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Fastly Blog

Fastly Fastly Fastly Fastly Fastly Fastly Fastly Six Common Live Streaming Mistakes (And How to Avoid Them) How Fastly and Skyfire Enable Trusted Agentic Commerce at the Edge AI Traffic Grew 6.5x Faster Than Human Traffic This Year Python SDK Beta: How the Language of AI Runs Faster and Safer with Fastly Give AI Agents the Markdown They Actually Want How to Configure Local Logging for an On-Prem Next-Gen WAF Agent Accountability Without Control Is Breaking Security Leadership Fastly Joins the Agentic AI Foundation (AAIF) to Guide Edge AI Interoperability The E-commerce Industry in the AI Era: Has the Agentic Flood Hit? No Margin for Error: What the FIFA World Cup Teaches Us About Performance at the Edge Why iGaming Infrastructure is Breaking and What Comes Next The Publishing Industry in the AI Era: Why Bot Strategy is Now a Business Strategy Bad Performance Kills SaaS/PaaS Growth — Why Your CDN Matters Why your code is safe from Copy Fail on Fastly Compute Myth or Marvel: Claude Mythos and What it Means for Security Introducing Compliance Audit Reports Supporting Google Private AI Compute with Privacy-Preserving Edge Infrastructure Fastly Nearly Half the Web Isn’t Human: Inside Fastly’s Threat Insight Report Media over QUIC: Can Streaming Finally Have Both Scale and Low Latency? Introducing Fastly’s Redesigned Homepage: Your Central Hub for Actionable Insights The False Choice of Indiscriminate Blocking: Why Technical Precision is the New Standard for an Open Internet What is CVE-2026-23869? React Server Components Security Alert Fastly enables first-party tagging for Google Advertisers Shrink Your Bill With Efficient Software Your AI coding agent just got better at Fastly Fastly Ranked as a Leader in the 2026 Forrester Wave™ for Edge Development Platforms Fastly at RSAC 2026: New Advances in AppSec, Bot Management, and Deception Mastering the Edge: What Golf Can Teach Us About Speed, Precision, and Performance Real-Time CDN Monitoring for Live Events with Bronto Imperva Alternatives Fastly + Scalepost: Extending the Fastly platform to manage AI Crawlers Best content delivery networks for bot management Vibe Shift? Senior Developers Ship nearly 2.5x more AI Code than Junior Counterparts Maximizing Compute Performance with Log Explorer & Insights Fastly CDN Expands Scaling Fastly Network: Balancing Requests | Fastly Best Practices for Multi-CDN Implementations | Fastly Compute@Edge: Serverless Insights by Company | Fastly Fastly can teach you about the Wasm future in just 6 talks Fastly's Observability Unleashed: New Updates and Insights Optimizing your multi-CDN infrastructure to improve performance Stay ahead of attackers by pushing your security perimeter to the edge Are APIs the Key to Digital Innovation or a Trojan Horse? Fastly Academy: on-demand learning at your fingertips. | Fastly 30 Years of Web: Building for Tomorrow 4 Ways Legacy WAF Fails to Protect Your Apps Adobe boosts performance and MTTR with Epsagon and Fastly logs | Fastly Beta" A New Serverless Compute Environment Early TLS at Fastly Technical trainings & the future of edge delivery at Altitude 2016: a year in review Innovation Capacity Defined: Tech Stack Values | Fastly Deep Log Visibility Offered by Logentries | Fastly Caching the Uncacheable: CSRF Security Increase Your Hit Ratio With This Simple Tip
Bot Defense is Table Stakes. Machine Traffic Requires a Business Strategy
Joan Jenkins · 2026-06-09 · via Fastly Blog

As my colleague Hossein Lotfi recently shared in his deep dive into our latest network data, AI requests on our platform have grown roughly 6.5x faster than human traffic. With autonomous machine-to-machine traffic now approaching half of all internet requests, businesses face an entirely new landscape.

As AI traffic grows in importance, businesses need to decide which AI interactions create value, which create risk, and how to act on both. Stopping bad bots still matters but is no longer enough. Without a clear strategy, managing this machine traffic is a fundamental business challenge. Most companies can’t reliably tell the difference between what traffic should be stopped and what traffic adds value.

Blocking Is Only Half The Strategy

Most companies are not ready for this. They can see automated traffic increasing. They can feel the pressure on applications, APIs, and infrastructure. They know AI systems are accessing their content, but they often do not know which ones, why they are there, what they are doing, or whether that activity creates value.

Is blocking every bot the answer? It’s tempting when faced with a flood of unknown requests. Some automated traffic should absolutely be stopped: malicious bots, credential stuffing attacks, aggressive scraping, inventory hoarding, policy-violating crawlers, and abusive automation create real risk.

But blocking by default is not the same as having a complete strategy. Treating every automated request as the same kind of threat collapses nuance into a single answer. It may reduce some risk, but it can also block future customers, reduce distribution, degrade user experiences, and prevent you from understanding how AI systems interact with your digital properties.

Two Companies, Two Different Responses to AI Traffic

The debate around AI traffic is often framed as allow versus block. The decisions companies make can have a big impact on business.

Take the two large companies represented in the charts below as real-world examples: One saw a spike in fetcher traffic and instituted a policy to start blocking them, most likely to maintain content authority. The second has intentionally not blocked them and as a result saw increased fetcher volume over multiple months.

The companies that simply block AI traffic may feel safer in the short term. But they may also make themselves invisible in this new version of the internet. So what should you do?

The Edge Is Where Decisions Happen

Human and machine traffic now share the same infrastructure, but they behave completely differently. While the edge was already the real-time decision layer for top-tier digital companies, this shift accelerates that reality for everyone else. Because it sits in the path of every request, the edge is where you must inspect traffic, enforce policy, protect applications, accelerate delivery, manage origin access, and act before requests create cost, risk, or latency.

To make this work, you need clarity. You have to see which AI systems are accessing your digital properties, understand their behavior patterns, and separate useful automation from unwanted automation. With a solid strategy, you don’t have to let every machine in or keep every machine out. The goal is to respond to each request based on its exact intent and impact.

Building a Machine Traffic Strategy

Every business needs a strategy based on its goals and priorities. What works for a travel platform might not work for a publisher. The companies adapting most successfully focus on three key areas: visibility, context, and precision.

Gain visibility into exactly which machines are interacting with your sites, applications, APIs, and content.

Build context about what those systems are accessing, how often they return, whether they respect policies, and whether they create business value.

Apply precision in how you respond. Depending on the machine’s intent, you might choose to accelerate it, challenge it, redirect it elsewhere, or even rewrite the response on the fly to give that specific bot tailored data.

We help our customers execute that machine traffic strategy at the edge. In the path of every request, Fastly delivers the visibility, context, and precision required to balance performance, security, bot management, and origin access with real-time intelligence. We help organizations distinguish beneficial automation from unwanted activity.

The internet is no longer just for humans. Some machine traffic creates risk and some creates value. The future belongs to those who can see the difference and act on it.

Forward-looking statements

These articles contain “forward-looking” statements that are based on Fastly’s beliefs and assumptions and on information currently available to Fastly on the date of these articles. Forward-looking statements may involve known and unknown risks, uncertainties, and other factors that may cause its actual results, performance, or achievements to be materially different from those expressed or implied by the forward-looking statements. These statements include, but are not limited to, those regarding expectations regarding the future growth, velocity, and composition of AI and automated traffic; the adoption rates and scale of agentic workloads and AI assistants; the behavioral patterns of AI crawlers and fetchers; the impacts of automated requests on web infrastructure; and the performance, capabilities, and expectations regarding customer experiences with Fastly’s products and services, including Fastly Bot Management. Except as required by law, Fastly assumes no obligation to update these forward-looking statements publicly, or to update the reasons actual results could differ materially from those anticipated in the forward-looking statements, even if new information becomes available in the future. Important factors that could cause our actual results to differ materially are detailed from time to time in the reports Fastly files with the Securities and Exchange Commission (“SEC”), including in our Annual Report on Form 10-K for the fiscal year ended December 31, 2025 and our Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on Fastly’s website and are available from Fastly without charge.