infused software companies should benefit from AI’s productivity gains at the bottom line.

Particularly as the industry evolves to smaller, more efficient models with similar performance but at 1% of the cost.


Read More:

Analyze All the Things : Data Omniscience with Omni