The Experts Aren’t the Problem. It’s How You’re Listening to Them

It’s become increasingly common for people to justify wacky ideas—ranging from Holocaust denial to miracle cures—by claiming that they are doing their own research. Originally coined by conspiracy theorist Bill Cooper in the 1990s, the phrase has become a rallying call for those who seek alternatives to mainstream expertise.

Yet doing your own research is much harder than it would first appear. As Leonard Read pointed out in his classic essay, I, Pencil, even an object as simple as a pencil requires expertise in everything from mining, chemistry, logistics and myriad other things to produce it. It is far too complex for any single person to do it by themselves.

Pencils are far less complicated than most things we have to make decisions about. Anybody can, of course, access papers in scientific journals and see what they have to say, but unless you’re an expert you will be unable to evaluate the validity of the materials and methods used to carry out the research. To benefit from expertise, you need to learn how to listen critically.

Data Divorced From Context 

The Internet has democratized information. Today, a teenager with a smartphone can retrieve more data in seconds than a specialist at a large institution a generation ago could in a week. With just a few clicks we can learn about everything from how to fix a bike to the science of mRNA vaccines and the thoughts of philosophers from centuries ago.

Yet information and expertise are two very different things. For example, when Russia first invaded Ukraine in 2014, many outlets reported that the country was evenly divided between Ukrainian and Russian speaking regions. That fact, while technically accurate, is also tremendously misleading about the country and its culture.

Here’s how Timothy Snyder, an expert on the region who speaks Russian and Ukrainian fluently, explained it at the time:

Ukraine is a bilingual country. Electoral posters are in both languages. Candidates switch from one language to another on political talk shows. The giant banners on government buildings that read “One Country” are in both languages. If you watch a soccer game on television you might notice that the man doing the play-by-play speaks Ukrainian while the man doing color speaks Russian: almost all Ukrainians understand both and most speak both. If you go to a coffee shop you might find a polite waitress who adjusts to the language she thinks you speak best. No country in Europe is more cosmopolitan than Ukraine in this respect.

Those are things you would have to spend time in the country to experience. You would have to study the country’s history to understand that its national identity goes back more than a thousand years, while Russia is much younger and less grounded. In fact, Russia was essentially a vassal state of the Mongols until Ivan the Terrible rebranded it in 1547.

When I lived in Ukraine, it was customary to speak Russian in the office and Ukrainian at home and with friends. After the full-scale invasion in 2022, many people who first met each other at work and spoke Russian with each other, switched to Ukrainian without missing a beat. Anyone “doing their own research” by looking at how people answered surveys before the war would miss all of this.

Evaluating Experts

Technology experts tend to make bold predictions about the potential impact of artificial intelligence. In an interview with Axios, Anthropic CEO Dario Amodei, suggested that AI could boost economic growth to 10%. Researchers working with Boston Consulting Group found that consultants could increase their productivity by more than 40%.

Yet economists see it differently. A study by the St. Louis Fed suggests a 1.1% increase in aggregate worker productivity, with much of that increase concentrated in the tech sector.  A paper by Nobel laureate Daron Acemoglu, which analyzes total factor productivity (TFP), a measure which includes the use of capital, sees a 0.66% increase over 10 years, translating to a 0.064% increase in annual TFP growth.

That’s an enormous gap. How could experts differ so starkly? The answer lies in the nature of their expertise. Technologists are amazed at how AI can enhance performance on specific tasks like coding. Yet those who study the broader economy look at how much impact those tasks have on society as a whole, and come away far less impressed.

This type of category error is more common than you would think. For many years, Ukraine was considered to be a submarket of Russia, so when issues about Ukraine arose it was Russia experts who were consulted, often leading to wildly inaccurate assessments. Expertise in one area rarely translates neatly to another.

So it’s important to parse expertise and ask, “What precisely are judgments based on?”

Ecosystems Of Knowledge And The 30 Year Rule

In 1882 Thomas Edison opened Pearl Street Station, the first commercial electrical distribution plant in the United States. Yet as the economist Paul David explained in a landmark paper, electricity didn’t have a measurable impact on the economy until the early 1920’s — 40 years after Edison’s first plant was built. To truly impact productivity, factories needed to be redesigned and work itself had to be reimagined.

The internal combustion engine followed a similar path. In the age of the corner store, the automobile had limited economic effect. It wasn’t until production and distribution systems evolved—through innovations like the supermarket, the shopping mall, and the category killer model—that cars began to transform the economy in a meaningful way.

The reality is that it typically takes about 30 years for a technology to go from an initial breakthrough to a measurable impact on the economy. Douglas Engelbart first showed the potential of personal computers in his Mother of All Demos, but it wasn’t until the late 1990’s, or about 30 years later, that computers had a measurable effect on the US economy.

Despite all the hype, things haven’t changed much since then. Today’s cutting-edge technologies—blockchain, CRISPR, quantum computing, artificial intelligence—all had their initial breakthroughs over a decade ago. None has had a measurable economic effect. Even mRNA vaccines, which owe their impact an unprecedented effort during the Covid pandemic, had their first clinical trials in 2001.

The problem is that to make a significant impact, you need to get experts who deeply understand the technological solutions to work effectively with experts who deeply understand the real-world problems to be solved.  That’s not a technology problem. That’s a very human problem and it takes time to solve.

Integrating Expertise

One of the best innovation stories I’ve ever heard came from a senior executive at a leading tech firm. Apparently, his company had won a million-dollar contract to design a sensor that could detect pollutants at very small concentrations underwater. So the firm set up a team of crack chip designers and they got to work.

Shortly after, the team’s marine biologist walked in and casually dropped a bag of clams on the table. Noticing the stunned looks around the room, he explained that clams are incredibly sensitive to pollutants—able to detect contaminants at just a few parts per million—and respond by opening their shells. So instead of developing an expensive sensor, all they needed was a basic system to detect when the clams opened.

“They saved $999,000,” the executive told me, “and had the clams for dinner.”

The story gets to the core of the challenge of listening to experts. If you only listened to the chip designers, you would devote far too many resources and come up with a less optimal solution. If you only relied on marine biologists, you would never be able to design even simple chips. To solve meaningful problems, you need to integrate insights from multiple domains. Innovation isn’t just about technical talent, it’s about creating the space for the dialogue across disciplines.

So when we listen to experts, we need to apply a critical lens and ask: What’s the nature of their expertise? If you listen to technology experts, for example, you will come away with a better understanding of what a technology like artificial intelligence is capable of. If you listen to economists, they will give you more realistic context about its potential impact on society.

Yet if you examine both with a critical eye, a new story emerges. AI’s productivity on some tasks, such as coding, is already transformational. For others, such as personal services, it has been negligible. That may not predict what the future will be, but it will give you some actionable insight on where to focus your efforts most productively.

Understanding the world isn’t about “doing your own research,” but listening to experts critically and integrating the accumulated knowledge from multiple domains.

Greg Satell is Co-Founder of ChangeOS, a transformation & change advisory, an international keynote speaker, host of the Changemaker Mindset podcast, bestselling author of Cascades: How to Create a Movement that Drives Transformational Change and Mapping Innovation, as well as over 50 articles in Harvard Business Review. You can learn more about Greg on his website, GregSatell.com, follow him on Twitter @DigitalTonto, his YouTube Channel and connect on LinkedIn.

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