The hardest part of running a company like Eastman right now probably isn't deciding whether to invest in AI, automation, or the next generation of specialty materials. It's something quieter than that: knowing, decision by decision, which parts of the business are allowed to move faster and absorb more risk, and which parts can’t afford that luxury.
Get that judgment wrong in one direction, and the business moves too cautiously to keep up with where the market is headed. Get it wrong in the other direction, and eventually something breaks that can't be fixed after the fact. Most companies don't notice they've drifted toward one side until it costs them something.
Every large company has to decide which bets can be wrong for a while, and which ones can't be wrong even once. Eastman is running three real experiments right now, and each one puts that exact question to the test.
Molecular recycling only works if Eastman keeps pushing into unproven territory: expanding capacity, questioning old assumptions about what plastic waste is worth, doing it now that some of the government support the bet once counted on is less certain than it was. That is squarely "keep moving and find out" territory.
But plenty of what makes that bet valuable can't afford that same room for error. A batch of material with only a few hours to prove it's good before it becomes unusable. A shipment that leaves the plant carrying a certificate promising it already passed every check. A brand relationship that only holds if the recycled material really is what Eastman says it is, every single time. None of that gets a second attempt.
Climbing away from commodity chemicals toward specialty products tells the same story from another angle. The reason customers pay more for Eastman's materials instead of someone else's is trust built over years, not just chemistry. Trust like that is patient capital: easy to spend quickly, slow to rebuild. Keeping brands paying full price for it only works if what Eastman shows a customer today still matches what happens at the plant tomorrow, without gaps.
Some parts of a business can move fast and recover from a mistake. Other parts can't. The companies that navigate that tension well over the next decade won't be the ones who pick a side, caution or speed, and apply it everywhere. They'll be the ones who can push hard where the cost of being wrong is recoverable, and get things exactly right where it isn't, without ever confusing which is which.
That's a harder discipline than it sounds. The instinct under pressure is to pick one mode and use it on everything. Fast everywhere feels like momentum, until something expensive breaks. Careful everywhere feels responsible, until a competitor moves past while the checklist is still being worked through.
Here's a pattern I've watched repeat, account after account, across companies that have nothing else in common: the mistake almost never happens where everyone's watching. The most scrutiny tends to go to the newest, highest-profile bet, the thing with a name and a budget and executive attention, precisely because it's new and everyone's watching it.
Meanwhile the oldest, least visible systems, the ones nobody remembers investing in, sit quietly in the background because nothing about them asks for attention. And it's usually the old, unwatched thing that turns out to be the one that couldn't afford to be wrong.
Knowing which parts of the business can take risk and which can't is already hard. Despite what popular marketing says, AI makes it harder, not easier. It's tempting to treat AI as one more tool that makes everything faster, uniformly. But speed without a clear sense of where it's safe to be fast is just a more efficient way to make mistakes.
The organizations getting real value out of AI right now aren't the ones running it everywhere at once. They're the ones being deliberate about where they let it move loose and where they don't, and building enough underneath that distinction to make it hold up under pressure, not just on a good day.
Right now, more of Eastman's foundation is being asked to carry both kinds of weight at once than at any point in its history: the speed of new bets, and the precision the oldest and most sensitive parts of the business have always required, including, often, the parts nobody's looked at closely in years.
That's really where Cloudflare's role starts to make sense. Not another tool bolted onto the business, but the layer that makes it possible to draw that line cleanly, and keep drawing it correctly even in the parts of the business that stopped getting attention a long time ago. One place to decide what's allowed to move fast. One place to make sure what can't afford to be wrong, isn't, whether it's a new initiative everyone's watching or an old system nobody's thought about since it was built. Not a constraint on speed. The thing that makes speed anywhere safe to have at all.
None of this is a claim that anyone knows exactly how much of the next decade will be shaped by AI, or how permanent any of it turns out to be. Nobody does yet. But that uncertainty is exactly why the parts of Eastman that can't afford to be wrong deserve more attention now, not less, and why the parts that can move fast deserve the room to actually do it.
That's the conversation worth having, whenever it's useful.
Built Differently, Ready for What Nobody Saw Coming
For decades, security companies protected infrastructure and applications from the Internet by stacking boxes, appliances, gateways, and inspection tools around them. The assumption was simple: build the infrastructure first, layer security on top of it after. Traffic has to reach the edge of that infrastructure before anything even looks at it — which means the attacker is already close by the time anything gets stopped.
Cloudflare asked a different question: what if the Internet itself could be that security layer?
That question goes back to 2004, and a much smaller one: where does email spam actually come from? To answer it, the founders built Project Honey Pot — a distributed system that let any website owner plant tracking traps for spammers and malicious bots, mapping their behavior across the internet in real time. Over five years, thousands of websites in 185 countries joined. The dataset grew rapidly, and users kept pushing for more: don’t just track the bad guys, stop them.
That question is the origin story. And here’s what it actually took to answer it.
Lee Holloway didn’t build another standard web proxy. He built something the internet had never seen: a globally distributed reverse proxy layer, running the exact same software stack on every machine, everywhere, simultaneously. The physical infrastructure was unremarkable — commodity x86 servers, sitting in colocation facilities around the world, nothing exotic. The radical part was what the software was designed to do.
Instead of sending traffic to one system for caching, another for security, and another for routing, Lee built a single unified pipeline — a request arrives, gets parsed, hits security logic, gets routed, and gets served, all inside the same system, in the same pass. Combine that with anycast routing, where every Cloudflare location shares the same IP address and the internet automatically routes each user to the nearest one, and something remarkable falls out of it: any Cloudflare server, anywhere on Earth, can handle any request, for any customer.
Why didn’t everyone build it this way? Because it is brutally hard. It required writing high-performance networking code so security wouldn’t slow anything down. It required solving distributed systems problems most companies avoid entirely — pushing policy changes globally in seconds, keeping every location consistent, failing over gracefully when parts of the network go down. And it required walking away from the business model most networking companies were built on: selling high-margin hardware appliances. Cloudflare’s entire bet was that if you own the network, you don’t need to sell boxes. They didn’t build services on top of a proxy — they built a network.
And the numbers now prove the bet paid off. Roughly a quarter of the world’s Internet traffic runs through this network today. It’s built to sit within 50 milliseconds of nearly every connected person on Earth — more than 337 cities, over 125 countries — with AI inference running from over 210 of those locations, offering access to more than 350 different models, and 80% of the top 50 generative AI companies running on Cloudflare.
But the numbers were never the real story. The architecture is. Because that same architecture — one pipeline, already in the path, before the request reaches anything important — is exactly why Zero Trust makes sense, why API security makes sense, why AI security makes sense. Not tacked on later. Already there.
Which brings us to why this matters right now more than ever. AI agents are not like traditional software. Traditional software runs in predictable locations, on predictable schedules, talking to known endpoints. AI agents are autonomous — they make decisions, call APIs, spin up processes, talk to other agents, constantly, globally, simultaneously, at a scale that was unthinkable five years ago. That kind of traffic needs infrastructure that is globally distributed, low-latency, secure by default, and instantly available — no servers to provision, no regions to choose.
Cloudflare has been building exactly that infrastructure for fifteen years, without knowing AI agents would ever need it. That foundation was not built for AI. But building for the hardest problems on the internet — global scale, millisecond latency, consistent security everywhere, no boxes — turns out to be exactly what AI needs.
Cloudflare didn’t predict AI. They just built the right network for it. And AI arrived.
