The 67-Second OpenTelemetry Problem
OpenTelemetry has an adoption problem we need to solve.
In 1950, a Formula One pit stop took 67 seconds. Mechanics wrestled with jacks and fuel cans while the driver sat still, powerless. That was normal.
By the 1980s, the best crews had cut it to under 10 seconds.
Then came 1993. Michael Schumacher’s Benetton team unveiled a radical refueling system. Normally, a pit stop meant losing ground. Schumacher went in second and came out first.
For the first time, the pit lane had decided the race.
Today, Red Bull can change four tires in 1.82 seconds. Faster than a blink (well, maybe two).
In a sport built on speed, the greatest edge came not from the car at all, but from reimagining what could happen in the garage.
OpenTelemetry adoption feels a lot like those 1950s pit stops.
Painfully slow, full of wasted motion, and accepted as normal. Most teams know the standard is inevitable, but they still find themselves stuck in the garage instead of back on track.
The problems fall into three patterns. Projects often drag on for a long time before the first trace appears. The concepts feel like a foreign language. And even when the data shows up, momentum has already drained away.
67 Seconds Pit Stops
Getting started with OpenTelemetry is notoriously slow. Projects are scoped in months, sometimes years. It feels less like a rollout and more like sitting in the pit lane while everyone else keeps racing.
Decision fatigue hits immediately. Do you try zero-instrumentation or wire everything in manually? Which propagator, which exporter, which SDK version? Every service, every language adds another fork in the road. The OpenTelemetry ecosystem offers dozens of choices before you write a single line of code.
Teams spend weeks just picking the right combination of components and only discover if they chose correctly much later, when production traffic is already flowing.
The work is upside down. Instead of shipping instrumentation, devs spend weeks learning concepts and debugging basics. Dozens of lines of YAML later, there’s still nothing to show for it.
Foreign Language
OpenTelemetry concepts seem simple on paper. Context propagation, span processors, exporters. Straightforward steps to collect and move data.
In reality, when you try to implement them, it's like learning a new language from scratch.
You need to understand the subtleties of each component, how they interact, and how to troubleshoot when things go sideways.
This learning curve creates a significant barrier to adoption. Only a few experts can navigate OpenTelemetry. The rest of us wait, guess, or sit in a dark room reading the docs.
Copy a snippet, pray to the observability gods, move on.
The frustration is compounded by the fact that every language stack has its unique quirks. A solution that works well in Java might not work in Go. Python requires a different approach altogether. Scattered documentation, examples that don't work in real life, and the same questions get asked online - "I got OpenTelemetry to work. But why was it so complicated?”
More time spent decoding the language of OTel than shipping value with it.
Telemetry that takes months to explain is telemetry nobody uses.
Out of Fuel
Most OpenTelemetry projects ignite with high hopes. Imagine a world with one standard - free from vendor lock-in, with a clear view of complex systems. The entire team rallies behind this vision at the outset.
But the spark doesn’t last. Early enthusiasm gives way to long debugging sessions and recurring setbacks. Critical decisions pile up like unpaid bills, and the rollout starts to feel less like progress and more like maintenance.
By the time data finally arrives, the thrill - as the great B.B. King would say - is gone.
The coverage is patchy, the instrumentation inconsistent, and the results underwhelm. The company that was energized at the start is now second-guessing whether the effort was worth it.
Winning the Race
Slow adoption doesn’t just waste time; it damages OpenTelemetry itself.
Once a rollout drags, people stop blaming the process and start blaming the standard. “We tried OTel and it didn’t work.” That reputation sticks (and sucks).
The result is a graveyard of half-instrumented services. Different SDK versions, configs nobody understands, dashboards nobody trusts. Some teams quietly fall back to vendor agents because at least those give them data.
The tragedy is that OTel isn’t broken - the way most companies adopt it is. But every failed attempt chips away at confidence, and adoption debt spreads fast.
Pit stops used to lose you the race. Today, they win it.
In 1950, what was supposed to keep the car in the race looked like wasted time in the garage. The teams that learned how to turn that dead time into an advantage were the ones pulling ahead.
OpenTelemetry is your pit stop - use it to win.
67 → 1.82 (this is marketing stuff now)
What if OpenTelemetry adoption looked more like Red Bull's pit crew? Automated, instant, light work, smooth learning curve. Lawrence provides developer tools to automatically install, maintain, and troubleshoot OpenTelemetry at scale. So you don't wait 67 seconds while others keep racing. Get into the private beta!