Rob Wiblin’s 80,000 Hours team has a goal of identifying the skill bottlenecks keeping us from solving the world’s most pressing problems. Yesterday he sent out an important question to the universe via Twitter.

“When a situation is super complicated and the analysis very rushed, is that a time to listen to experts more, or actually a time to listen to them less? Might be more accurate to say, do we need a different kind of expertise?”

Wiblin is of course referring to the only thing that matters at the moment; the Covid-19 epidemic. And his question is towering over all of us right now.

There’s no denying it. We’re not great at pandemic response or preparation in America. And though it’s certainly cathartic to energize political debates as to why, politics won’t make us better. And we need to be.

This is where Wiblin’s point becomes so important. We have mountains of medical experts, public health officials, legislators, and economists working the problem. I’ve been stuck in my house watching them between conference calls and design sessions for work. And something is startlingly clear.

I don’t think we have all the right kinds of experts.

This isn’t a criticism of the people working the issue. It’s a reckoning that modern technology and medicine hasn’t intersected the way other industries have. And so we aren’t very effective at ingesting data at scale, identifying patterns and creating feedback loops. These are the blocking and tackling functions of modern technology enabled capabilities. And we don’t really do much of it in pandemic response. Not relative to how much we do of it in other less important domains. We’re better at suggesting the next porn video than we are telling you how many ICU beds Chicago will need this month.

That feels like a problem to solve.

As tempting as it is to just say we need SiliconValley to weigh in here and fix it, I’m not sure we want that either though. Along that path we’re just as likely to get an opt in mobile app that allows us to turn our medical information over to a corporate enterprise that no one will trust as we are a more robust capability.

Apple and Google did that yesterday by the way. It’s a start. But it’s not exactly what we need. We need more.

Long before the terms machine learning, artificial intelligence and big data were broadly used as advertising buzzwords, I was in Iraq leading an all source intelligence team with much more archaic tools. While we were there, we piloted a new technology from a group no one had heard of. It was a new “intel” software. In actuality it was a pretty basic data aggregation and distribution suite.

At the time, we didn’t have any systemic content management system for our intelligence reports. We had no enterprise search function. We weren’t using anything at all that resembled the metadata or tagging that common applications like WordPress or even Facebook and Twitter use today. Shockingly, there was no curated single source of truth for the data.

The intelligence analyst next to you could be chasing the same target you were and you wouldn’t know it until the evening in person intel brief.

As crazy as it sounds to us now, at the time, we were actually pretty successful at our core job. Like the experts fighting the pandemic, the processes we’d built up over what was already a “long” war had been optimized. We had deep subject matter experts in areas in which we operated. We knew what we were doing. We just didn’t know what we weren’t doing. And how much better we could get by doing it. So when I briefed the brass on the technology suite we piloted and suggested we were in the technological stone age and needed something like it, I was brushed off.

I still remember the look on the senior intel officers face when I told him the Family Tree application on Ancestry.com was the best intel tool in the world. And that how they digitized, stored, tagged and used metadata of old documents to enable it was as close to what the needed future looked like to us as we were going to get.

He actually giggled at me.

So did the exec for the company that built the software suite we piloted when I approached them for a job after I got out of the service a year later. They wanted software engineers, data scientists and UX designers. Not Naval Officers. They were building technology. Not capability.

The point of that war story is Wiblin’s point. At the beginning of the intersection of technology and counter-terrorism, he could have asked the same question he did yesterday. We had the old experts on both sides of the people and technology divide. After technology created capability and then application created organizations and processes, we built new experts.

Today’s counter terrorism experts are steely eyed data and computer warriors. And they’re enormously effective. That company that dropped off that laptop on my desk in Iraq was Palantir. They’re going to clear a billion dollars in revenue this year and are deeply imbedded in a robust cybersecurity, counterterrorism, technology industrial complex.

They never did show any interest in me. So I went to another tech firm and built out a technology based work from home capability for fintech enabled financial experts. We were bad at that at first too. Until we learned how to use technology and data and processes to drive outcomes. And now we aren’t. And now it seems to matter a bunch.

Which brings us back to our current predicament. The intersection of technology, data and medical care should have intersected decades ago. It didn’t; not at the scale it could have and for reasons that could fill a few books. That intersection in the beginning would not have created magical capability any more than dropping a laptop with a software suite on my desk in Ramadi did. But it would have started something that, over time, would have gotten better.

Perhaps that intersection can happen at scale now. And the norms of medical care we valued in the past may wear away in the face of the clear and present danger of pandemic. And then we’ll get better. Just like we’ve gotten better at digitally enabled counter terrorism. Just like we’ve gotten better at baseball scouting. Just like we’ve gotten better at targeted advertising. Just like we’ve gotten better at electric cars.

Each of these processes of building out expertise started with a burning need. We approached it with modern technology. And we built processes and organizations that turned that technology into capability. And then we built experts in that capability.

Wiblin’s right. We need that new kind of expert now.

Be wary of people to tell you that you can’t challenge a doctor on things not specific to medical care. But also be wary of the Silicon Valley tech guru who simply “has an app” to solve that problem too.We need both right now. And we’ll need the expertise that comes from that intersection.

That’s where the goodness begins.

Here’s to hoping this gets the train started. My guess is, you’d get some pretty smart people to climb on if it looked a little different than it does now.