AI Creates the Problems it Solves
The Red Queen Scenarios in Sales & HR Are Fueled by AI Apps
Yesterday a friend starting their job search asked for my take on today’s job market. In my note back, I noted that AI-assisted writing is making it even harder to stand out:
AI has made it so easy for people to write cover letters tailored to the job description and the goals of the company, more and more people are applying to each posted listing – requirements be damned. It’s been the case for a while that automated-application tools are drowning HR and recruiters, but that flood has turned into a deluge.
Automated resume screening tools were already dealing with job applicants who were juicing their resumes with keyword stuffing and invisible text. But these tactics have been amplified – on both sides! – by LLM-powered tools.
Applying and hiring today is a perfect example of a “Red Queen situation”, a term from evolutionary biology inspired by a quote from the Red Queen in Alice in Wonderland:
“Here, you see, it takes all the running you can do, to keep in the same place.”
But this dynamic isn’t limited to HR… it’s also growing in sales and marketing.
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Today, Kyle Poyar posted a stat from Clouded Judgement that blew my mind: in Q1, public SaaS companies had an average adjusted CAC payback period of 57 months. For those of you not familiar with SaaS metrics, “CAC” stands for “customer acquisition cost.” It’s how much you spend on sales and marketing to close one customer. Kyle spells it out:
Said differently, it takes the BEST companies nearly five years to recover their sales & marketing spending. Five years!
And this isn’t an outlier average! Here’s the distribution, via Altimeter:
Kyle notes, “Zero public companies ended Q1 with a CAC payback period of less than 12 months. Only five were under 24 months.”
Why do these companies have to spend so much money to get customers?
Well, AI of course. SaaS marketing icon, Dave Kellogg weighs in, saying, saying there’s a pipeline crisis (“it’s harder and harder to cost effectively generate pipeline.”) and a traffic crisis (“caused by AI and other forms of search front-running”).
What strikes me is how these figures fly in the face of one of the first post-ChatGPT AI use cases: AI-powered SDR replacements. “SDR” stands for, “sales development representative.” These are the people sending emails and making cold calls, trawling for leads to hand off to higher paid sellers. SDR work is perceived as highly rote, making it a natural fit for the first wave of “human replacement” offerings.
AI SDR replacement startups were everywhere in 2024. You probably remember this viral billboard that was plastered all over San Francisco:
Artisan is one such AI-automated outreach service. There’s also Success.ai, B2B Rocket, Seamless.ai, and so on.
But as we’ve seen, despite the growth of this sector, companies are paying more to acquire customers, not less! As Kellogg notes, AI has both created an incredible amount of noise and given prospective customers tools to cut through it. People turn to ChatGPT, Claude, and Google Search summaries to navigate the deluge. And AI-powered email abstracts and filtering are powered by Gemini and Copilot.
It’s an arms race. A Red Queen scenario. The AI ecosystem is creating the problems it aims to solve.
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Is there a fix for all of this? Will it normalize? I don’t know. I do know that both the examples above have created a bit of an ironic situation: the growth of AI has dramatically increased the value of human connection.
In both the job market and sales, knowing someone at your destination is perhaps the one tool to cut through the crap.