Addendum to "Knowledge Is Important: Skills are Essential"

This addendum expands upon the distinction between explicit knowledge and action-oriented skills (”craft”), offering commentary on a report by Michael Kratsios (Science. A New Golden Age).

I was so impressed by Michael Kratsios’s Report to the President of the United States on science funding that I want readers of my earlier Substack publication to know about it. Moreover, Kratsios talks about craft (that I called skill) in ways that strengthen my earlier statement.

I first describe some of the issues that gave rise to the report by Kratsios: traditional scientific grant funding and conservative peer-review processes. I point out, in agreement with Kratsios, that they often penalize imaginative, high-risk research and burden scientists with excessive paperwork compared to hands-off funding models like DARPA.

Most of this article is a long quotation from Section IV.1: Section IV is titled “Ensuring That Science and Technology Better the Lives of All Americans,” and Section 4.1, which I quote in its entirety, is labeled “The Marriage of Science and Craft.” The point is to highlight that technological and scientific advancement relies not merely on explicit instructions, patents, or machinery, but on “process knowledge”—tacit, uncodifiable expertise that exists within experienced practitioners and can only be transmitted through direct personal experience and mentorship.

In my previous post, “Knowledge Is Important: Skills are Essential,” I distinguished between knowledge and action, pointing out that action often requires skills that are difficult to teach: they have to be learned through doing, through trial-and-error. The knowledge required to do a skill is ineffable, that is, non-describable.

This posting is simply to show all of my readers this interesting support from Michael Kratsios, Assistant to the President for Science and Technology and Director, Office of Science and Technology Policy, The Office of the President of the United States (see Kratsios, 2026, in the references).

I find the entire report refreshing: sensible in its recommendations for changing how science is done and, more importantly, how it is funded and credit is given. Today, it can take years to secure funding from government agencies (e.g., NIH and NSF). Moreover, the peer review by these panels tends to be very conservative. Unusual applications are turned down as “too risky.” Mind you, distinguished scientists do these reviews, experts in the field (which is part of the problem: When an expert sees something that violates expectations, they are extremely skeptical and have a strong tendency to be dismissive). This is a subconscious bias.

I have served on some of these committees, and everyone works hard, listens to different opinions, and does their very best to distribute the limited funds to the committee’s consensus on the very best proposals. This is made even more difficult because the number of truly excellent proposals exceeds the available budget. Quite often, it is difficult to decide whether to fund a promising young investigator or continue funding a seasoned professional with many major contributions. When I was on these committees, I accepted those rules, marveling at how much time and effort very senior scientists spent reviewing and discussing the proposals. Everyone was trying hard to do the best possible job. I slowly realized that the bias of highly experienced scientists was to be skeptical of new, apparently outlandish ideas: new, imaginative work. (I was able to reverse a few decisions, but not always. Of course, I might have been wrong.)

Worse, the paperwork required to submit and review these proposals was enormous, taking time away from research. And if an excellent laboratory that had done good work over the years had a slump of poor results, they would not get funded, which meant that excellent work was stopped, and the people were often dispersed. One result of this is that senior laboratory directors are seldom in their labs. They are on committees or hiding, writing grant proposals.

In earlier years, when I got money from ARPA, before it was renamed to add the word “Defense” as the first letter, Defense Advanced Research Projects Agency (Congress was unwilling to support basic research unless it was for the Defense Department), ARPA allowed project managers full authority in selecting promising groups to fund. If the group had some bad years, the program manager would visit to understand what had happened, and unless there was evidence that the group was falling apart, would continue the support. It was OK: No group produces excellent work every year of existence.

The ARPA/DARPA funding model won’t work in today’s era of science, but Kratsios was clearly inspired by the history of their significant impact on scientific (and technological) progress, which informs many of the arguments for this new report. It contains truly excellent suggestions, some of which many of us have been asking for for a long time.

But, I digress. I want to show a section that amplifies my discussion of the difficulty of automating action, which I also called “skills” and Kratsios labels “craft.” I found his section about the difference between knowing and doing (Knowledge and Action), so well done that I have posted it here so you can see it. (This is only a small section of the report. I recommend the entire report.)

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Section IV.1: The Marriage of Science and Craft

In policy conversations, we often speak as though technology consists solely of intellectual property and gadgets. We focus on the patents that can be filed, the knowledge that can be written down, or the complex machines that can be built. But scientific and technological capability consists of much more than its most visible inputs and outputs.

A better taxonomy holds that technology exists in three forms: tools, explicit instructions, and process knowledge.¹²² Consider chipmaking. The tools are the lithography machines, etchers, implanters, and more. The explicit instructions are the blueprints and recipes. But the process knowledge, like how to troubleshoot semiconductor yields, how complex variables affect wafer cleaning, how the next process node should be designed to balance performance and manufacturing risk, lives in the heads of experienced engineers and technicians.

This tacit knowledge cannot be fully codified. Anyone can be placed in front of a piano bench with a score of Rachmaninoff, but playing it well requires a personal command of musical dynamics and tactile skill.

As the chemist-turned-philosopher Michael Polanyi observed, “we can know more than we can tell.”¹²³ A skilled welder knows things about metal behavior that no manual captures, like the way aluminum warns you before it warps, or the sound a good bead makes as it forms. A machinist develops intuitions about cutting tools that come only from years at the lathe. A pharmaceutical manufacturing technician recognizes subtle variations in chemical processes that determine whether a drug batch meets specifications.

This process knowledge, embodied in an experienced workforce, is the true keystone of technological capability.

The same applies to the practice of science. When the sociologist Harry Collins studied laboratories attempting to replicate a new type of laser in the 1970s, he found that no scientist succeeded using published sources alone. Those who built working devices had all spent time in a laboratory with someone who had already done it. The knowledge to build the laser flowed through personal contact, often so subtle that scientists themselves could not fully articulate what they had learned.¹²⁴

Likewise, a synthetic biologist improves through countless failed experiments while coaxing cells into expressing a novel protein. An immunologist, after years of experience and guidance from senior mentors, develops intuitions for which protocols will work with finicky cell lines, knowledge that no methods section can capture. This is why many forms of scientific expertise require years of on-the-job training in working research organizations, and why academic publications alone remain insufficient to transmit the craft of science.

Papers and patents are not the ultimate ends of progress, but way stations in the training of better scientists, engineers, and technicians. Science is not simply about the equipment, which any laboratory with enough capital can purchase, nor the instructions, which can be shared on a sheet of paper.¹²⁵ Our true competitive advantage lies in the process knowledge embodied by America’s talent. Without skilled practitioners who pass their craft to those who follow, the engine stalls.

Except for the first reference, the references are ordered by their footnote numbers in the document.

Michael Kratsios, Science. A New Golden Age. A Report to the President. Office of Science and Technology Policy. July 2026. (The quoted section is at https://www.whitehouse.gov/science/#chapter-4-the-marriage-of-science-and-craft )

122a.Dan Wang, “How Technology Grows (a Restatement of Definite Optimism),” blog, July 24, 2018, https://danwang.co/how-technology-grows.

122b. Dan Wang, Breakneck: China’s Quest to Engineer the Future (W. W. Norton & Company, 2025).

123. Michael Polanyi, The Tacit Dimension (Doubleday, 1966).

124.Harry Collins, Changing Order: Replication and Induction in Scientific Practice (University of Chicago Press, 1992).

125.Wang, “How Technology Grows.”