The internet has always had its bots. From the earliest days of the web, we’ve lived with search engine crawlers like Googlebot and Bingbot indexing our pages, building the maps that make the web usable. There’s been an unspoken social contract: in exchange for letting bots crawl your content, you get discoverability and inbound traffic.
But there’s a new breed of bot on the loose — AI crawlers. Instead of driving traffic back, they hoover up content to feed large language models. They don’t index, they ingest. And unlike search engines, they don’t promise you visitors in return; they promise to answer queries themselves.
That tension is coming to a head. The Internet Engineering Task Force (IETF) is now drafting standards that would let websites tell the difference between traditional search bots and AI bots — and choose which ones to allow or block. On the surface, this looks like a technical distinction. In reality, it’s about the future of the open web, the economics of data, and the role platform engineers will play in shaping it.
Why the IETF Standard Matters
The IETF doesn’t make headlines often, but when it does, it matters. This is the body that helped define HTTP, TLS, and countless other protocols we take for granted. When they start drafting standards for bot identification, we should all pay attention.
The proposal is simple: give site owners a way to distinguish “search” crawlers from “AI” crawlers. With that line drawn, a website could, for example, allow Googlebot to index its content but deny OpenAI’s GPTBot or Anthropic’s Claude crawler.
That’s not just about bandwidth or traffic management. It’s about controlling access to the raw material of AI — data. If standardized, this could fundamentally reshape how models are trained and how data flows across the web.
The Politics and Power Struggle
Make no mistake: there’s a lot of politics in this push.
Search engines have long enjoyed a privileged role. Google indexes your site, but in exchange, it sends you traffic. That traffic fuels the web’s business model — ads, subscriptions, commerce.
AI bots break this model. They scrape content to generate answers directly. When you ask ChatGPT a question, it doesn’t send you to my site or yours; it synthesizes an answer and keeps the user in its walled garden. For publishers, media companies, and anyone relying on inbound traffic, that’s not a fair trade.
The stakes are high, and the battle lines are forming. Google sits on both sides of the fence — a search giant and an AI player. Microsoft and OpenAI are pushing AI forward aggressively. Smaller AI startups worry that if data access closes, only the giants will have the scale and licensing budgets to compete.
This isn’t just a technical standards debate; it’s an economic and cultural one.
The Platform Engineering Perspective
So why should platform engineers care? Because when the IETF draws the lines, it will be platform teams who have to implement them.
Here’s what’s at stake for platform engineering:
- APIs and rate limiting. Distinguishing between AI bots and search bots means new detection, tagging, and enforcement mechanisms. Platform teams will be the ones tuning these controls.
- Data governance. If your organization produces content or APIs, platform engineers will help decide which bots are allowed in, which are blocked, and under what conditions.
- Observability. Tracking bot traffic at scale is non-trivial. Platform teams will need to add new telemetry, metrics, and logging to understand what crawlers are doing.
- Security. Not all crawlers identify themselves honestly. Malicious scrapers may pose as legitimate AI bots. Protecting systems means more sophisticated defenses at the platform layer.
This isn’t a future problem — it’s today’s ticket in many engineering backlogs.
Implications for AI Development
If this standard takes hold and websites widely block AI bots, the era of free, open scraping may end. That would force a shift:
- From open web scraping → to negotiated data licensing deals.
- From a long tail of accessible content → to silos of walled gardens.
And that has consequences. Big tech players with the resources to ink licensing deals (Google, Microsoft, OpenAI) will keep training at scale. Smaller players, startups, and open-source AI projects may find themselves locked out.
That risks reinforcing an AI oligopoly — where only the biggest can afford the data that fuels innovation. Ironically, a standard meant to protect the web might end up centralizing AI power even further.
Shimmy’s Take
This debate feels familiar. It echoes the early battles of the internet: open vs. gated, free vs. paid, access vs. control.
I get it. Creators and publishers deserve protection and compensation. No one wants to see their content scraped, stripped of context, and monetized by someone else’s AI without credit or payment. But we also have to be honest: AI needs data to grow, and overly restrictive rules could choke innovation.
Here’s where platform engineering comes in. Platform teams sit right at the choke points of data access. They’ll be the ones deciding what flows in and out, how it’s tracked, and how it’s enforced. They’ll be asked to balance openness with protection, innovation with governance.
The danger is going too far in either direction. Too much restriction, and we risk a fragmented, innovation-stifled AI landscape. Too little restriction, and we risk exploitation and erosion of trust. Navigating that middle ground will be hard — but it’s where the future lies.
Closing Call-to-Action
So here’s my question: Is your organization ready for the AI bot wave?
Because whether you run a media company, a SaaS platform, or an open-source community, AI crawlers are already knocking at your door. And when the IETF standards solidify, you’ll need to decide: who do you let in, and who do you keep out?
Platform leaders, this isn’t just a debate for lawyers and policymakers. It’s a call to arms for engineers. The knobs and levers of enforcement will live in your platforms. Your decisions will shape not just your company’s strategy, but the future of the web and AI itself.
The negotiations aren’t abstract anymore. They’re happening now — in standards bodies, in boardrooms, and yes, in your engineering backlogs. The AI era isn’t coming. It’s here. And platform engineering is on the front lines.