Asked a friend, who works in a successful business and is heavily using AI for building things, why he would use a SaaS vs building it himself. This was his answer. Sure, you have to pick the product, so that's your liability, but that's why you pick SaaS with a good reputation.

i still don't get this position if you get an ai generated PR you can just... close it? have a review bot that automatically flags and closes slop PRs? or put instructions in agents.md that AI generated PRs will follow?

Serving a $DASH order through an AI agent costs $0.02 to $0.20 cents of compute, against $1.70 of contribution profit per order. The napkin math: Unit economics, from the Q2-26 10-Q: • Average order: ~$34 • Revenue: ~$4.60 (13.5% take) • Contribution profit: ~$1.70 (5.0% of order value) • Adj. EBITDA: ~$0.95 (2.8%) Everything below is measured against that $1.70 contribution profit. Moving forward, an agent doesn't make one call. It browses, compares and checks out, and every step re-reads its context. At market prices for frontier-class open models (~$1.30 per million input tokens, ~$0.10 cached), a session costs (est.): • Reorder "my usual", ~45k tokens: ~$0.02 • New discovery order, ~135k tokens: ~$0.06 That is 40x to 120x the compute behind a Google search. $GOOGL earns ~4¢ per search and spends ~0.05¢ of compute on it (est.), and its largest variable cost is the ~0.7¢ of traffic acquisition it pays Apple, Samsung and others. Advertisers pay per click, so they carry the searches that don't convert. An agent paid on a take rate carries a different equation. For DoorDash that means the failed sessions land on the completed orders. If 80% of reorder sessions convert, compute is ~2.4¢ per completed order, and if 30% of discovery sessions convert, it is ~20¢. Both conversion rates are my assumptions, not disclosures. Contribution profit per order: • App order: $1.70 • Agentic reorder: ~$1.68 • Agentic discovery: ~$1.50 Compute takes 1.4% of the margin on a reorder and 12% on discovery. Those two ratios are also the break-even. An agent order that would have happened in the app anyway carries the compute as pure added cost, so the agent pays for itself once 1.4% of its reorders, or 12% of its discovery orders, are orders DoorDash would not otherwise have had. What absorbs the compute is the take rate. The same $34 discovery order on a payments-style 0.3% fee would need 58% of sessions to convert to cover it, and at DoorDash's 13.5% it needs 1.3%. Merchants of record on marketplace takes ($DASH, $BKNG, $EXPE, $AMZN) can add an agent for cents per order, while a third-party agent on a thin fee is confined to repeat and big-ticket purchases until merchants can no longer refuse it. So the exposure sits on the revenue line. Ads reached a ~$1B annualized run-rate in 2025, about 7% of DoorDash's $13.7B of revenue, which is 26¢ to 33¢ of the $4.60 it earns per order (est.). They are sold in the app feed, and an agent order never opens the feed. The agent's costliest order burns 20¢ of compute, and the average order carries ~30¢ of ad revenue it doesn't show. But there's a scenario Doordash put sponsored options for the agents, ensuring those ads were read by the agent, delivering better ROAS for advertisers. The line to watch as agent orders scale is net revenue margin, 13.5% in Q2-26, since that is where displaced ads would show. I'm wrong on compute if contribution profit falls below 4.5% of order value with management citing AI serving costs.

Lunch, as it turns out, is strangely a very difficult problem to solve (exponentially harder as the number of teammates grows). It's been fun to see how different companies and teammates are using our CLI to make lunch an easier thing to do. Now we've built it into an MCP to get companies all things food and more!

Hey David, Gayani here from Figma. You're right that our remote MCP server only accepts clients on our supported list, and Pi isn't on it yet. You can see the current list in our MCP catalog at figma.com/mcp-catalog. If you'd like Pi considered for a future addition, please fill out this form: forms.gle/qSvUawwznWyoj8Go7.

Hi, Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan. Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago. (a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want. (b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions. (c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent. (d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet. I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news. Codexingly, Tibo