Before Signing That SaaS Renewal

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The signals

• Ankit: Clients are buying back 30 percent of the service and doing the rest themselves. His clients started telling him they love the package, but they can get about 30 percent of it now from software they already pay for, and their own team can absorb the other 70. He is deliberate about not arguing the point: leave aside whether it is a good idea or how well they do it, their impression is that it is good enough. The consequence is not a lost account, it is a repriced one. He had to come down roughly 30 percent to stay competitive, which meant cutting overhead, which is what sent him looking at his own subscriptions. The company is around 50 to 60 people and was bigger this time last year. He is the first guest on this show whose signal cost him money in the same quarter he noticed it: "it definitely caught my pocketbook".
• Sam: A green run is not proof of useful verification. Six repositories with scheduled jobs that keep their contents current, each running a cascade that checks Perplexity, validates against X, then goes to Claude, so the models check each other's work and Sam only resolves the questions. On the Friday of Labor Day weekend one output was visibly wrong, and three days of checking by hand followed, back to the legislative sources. The failure mode was not a crash. Certain research failures came back empty, everything logged the same way, and as long as one leg of the cascade returned anything the run reported success. Empty is not an argument, but it counts as an answer. Ankit had hit the identical thing months earlier: "it returned nothing and that counts because it did something." The redesign is per-stage: catch an empty return at any point in the chain, and catch the model bluffing further down the stack.

The subtractions

• Ankit: HubSpot, and then the habit of buying. The trigger was refusing a year-long contract at roughly $1,800 to $1,900 a month for a tool they were using at 20 or 30 percent. He asked AI what it would look like to open-source the whole platform, got five candidates, vetted them, and pointed it at his own AWS: Twenty CRM and Chatwoot, company SSO on the front so nobody manages passwords. Built in three or four days, fully transitioned in two weeks against a planned two months, and the help desk got faster rather than worse. Roughly $2,300 to $2,500 a month, about a tenth of the SaaS budget, and the pattern kept going into a shared inbox and an open-source virtual office for a remote team. His defence of the vendor is the sharpest line in it: HubSpot is a Swiss Army knife when what you need is a scalpel. And his own caveat is the load-bearing one: the speed came from building blocks that already existed, the CLIs, the keys and tokens done properly, the hosting. You cannot go zero to sixty on a bumpy road.
• Sam: The decision system that made decisions without him. Nine months of building a decision layer into his own second brain, where pre-made decisions sat as lit fuses: unless he acted, the default fired, including letting things go that had not been touched in two weeks. It worked until a family emergency took him away for a week and the cascade started firing on decisions he had pre-approved under circumstances that no longer held. There was no safety mechanism, because he had not built one. He spent the day before the show defusing every fuse and is back to roughly April levels. David Allen's line is the one that fits: build a system that tells you exactly what to do at two o'clock on Tuesday, and by two o'clock on Tuesday you will want to do something else. Rebuilding trust is going the way trust is built with people, from the bottom up and in small partitions, not from the top down. Ankit's response named it: top-down is not trust, it is authority.

About this episode

The two subtractions run in opposite directions and that is what makes the episode. Ankit removed a vendor and it worked, because the substrate underneath was already his. Sam removed his own automation, because he had built it past the point where he could see whether it was working. Both are the same instruction: look at what is actually running, not at what is supposed to be.

The other thread worth keeping from the tape is where Ankit puts the line between code and models. His first question about any task is whether it is deterministic, or can be mapped until it is; if it can, it is code, every time. AI writes the program, it does not run the business. The worked example is vision insurance, where a prescription crossed with plan tiers produces hundreds of thousands of possible combinations and used to cost twenty minutes of a person's arithmetic per patient. AI learns each new plan and writes the mapping script; the pricing itself is deterministic code. Five to ten minutes saved per transaction, and more to the point, mistakes prevented, which is the kind of thing that kills a small business. He does not use AI for patient information or clinical decisions.

Also on the tape: he gave everyone AI, took it all away, and rebuilt around learning theory instead, chunking concepts and turning them into steps, which is how a 17-step blog-writing process came to exist. His closing hypothesis is that as models improve, the harnesses improve faster, and the scarce skill becomes understanding the concepts well enough to break them down, because the average answer is free now and the value is in the niches around it. Lean, he points out, is not really about removing waste. It is about creating value.

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