37 days, Florida. On November 3, we decide whether the next generation can afford to call our great state home. I’m running for governor to protect the Florida Dream, lower costs, and ensure our best days are still ahead. I will never take your vote for granted. I intend to earn it—and keep every promise I’ve made.

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?

37 games to choose from across the Big Five leagues this weekend. 💰 Lock in your picks before PM UTC tomorrow to take home your share of the $10,000 prize pool. Pick your sides and climb the leaderboard in our all-new weekly football competition. ⚽

37-year-old Tim Duncan full highlights vs. Miami Heat (103-100 L in overtime) - Game 6 of the 2013 NBA Finals: 30 Points on 13/21 FG (61.9%), 4/5 FT (80.0%), 17 Rebounds (5 Off. Rebs), 1 Steal, 2 turnovers, 2 PF, and a +/- of +16 (game high among both teams, YES), in 44:26 minutes played. He started the game 8-for-8 from the field and scored 25 points in the 1st half, accounting for half of his team's overall points. I wanted to post this because not many think of other great performances from this game, and Duncan was superb, especially considering his age and the fact that he was struggling for most of this series. Miami Heat vs. San Antonio Spurs - Game 6 of the 2013 NBA Finals - 6/18/2013 I also posted it on YouTube:

37 million years old whale spine found in the hot dunes of Egypt. This is a complete skeleton, the first-ever find for Basilosaurus, a large, predatory, prehistoric archaeocete whale uncovered in Wadi El Hitan, preserved with the remains of its prey.

37,000. That's how many civilians died needlessly in armed conflicts in 2025. Civilians are #NotATarget#. International humanitarian law requires all parties to armed conflict to protect civilians and civilian objects. Protecting civilians is not optional. It is a legal obligation. #PoCWeek2026#

37 airlines have now either installed or committed to installing SpaceX's @Starlink on their fleet: • Southwest Airlines • United Airlines • British Airways • Singapore Airlines • Emirates • Qatar Airlines • Air France • Hawaiian Airlines • Alaska Airlines • Virgin Atlantic • Lufthansa • Korean air • Air Baltic • Air Canada • Aer Lingus • Air Busan • Air Dolomiti • Air New Zealand • flydubai • Air Seoul • Asiana Airlines • Austrian Airlines • SWISS Air • Scandinavian Airlines • Gulf Air • Iberia • Discover Airlines • ITA Airways • Vueling • Brussels Airlines • Jin Air • LEVEL • WestJet • Edelweiss Air • JSX • ZipAir • Eurowings Pressure will continue to mount for the airlines that don't adopt Starlink, as they'll lose customers to airlines that have adopted it. I already have friends that actively seek out flights with Starlink because the speed is such a game-changer. As of a couple months ago, over 2,500 airplanes already have Starlink installed, according to SpaceX (includes private planes, business jets and commercial aircraft). That number is likely a decent bit higher now.

37 years ago this week, @Whitesnake earned its first and only No. 1 hit on the #Hot100# with its classic song “Here I Go Again.” #BBChartRewind# 🤘📈 Take a look at the top 10 that week, and swipe to see the full chart.

37 years ago this week, Whitney Houston earned her fourth of 11 career No. 1s on the #Hot100#, with her classic “I Wanna Dance With Somebody (Who Loves Me).” #BBChartRewind# 📈💃 It was also one of four No. 1s from her album ‘Whitney’ (along with “Didn’t We Almost Have It All,” “So Emotional” and “Where Do Broken Hearts Go”). It’s one of just nine albums in history to generate four No. 1 songs. Take a look at the top 10 that week, and swipe to see the full chart. ➡️