---
title: "How Much More Efficient Should a SaaS Startup Be When Using AI?"
description: "Discover how AI could drive 13% efficiency gains in SaaS startups, with engineering teams seeing 25% productivity boost. Data-driven analysis for tech leaders."
categories: ["financials","AI"]
keywords: ["SaaS startups","AI efficiency","Theory Ventures","Tomasz Tunguz","productivity gains","software engineering","content marketing","sales development"]
ai_summary: "Explore how AI can enhance SaaS startup efficiency by up to 13%, focusing on engineering and marketing productivity."
date: 2023-06-02
lastmod: 2026-07-23
canonical_url: https://www.tomtunguz.com/how-much-more-profitable-saas/
author: "Tomasz Tunguz"
---
If we assume some basic productivity gains in a typical SaaS company from AI in the next 12-24 months, how much more profitable will the business be?
Sales development, content marketing, & software engineering strike me as the workstreams that will benefit immediately.
| Team | Team Productivity Gain | % of Company | Overall Gain |
| --- | ----: | ---: | ---: |
| SDR/BDR | 15% | 10% | 1.5% |
| Content marketing | 30% | 5% | 1.5% |
| Engineering | 25% | 40% | 10% |
| Overall | - | - | 13% |
*See assumptions here*1
This thought experiment highlights a few ideas to validate :
1. AI initiatives focused on cost-reduction should start with engineering. AI's contribution to engineering productivity is 5x+ greater than other teams because of the scale of the gain & the company's team composition.
2. A quantum leap of productivity in less common workstreams doesn't materially change the financial profile of the company (though it may for the department). Workstreams within SaaS companies are specialized : content marketing, a valuable GTM strategy for many businesses, represents a low-single digit percentage of the employee population.
3. AI startups building next-generation offerings should prioritize common workflows shared across a significant fraction of the employee population or workflows for highly paid employees.
This 2x2 matrix captures these ideas. Startups should focus on AI for Execs (perhaps why legal has been a heavily funded category) & AI for common workstreams (code autocompletion).
There will be exceptions, especially where a user base is predominantly individual users or the willingness to pay for the software outweighs the productivity gains.
A mental model like this should guide both software procurement on the buyer side & product management on the vendor side especially in an procurement environment where every software vendor must justify their cost-savings or revenue increase.
----
1
BDRs are often mapped to AEs at 1:1 ratio & if we assume BDRs account for 25% of sales headcount (the remainder includes AE, sales operations, post-sales, & management). Assume a 15% productivity improvement that results in a 1.5% improvement across the company.
[Marketing has many different disciplines](https://tomtunguz.com/bill-macaitis-talk/) of which content marketing is one. Assume content marketers represent 5% of the company & the team sees a 30%% increase in productivity, the marketing team will improve overall productivity 1.5%.
Software engineering sees the greatest benefit. Comprising 40% of the company & producing 25% more output (Github says 50% of all code is now machine-generated), the startup should see a 10% overall improvement.