Never waste a good crisis
Building reliable AI products amongst the chaos
When you’re a thriving business, it can be easy to fall asleep at the cash register. You’ve had success for so long, you forget how to adapt and react.
Take Blockbuster.
It kept dismissing the internet, there was always a rational-sounding reason to wait: people won’t want to watch movies on their laptop, patrons love the experience of strolling the aisles, the economics won’t work at scale.
Every single one of those lines crumbled. And by the time Blockbuster took the threat of Netflix seriously, it was too late.
Des has been thinking a lot about Blockbuster lately – because he believes most SaaS companies are living that story right now, just with AI. Last week, he joined the Billions podcast to talk about what it takes to avoid that fate and adapt to the AI age.
Here are the biggest takeaways:
A pivot among chaos
Fifteen days after ChatGPT launched in November 2022, Intercom was already building Fin.
The urgency was prompted by a demonstration from Ciaran. He asked ChatGPT how to install Intercom on a mobile app – the kind of question the support team gets regularly – and it answered it perfectly in under five seconds. “No typical human efficiency software would have got to that outcome and then of course it could answer it in any question in any language and it could do it 24/7.”
Even accounting for hallucinations and guardrails, it was clear this would change customer service irreparably.
What followed was a hard pivot: ripped-up roadmaps, reallocated resources, a new domain, and a new brand direction. Prior to that, Intercom’s topline revenue wasn’t shrinking, but growth had slowed and the company had already begun to restructure.
Adding AI to an already manic sea of changes ended up being a blessing in disguise.
Would Intercom have moved as fast if it had been growing at full speed? Maybe not.
According to Des: “The mistake we would have made if the business was doing great and we saw this opportunity, would be underinvestment… We might have said, ‘Oh, that’s cool. Fergal has a fun little experiment. Let’s let him work on that.’”
The fact that the company was already mid-pivot meant one more radical change was easier to absorb. As Des puts it: never waste a good crisis. If the floorboards are already up, fix everything.
Building AI is not like building SaaS
In SaaS, development is deterministic. You talk to customers, you scope the work, you build it, you ship it, you move on.
AI doesn’t work like that. As Des says: “You don’t know what’s possible. And if something’s possible, you don’t know how reliable you can make it.”
The process Des describes looks nothing like traditional product development.
Before a designer or product manager is involved, you start with the model and test for what new capabilities are now possible. This might involve what Des calls a “torture test”. The AI team constructs hundreds or thousands of scenarios with a known correct answer, and run the model against them. You need to ensure it can complete the task reliably, at or above human quality.
Then you can move into productisation, and determine what infrastructure must be built around it for it to work in the real world.
And once it’s live, you’re not done. You run millions of real scenarios through the system, watch how customers configure it, measure how it performs in the wild, and build reporting and oversight tools so customers can see what’s happening in real time.
You should not chase models
It might be tempting to assume that the path to a better AI product is upgrading to the latest model, but Des pushes back hard on this. When a company announces it has switched to a new model two hours after launch, it means one of two things: they had extremely early access, which is possible but rare, or they have no evaluation criteria.
The AI team’s process for evaluating a new model runs thousands of scenarios and compares three things: what current Fin would say, what the candidate model would say, and what the ideal answer looks like – what Des calls “God’s own support agent.” Only when the new model is demonstrably closer to the ideal does it earn a place in production.
Additionally, most of Fin’s improvements haven’t come from model upgrades. They’ve come from better AI architecture, prompting, and ways to disambiguate. Fin runs across 25 subsystems, many of which now run on models post-trained internally for specific aspects of customer experience. Change one subsystem, and you have to re-evaluate the entire system – because there are trickle-up and trickle-down effects you won’t see otherwise.
That kind of rigour is where the real competitive advantage lives.
You have to know what “good” looks like
If you want to do AI properly, you have to take it seriously. And that means having a clear definition of what good looks like. Without one, Des asks, “What are your team doing? They’re just kind of performatively running around in circles.”
In customer support, good is relatively well-defined. There’s a correct answer to “how do I reset my password?” You can write it down, put it in the torture test, and benchmark off of it.
In other domains, it’s harder, but the discipline is the same. Des uses an example of a LinkedIn post generator. The only true measure of good is whether the post performs. You probably can’t see that directly, so you find a proxy: acceptance rate, how often users actually click post.
You then follow these posts and collect examples that performed well, and maybe some human-written examples. Over time, you build a spectrum from terrible to excellent, and you use it to calibrate your model.
It’s difficult but it’s non-negotiable. Every AI team needs to be able to answer: what does great look like, and are we getting closer to it? Without that, you’re not improving.
What founders should do right now
We’re four years into AI. Des’s advice to founders is to stop thinking in co-pilots and start treating it as a reinvention.
The first step: find someone you trust who is genuinely good at AI, and ask them what percentage of your product still requires a human in the near future. For most SaaS companies, the answer is going to be a lot smaller than expected.
According to Des, you then ask yourself: “If you were starting this product again today and you were good at AI, what would you build and where are humans, if anywhere, essential? And you would build a product that uses AI everywhere it can be made reliable… and boring old SaaS UI wherever you need humans involved. That’s your new product direction.”
Getting there will require painful decisions – roadmap cuts, unhappy customers, engineers resistant to new tools. But the alternative is the Blockbuster path: a string of rational-sounding reasons to delay, right up until it’s too late.
The time is now, as Des says: “You might have one or two years left.”
To hear the full conversation with Des, where he talks managing AI hallucinations, pricing AI products, and Fin’s future as a customer experience Agent, watch on YouTube or listen on Spotify.