AI could upend food delivery

DoorDash's report that it processed 970 million orders in Q2 and generated $4.5 billion in revenue highlights the massive scale at which AI‑driven logistics can operate, while the 10‑person startup Bites, with only about 300 Bay Area restaurants, illustrates the early‑stage competition trying to leverage AI agents for food‑delivery optimization. For website owners in the restaurant and delivery niche, this surge means AI bots will increasingly scrape menus, pricing, and availability data to feed predictive ordering engines. The disparity between DoorDash’s volume and Bites’ modest footprint signals that larger platforms will deploy sophisticated crawlers to index every restaurant listing, potentially inflating traffic spikes and skewing analytics. Moreover, AI‑powered recommendation engines may begin to query your site’s API or public pages in real time, demanding stricter bot management to protect inventory data and prevent rate‑limit breaches. **Actionable tips:** 1. Update your llms.txt to explicitly allow DoorDash’s known crawler user‑agents (e.g., `DoorDashBot/2.0`) while denying unknown AI agents, using the `Disallow` directive for generic “AI‑Agent” patterns. 2. Implement rate‑limiting rules in your server firewall that trigger when a single IP exceeds 200 requests per minute, a threshold typical of AI‑driven scrapers, and log these events for llms.txt audit. 3. Use llms‑central’s bot‑tracking dashboard to monitor spikes from the `bites.ai` user‑agent and set up automated alerts to adjust your robots.txt or llms.txt entries before the traffic overwhelms your infrastructure.