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Sales tax is the unsexiest problem in ecommerce. It is also one of the ones that bites hardest as you scale. We've had clients come to us not collecting sales tax in states where they've had nexus, with millions of dollars in liabilities. Forget ambulance-chaser lawyers for ADA compliance and the California Invasion of Privacy Act; the last thing you want is State tax collectors breathing down your neck... they don't just go away. We've outsourced this problem for our clients to Numeral, who just raised a $100M Series C. The moment a brand starts growing, it owes compliance in every state where it crosses nexus. Registrations, filings, remittance, exemption certificates, plus physical mail from dozens of departments of revenue. Miss one filing and you eat penalties and interest. No founder or finance team should spend time on that in ecommerce. Numeral is the done-for-you solution. Registrations, filings, mail handling, product classification, handled end-to-end. Congrats to the entire Numeral team! Not having to think about sales tax ever again is a blessing for any e-commerce brand, and this latest round underscores that.

37 mistakes companies make with AI transformation: 1) Not investing in your data foundation/not having a data “clean-up” strategy. Often people expect that with tools, everything gets solved. 2) Starting with “we need AI” instead of a real problem (this is true for every tech cycle ever). 3) Underresourced AI center of excellence that serves every part of the organization. Backlog builds up, employees get disenfranchised, shadow AI explodes. 4) Trying to automate the same workflow vs rethinking from scratch. Building AI add-ons to existing processes rather than rethinking processes from the ground up. 5) Thinking too big and flashy. Not considering the implications day-to-day and the value of quick, unsexy wins. 6) Over-engineering. Sometimes you dont need a full agentic system and traditional software works just fine. 7) Obsessing over cost before proving feasibility of a use case (i.e using a smaller model first before validating technical feasibility with larger models). 8) Encouraging/pushing employees to use AI without real depth. Widespread rollout with limited education/lack of training for employees. 9) Telling your people that AI won’t impact jobs. 10) Overprotecting data + spend to the point of limited experimentation from your workforce. IT/Security blocking this or slow rolling it out (which is fair but bad for the speed in which this is moving). Culture doesn’t encourage AI use. 11) Not having places to go to ask questions / knowledge share. Whether that be a skills library, shared repo, or internal AI office hours. 12) Failing to solve the last mile. Everyone’s so focused on models, but successful applied AI is a complex last mile problem: governance, data, observability, context management, people, process, etc. 13) Shipping it and call it done. Lack of discipline to go beyond the shiny demo and ensure sustained adoption that meaningfully empowers teams. 14) Slop is tolerated. 15) No governed way to build for non-technical people. No Citizen SDLC to empower SMEs to build and share production apps. 16) Assuming AI transformation is the responsibility of one person within the org. 17) Run like an IT project. No senior exec actually owns injecting AI across the business, therefore initiatives stall and leave no lasting impact. There is no clear owner. 18) CEO is not a driving force. Leadership enforcement without the leaders actually knowing how or what to enforce. 19) Not getting the buy in of the “bad guys.” Bring Legal, Finance, and IT along for the ride early. 20) Not investing in / underestimating change management. Easy to get the folks who are excited on board, but it's a long process to make others feel comfortable. 21) Not measuring baselines before any adoption. What are the metrics pre-AI tool to post AI tool? No baseline = no roi story, and thinking that all AI usage is positive ROI without measuring usage/tying it to real outcomes fails the same way. 22) Inventing new KPIs for AI instead of focusing on having AI accelerate existing functional KPIs. 23) Reducing AI to headcount and being overly stringent on ROI too early into programs. 24) Being driven by FOMO and not having the patience to treat AI transformation as the multi-year migration it actually is. 25) Being married to past purchasing mistakes and not choosing the best technology at the moment. 26) Not anticipating the complexity of getting systems to work nicely together (a kind of scope creep as the reality blows up work required). 27) Not being agile enough to change course when the landscape changes drastically. 28) Locking in to a single provider ecosystem. 29) Not providing employees access to the underlying systems needed to make AI useful to take action, not just chat. 30) Underestimating how much of an impact AI can actually have. It is both a cooler and scarier time than ever before to be an incumbent. 31) Outsourcing thinking to AI - everyone can prompt, the differentiation is how you wield the tool to multiply the work you're doing. If you have good judgement you can do a lot more. If you don't, you end up wasting a lot of tokens spinning your wheels. 32) One functional department thinking they should own AI transformation. It treats AI as a vertical solution vs. horizontal capability that’s more than just technology. 33) Executing on AI initiatives before anchoring your work in a clear strategy that’s tied to business goals, a map of key processes, understanding of your technology and data reality, and clarity around how to meet your people where they are. 34) Not solving data permissioning and RBAC considerations before rolling out agentic tools firmwide. 35) Not giving people dedicated time to experiment or carving out time in their roles for it. 36) Not understanding how a business function ACTUALLY works before trying to apply AI. In someone’s head, the process for generating some end state dashboard is simple: systems generate the data, it gets consistently transformed and warehoused, then read into the dashboard that the VP sees. In reality, it’s a complete mess. 37) Neglecting internal evals to constantly test and evaluate how new models/harnesses perform company tasks on a $ per successful task basis. What's missing?

I asked @jmj the least glamorous thing he’s done in his career: ”I ran out of delivery drivers in Kansas City, so I flew there myself and delivered every bouquet of flowers myself. I was doing growth, and we did this Valentine's Day campaign where we were subsidizing the cost. You could buy flowers and get them hand delivered to your door for like $30. It's basically marketing and we charge a nominal amount. We launched this in San Francisco, New York, and Kansas City. I found people to deliver the flowers in San Francisco and New York. And then realized I don't have enough people in Kansas City. So I flew there, and I was driving through Kansas and Missouri, and I just delivered all the flowers myself. The idea was, I just learned how to do things that were very unsexy and to have fun with it. That moment I was like, this is awesome. I'm in Kansas driving flowers to people. They're all happy, and I'm listening to good music. I see a lot of employees at venture firms and companies who just wanna go straight to the top. And I just remember, man, I was delivering flowers. You have to do some of that really grindy grunt work. Back to the founders and companies who will win, I think they all do a certain amount of grindy work and have fun with it."