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Physical Organic Chemistry: Alive or Dead?

January 5, 2024

In Wednesday’s post, I wrote that “traditional physical organic chemistry is barely practiced today,” which attracted some controversy on X. Here are some responses:

(There are plenty more responses; if I didn’t list yours, sorry!)

For the most part, I agree with these responses. Physical organic thinking has permeated organic chemistry and adjacent fields: George Whitesides has probably the best piece on this topic, in which he argues that the essence of physical organic chemistry is “a general, and remarkably versatile, method for tackling complex problems,” not anything about chemistry per se, and consequently that the physical organic mindset can be applied to problems in all manner of fields. Viewed from this angle, we might say that physical organic chemistry hasn’t disappeared at all—instead, it’s become so commonplace that we forget to acknowledge it as distinctive at all.

Looking through the organic chemistry curriculum, too, suggests that physical organic chemistry is here to stay. Lots of the ideas that we teach to undergraduates, like molecular orbital theory and structure–activity relationships, were once distinctively the domain of physical organic chemists. Textbooks from before the apotheosis of physical organic chemistry (I have an old copy of Fieser & Fieser, for instance) are structured in a completely different way, not by mechanism but by functional group, while today many undergraduate organic classes discuss SN1/SN2 mechanisms in their first semester.

So, was I entirely wrong to claim that traditional physical organic chemistry is a dying art? I don’t think so. Despite all the successes of physical organic chemistry, it seems to me that something has been lost between the time of the norbornyl cation controversy and today. The sorts of elegant kinetic experimentation and argumentation that Winstein and others employed in their papers are now rare: take, for instance, this famous paper distinguishing between contact ion pairs and solvent-separated ion pairs. How many scientists today still do experiments like this? There are certainly names that come to mind, but from where I sit it seems to be an increasingly niche skillset.

I don’t want to fall into the trap of idolizing the past for no reason; there are plenty of techniques which have been forgotten by chemistry because there are better ways of doing the same thing today. Chemists used to estimate molecular weight by dissolving a known mass of sample and measuring the boiling point elevation induced. Now we have mass spectrometry, so nobody uses the boiling point method any more, and I don’t see this as a great tragedy.

But kinetics, and more generally the sort of careful physical organic chemistry practiced by participants in the norbornyl cation debate, doesn’t seem to have such a simple replacement. Computation is the most obvious candidate, but we’re still a long way away from being able to predict mechanisms accurately in silico; in mechanistic chemistry, experiments still reign supreme. Kinetic isotope effects are much easier to measure than they were back in Winstein’s day, but they’re hardly routine experiments (and easy to get wrong). The rigor and precision with which old-school physical organic chemistry approached mechanistic problems can still be found today, but it seems harder and harder to find.

It might have been inevitable that physical organic chemistry was always going to evolve away from incredibly detailed studies of simple reactions on simple molecules—just as biology has largely shifted from ecology and taxonomy to cell biology and biochemistry, organic chemistry too must change in order to keep working on the most interesting problems. And perhaps there's some truth to the argument that the old-school style of painstaking mechanistic study just isn't worth the effort and deserves to be de-emphasized. But it does seem to me that parts of the tradition of physical organic knowledge (to borrow Samo Burja’s phrasing) is being slowly lost to time, despite the fact that lots of really good physical organic chemistry is still being done today on all sorts of problems (enzymatic chemistry, organometallic chemistry, catalysis, heterogenous catalysis, chemical biology, &c), and that makes me sad.

Looking Back on the Norbornyl Cation

January 3, 2024

In this post, I’m trying something new and embedding calculations on Rowan alongside the text. You can view the structures and energies right in the page, or you can follow a link and view the full data in a new tab. While PDFs and printed journals are limited to displaying 2D renditions of 3D structures, there’s no reason why websites should follow suit—and now that all my calculations are already on the web, it’s simple to share the primary data.

The 2-norbornyl cation has a special place in the history of physical organic chemistry. In 1949, following up on previous work by Christopher Wilson, the great physical organic chemist Saul Winstein observed that acetolysis of exo-norbornyl sulfonates occurred about 350 times faster than solvolysis of the corresponding endo compounds.

“X” represents a leaving group and “Nu” a nucleophile.

Several stereochemical observations indicated that something puzzling was going on: both the exo and endo sulfonates gave exo acetate product, but enantioenriched exo-norbornyl sulfonate formed racemic exo-norbornyl acetate. Winstein argued that this data was best explained through the participation of an achiral nonclassical carbocation (“II”) featuring σ-delocalization and a three-center two-electron bond, as shown in the conclusion of the 1949 paper:

The nonclassical structure, “II” above, is a little tough to visualize as drawn. Here’s the computed structure at the B3LYP-D3BJ/6-31G(d) level of theory, which should be a bit clearer. You can click on atoms to see bond distances, angles, and dihedrals; notice that the C1–C2 bond above (C14–C18 in Rowan) is markedly shorter than a normal C–C bond, whereas the C1–C6 and C2–C6 bonds (C13–C14 and C14–C18 in Rowan) are quite long.

In the 1960s Winstein’s interpretation was challenged by another preeminent chemist, H.C. Brown, who argued that the data could adequately be explained by rapidly equilibrating classical carbocations. Brown suggested that most of the observations made by Winstein could be explained simply by the differing steric profiles of the exo and endo faces of the norbornyl cation: the endo face is more shielded, and so ionization is slowed (explaining the 350:1 exo/endo rates) and attack is disfavored (explaining why both isomers of sulfonate give exo product).

This began an incredibly contentious series of debates which dragged on for decades. Rather than attempt to wade through the resulting sea of publications, I’ll quote from an excellent 1983 review by Cheves Walling to give a sense for the magnitude of the controversy:

The debate [over the structure of the norbornyl cation] was vigorously pursued verbally in lectures, meetings, and seminars all over the U.S. and even abroad…. No one has ever counted the number of publications touching on the 2-norbornyl cation problem, but they include a number of reviews, chapters, and books, and a typcial [sic] research paper may well include references to over 100 others.

Walling’s review goes on to give an excellent overview of the various pieces of evidence employed by both sides of the debate, which I won’t summarize in full here.

The most important data was obtained by George Olah and co-workers, who pioneered the use of superacidic media to generate stable solutions of carbocations which could be characterized spectroscopically. With Martin Saunders and others, Olah employed 1H and 13C NMR spectroscopy, IR spectroscopy, Raman spectroscopy, and core electron spectroscopy to study low-temperature solutions of norbornyl cations: in all cases, the data supported Winstein’s proposed symmetric structure. (While equilibration occurring faster than the spectroscopic timescale could not be ruled out by Olah’s work, spectroscopic measurements all the way down to 5 K showed no detectable classical structures, indicating that any barrier to interconversion must be <0.2 kcal/mol.)

13C NMR spectra at –159 ºC, showing the equivalence of C1 and C2. At –80 ºC, rapid Wagner–Meerwein rearrangements render C1, C2, and C6 equivalent. Data taken from Olah's Nobel lecture. (This is a good use case for carbon NMR!)
Carbon 1s photoelectron spectrum of tert-butyl carbocation (left), showing a characteristic carbenium peak on the left, and the analogous spectrum for the norbornyl cation showing the absence of the carbenium peak. Data taken from Olah's Nobel lecture.

Note: On Twitter/X, Dan Singleton argues that the controversy was largely settled by 1982 and attributes this to the Saunders/Olah NMR experiments and Cyril Grob's work in this area, which I didn't mention. I appreciate the correction and welcome any further additions to the record.

Computational chemistry, which became able to tackle problems like this in the late 1980s and early 1990s, also supported the nonclassical structure of the norbornyl cation. A 1990 paper used HF/6-31G(d) calculations in Gaussian 86 to show that the symmetric structure was a minimum on the potential energy surface. Here’s a scan I ran at the B3LYP-D3BJ/6-31G(d) level of theory, showing that the energy increases as the “classical” C–C bond forms:

(This iframe doesn't work well on the phone - still a work in progress, sorry.)

Subsequent work has confirmed that Winstein was almost completely correct about the key issues. Most notably, a 2013 crystal structure from Karsten Meyer demonstrates that the norbornyl cation is indeed nonclassical in the ground state, leading Chemistry World to declare the mystery solved. Nevertheless, there’s still a little room for a classical cation supporter to doubt this result: crystal structures are snapshots of solid-state atomic configurations, while reactions occur in solution, where molecules are free to move around more. (In the Chemistry World article, Paul Schleyer predicts that Brown himself would have raised this objection.)

A paper from Ken Houk and co-workers, published a few days ago in JOC, addresses this issue by directly modeling the solvolysis process through ab initio molecular dynamics with explicit acetic acid solvent. In the solvolysis of the exo sulfonate, the authors find the nonclassical cation is formed on average within 9 femtoseconds of C–O bond cleavage, which is about as quickly as is physically possible. Once formed, the cation is entirely nonclassical: “classical 2-norbornyl cations are a negligible component of norbornyl cations in solution," thus addressing the last objection of classical cation partisans.

Simulations of the norbornyl cation in explicit acetic acid show complete nonclassical behavior (Figure 6 from Houk’s paper).

In contrast, Houk et al find that the endo sulfonate doesn’t form the nonclassical cation until about 81 fs after the C–O bond breaks, explaining the slower reaction rate: the transition state isn’t stabilized by σ-dissociation, and so is higher in energy. This is a nice example of the principle of nonperfect synchronization, which is explained concisely in this presentation.


* * *

What can modern scientists learn from the norbornyl cation controversy, besides the object-level fact that carbocations can exhibit nonclassical σ-delocalization?

1. Reality is confusing, and convincing arguments are often wrong.

It’s a good exercise to go back and read the early H.C. Brown papers in this area, like this account. Brown was an incredible scientist (the 1979 Nobel laureate in chemistry), and his data and reasoning are quite good; I find myself sympathizing with his viewpoint while reading his papers. Nevertheless, with the benefit of hindsight we know that he was wrong and Winstein was right. “Humility comes before honor.”

2. Tools drive scientific progress.

The argument over the norbornyl cation was ultimately settled only by the development of new techniques, like superacid chemistry, core electron spectroscopy, and high-level calculations. Now that we have these methods, it’s much easier to solve similar problems: if Winstein’s paper came out today, I doubt it would take more than a year or two to figure everything out.

This aligns nicely with what Freeman Dyson calls a “Galisonian” view of scientific progress, where scientific progress is driven not by ideas (the “Kuhnian” view) but by new tools and new data. In chemistry, at least, the tools-first view seems true to me—since 1950, it’s difficult to think of a development more important to organic chemistry than NMR spectroscopy, with flash column chromatography probably taking second place.

3. It’s easy for fields to get distracted by controversy and lose relevance.

Here’s Walling again:

Since a significant fraction of the efforts of physical organic chemists was drawn into the problem [of the norbornyl cation], an unhappy consequence was a feeling on the part of many (including some of those concerned with the distribution of research funds) that physical organic chemistry was in danger of withdrawing into a world of its own.

As older scientists have explained to me, the norbornyl cation debacle scared a generation of chemists away from physical organic chemistry. The entire subfield became obsessed with a niche and somewhat irrelevant issue, while scientists in adjacent subfields looked on with bemusement and frustration. As a consequence, traditional physical organic chemistry is barely practiced today: few scientists have the skill or knowledge to conduct kinetic studies like those performed by Winstein, Brown, and others, and those still working in the area struggle to get funding or recognition (in the words of Dan Singleton, “sucks when you have to peddle your papers in cemeteries”).

(In a nice historical perspective, Stephen Weininger argues the norbornyl cation debate was “a hook on which to hang a much larger agenda,” fueled both by a UK/US divide and a deeper dispute about whether valence bond representations or molecular orbital representations of chemical structures were superior. If this is true, it was a self-defeating exercise by all involved.)

Scientists should be motivated by a search for truth, but also by the desire to improve the world. Usually these two aims go together: basic research without obvious societal implications often leads to unexpected and important findings, which is why the government supports science in the first place. But it’s possible to become so myopically focused on a single issue in the name of truth that one forgets about other goals, as arguably happened in the norbornyl cation imbroglio.

Controversy attracts attention: we’re drawn to it, against our better judgment, like moths to a flame. We have to be careful not to get captured by disputes that are, in the long run, not worth the effort.

Update 1/5/2024: some followup thoughts based on feedback from X.

Thanks to Eric Jacobsen for many conversations about the history of physical organic chemistry, Eugene Kwan for conversations about the principle of nonperfect synchronization, and to Ari Wagen for feedback on this post and six months of excellent front-end development for Rowan. Any errors are mine alone.

Books from 2023

December 31, 2023

(Previously: 2022)

#1. Tony Fadell, Build
#2. Giff Constable, Talking To Humans
#3. Ben Horowitz, The Hard Thing About Doing Hard Things
#4. Dale Carnegie, How To Win Friends And Influence People

Sounds Machiavellian, but actually quite wholesome: a “dad book,” as my friend called it.

#5. Ben Patrick, Knee Ability Zero.
#6. Neal Stephenson, The Diamond Age

Snow Crash was much worse upon rereading as an adult, but The Diamond Age was a bit better: in particular, I didn’t really appreciate the “speculative governance futurism”/”comparative cultural criticism” facets of the novel when I read this in high school.

#7. Richard Hamming, The Art of Doing Science and Engineering

I reviewed this here.

#8-11. Brandon Sanderson, The Stormlight Archives

Many great works of literature are notable for their brevity: when you read Hemingway, or Dubliners, or Flannery O’Connor, you know that every sentence has been crafted with care. Giant fantasy novels like Wheel of Time (which I read last year) or The Stormlight Archives work differently. There are entire chapters which are probably extraneous, whole characters and plot arcs which exist merely to bring out certain traits or pieces of information.

But there are unique joys to megafiction: sitting down and reading hundreds of pages of a good story is relaxing in a way that other books simply aren’t. In my own life, I’ve found that I’m much better about making time to read when I’m in the middle of an engaging novel than when I’m reading theology or histories of feudalism. Narrative-driven “easy reading” has a bad reputation amongst the literati; in a world where all fiction is competing against screens for engagement, it shouldn’t.

#12. Antonio Garcia Martínez, Chaos Monkeys

I reviewed this here.

#13. Tom Holland, Rubicon
#14. Tom Holland, Dynasty
#15. Czeslaw Milocz, The Captive Mind

Fantastic; I probably would have liked this even more if I were still in school.

#16. C.S. Lewis, That Hideous Strength

I didn’t like this when I was a kid, but I like it now: in many respects THS can be viewed as a book-length exploration of the ideas in “The Inner Circle,” with a garnish of medieval cosmology here and there (see also).

#17. Kazuo Ishiguro, Klara and the Sun
#18. Mike Cosper, Recapturing the Wonder
#19. Mairtin O Caidhan, Graveyard Clay

Tyler Cowen recommended this book, but I didn’t love it.

#20. Ursula K. LeGuin, The Dispossessed
#21. Marty Cagan, Inspired
#22. Ernst Junger, On the Marble Cliffs

This was excellent (h/t Santi Ruiz).

#23. Michaeleen Doucleff, Hunt, Gather Parent

I reviewed this book here.

#24. David Kirkpatrick, The Facebook Effect
#25. Bill Carr & Colin Bryar, Working Backwards

I reviewed this book here.

#26. Geoffrey Chaucer, The Canterbury Tales

The best book I read this year by a mile; far better than I remembered. While many of The Canterbury Tales work pretty well as literature, they’re even better when viewed also as history. It’s rare to be able to read something from 800 years ago that’s legitimately funny and interesting.

Reading Chaucer fills me with questions about the medieval mind. The stories are steeped in Christianity, as one might expect. Any argument goes back to the Bible, even those among animals, and Chaucer assumes a level of familiarity with e.g. the Psalms far exceeding that of most modern Christians. Yet at the same time the Greco-Roman world looms large: Roman gods appear as plot characters in three tales (the Knight’s Tale, the Merchant’s Tale, and the Manciple’s Tale), and Seneca is viewed as a moral authority on par with Scripture. I’m curious how all these beliefs and ideas fit together and welcome any recommendations on this subject. (The Discarded Image is already on my list.)

#27. Gabrielle Zevin, Tomorrow and Tomorrow and Tomorrow

My wife recommended this book to me. I thought this would be a relaxing break from the November startup grind, but in fact it features a bunch of obsessed programmers working around the clock for months—a poor choice but a good novel.

#28. Jessica Livingston, Founders at Work
#29. Neal Stephenson, Seveneves

Overall, about a third of the books I read were startup-related: most of them were pretty bad, but even bad business/startup books are probably useful from the viewpoint of cultural immersion. Academic science is quite different from the startup ecosystem, and to the extent that cultural arbitrage is possible (in either direction), I need to become proficient in startup culture.

I’m not sure why the median business book is so bad—perhaps business people are too willing to spend money on books and not picky enough, or perhaps MBA types generally lack knowledge about the humanities, which makes both supply and demand worse.

(The median Christian book is also pretty bad. One unifying hypothesis: both pastors and businesspeople often have wise insights into specific situations, personal or business, but these insights aren’t readily generalizable into book form. Being able to give good advice doesn’t mean you should write an “advice book.”)

As always, book recommendations are welcome, particularly on the topics of medieval history/culture, software engineering, or startups. Apologies for the infrequent posting as of late, and happy new year!

Bitter Lessons in Chemistry

December 1, 2023

“And I took the little scroll from the hand of the angel and ate it. It was sweet as honey in my mouth, but when I had eaten it my stomach was made bitter.”

–Revelation 10:10

As machine learning becomes more and more important to chemistry, it’s worth reflecting on Richard Sutton’s 2019 blog post about the “bitter lesson.” In this now-famous post, Sutton argues that “the biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.” This might sound obvious, but it’s not:

[The bitter lesson] is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach. (emphasis added)

How might the bitter lesson be relevant in chemistry? One example is computer-assisted retrosynthesis in organic synthesis, i.e. figuring out how to make a given target from commercial starting materials. This task was first attempted by Corey’s LHASA program (1, 2), and more recently has been addressed by Bartosz Gryzbowski’s Synthia. Despite considerable successes, both efforts have operated through manual encoding of human chemical intuition. If Sutton is to be believed, we should be pessimistic about the scalability and viability of such approaches relative to pure search-based alternatives in the coming years.

Another example is machine-learned force fields (like ANI or NequIP). While one could argue that equivariant neural networks like e3nn aren’t so much incorporating domain-specific knowledge as exploiting relevant symmetries (much like convolutional neural networks exploit translational symmetry in images), there’s been a movement in recent years to combine chemistry-specific forces (e.g. long-range Coulombic forces) with neural networks: Parkhill’s TensorMol did this back in 2018, and more recently Dral, Piquemal, and Levitt & Fain (among others) have published on this as well. While I’m no expert in this area, the bitter lesson suggests that we should be skeptical about the long-term viability of efforts, and instead just throw more data at chemistry-agnostic models.

A key assumption of the bitter lesson is that “over a slightly longer time than a typical research project, massively more computation inevitably becomes available.” This idea has also been discussed by Andrej Karpathy, who reproduced Yann LeCun’s landmark 1989 backpropagation paper last year using state-of-the-art techniques and reflected on how the field has progressed since then. In particular, Karpathy discussed how the last three decades of progress in ML can help us envision what the next three decades might look like:

Suppose that the lessons of this exercise remain invariant in time. What does that imply about deep learning of 2022? What would a time traveler from 2055 think about the performance of current networks?

If we take this seriously, we might expect that chemical ML will not be able to advance much farther without bigger datasets and bigger models. Today, experimental datasets rarely exceed 104–105 data points, and even computational datasets typically comprise 107 data points or fewer—compare this to the ~1013 tokens reportedly used to train GPT-4! It’s not obvious how to get experimental datasets that are this large. HTE and robotics will help, but five orders of magnitude is a big ask. Even all of Reaxys doesn’t get you to 108, poor data quality notwithstanding. (It’s probably not a coincidence that DNA-encoded libraries, which can actually have hundreds of millions of data points, also pair nicely with ML: I’ve written about this before.)

In contrast, computation permits the predictable generation of high-quality datasets. If Sutton is right about the inevitable availability of “massively more computation,” then we can expect it to become easier and easier to run hitherto expensive calculations like DFT in parallel to generate huge datasets, and to enable more and more downstream applications like chemical machine learning. With the right infrastructure (like Rowan, hopefully), it should be possible to turn computer time into high-quality chemical data with almost no non-financial scaling limit: we’re certainly not going to run out of molecules.

The advent of big data, though, heralds the decline of academic relevance. Julian Togelius and Georgios Yannakakis wrote about this earlier this year in a piece on “survival strategies for depressed AI academics,” which discusses the fact that “the gap between the amount of compute available to ordinary researchers and the amount available to stay competitive is growing every year.” For instance, GPT-4 reportedly cost c. $60 million to train, far surpassing any academic group’s budget. Togelius and Yannakakis provide a lot of potential solutions, some sarcastic (“give up”) and others quite constructive—for instance, lots of interpretability work (like this) is done on toy models that don’t require GPT-4 levels of training. Even the most hopeful scenarios they present, however, still leave academics with a rather circumscribed role in the ML ecosystem.

The present academic era of chemical machine learning can thus be taken as a sign of the field’s immaturity. When will chemical ML reach a Suttonian era where scaling is paramount and smaller efforts become increasingly futile? I’m not sure, but my guess is that it will happen when (and only when) there are clear commercial incentives for developing sophisticated models, enabling companies to sink massive amounts of capital into training and development. This clearly hasn’t happened yet, but it also might not be as far away as it seems (cf.). It’s an interesting time to be a computational chemist…

Book Review: Working Backwards

November 25, 2023

I took a pistol course in undergrad, and while I was a poor marksman I enjoyed the experience. In particular, I was surprised by how meditative the act of shooting was. As our instructor explained, much of good shooting comes down to not doing anything when you pull the trigger. When you’re not firing, it’s easy to point a gun at a target and line up the sights, but as you pull the trigger you subconsciously anticipate the noise and movement of the pistol blast, which makes you flinch and pull the gun off-target. Being a good shooter thus requires consciously learning to counteract what your instincts tell you to do.

If you believe Bill Carr and Colin Bryar’s book on Amazon, Working Backwards, Amazon’s success can be understood in similar terms. According to Carr and Bryar, Amazon alone among the West Coast zaibatsu has succeeded not because of some big technical or social insight (Google Search, Windows) but because of a long series of canny business decisions. Bezos has said something similar: “Amazon doesn't have one big advantage, so we have to braid a rope out of many small advantages.” The implication is that you too can build an Amazon-quality firm; you don’t need any flashes of mad genius, just the ability to eke out small advantages through savvy management.

(This might seem like an insane claim, but it’s worth noting that Amazon has indeed launched a ton of successful and loosely coupled businesses: in addition to their core commerce business, there’s AWS, Amazon Robotics, Kindle, Prime Video, Fire TV, and a bunch of other stuff. Contrast this to Google’s recent track record…)

What’s more, Carr and Bryar go on to argue that Amazon’s business acumen is driven not by some inscrutable Bezos magic but by adherence to a simple set of principles. And these principles aren’t esoteric or Amazon-specific—almost any business can follow them. The reason so few businesses have copied Amazon’s success is simply because each principle defies human nature in some way. Just like pistol shooting requires one to unlearn one’s instincts and pull the trigger without moving any other muscles, being an “Amazonian” business requires discarding what you think you understand about building a business and going back to basics.

So, what are these magic principles?

1. Focus on Customers, Not Competitors

Focusing on competitors is human nature—in a competition, we judge ourselves based on our rivals, and like to imagine how we’ll defeat them. But success in business comes from satisfied customers, not vanquished foes, and keeping a relentless focus on users/customers is key to building something great. This is hardly Amazon-specific wisdom: “build something people want” is venerable YC advice, and Zero to One also makes the point that competition is bad and focusing on it counterproductive. Perhaps the fact that so many different people feel the need to emphasize this point speaks to how counterintuitive it is: were it widely adopted, it wouldn’t be repeated.

Plenty of people outside business also get this wrong. A few weeks ago, a friend was explaining how he feels that many computational chemists are making software not for users but for other computational chemists. This is a case in which writing papers leads to different incentives than releasing products: papers are reviewed by one’s peers (other computational chemists), while products are ultimately reviewed by users. Hopefully Rowan doesn’t make this mistake…

2. “Bar Raisers”: External Vetos in Hiring

Amazon includes a person called a “Bar Raiser” involved in all hiring decisions, who isn’t the hiring manager (the person who is trying to acquire a new team member) but who has final veto power on any potential hire. The hiring manager is hiring because they need help in the short term, so they’re typically willing to engage in wishful thinking and lower their standards—but the Bar Raiser (who’s just another Amazon employee, with a bit of extra training) has no such incentives and can make sure that no poor performers are hired, which is better for Amazon in the long run.

I like this idea because it’s a nice example of mechanism design: just a little bit of internal red-teaming which (according to the book) works quite well. (Red teaming is another one of those “good but counterintuitive practices” which seems underutilized—see the discussion in a recent Statecraft interview.)

3. Problem-Focused, Not Skills-Focused

It’s natural to think about what we should do next in terms of what we’re good at: “I’m good at X, how can I use X to solve a problem?” Carr and Bryar argue that “working forwards” in this way is stupid, because only at the end do you think about who (if anyone) might care if you succeed. “Working backwards” from problem to solution is a much better strategy: first you articulate what you might need to accomplish to produce a compelling solution, and then you think about if you can do it. Inside Amazon, most potential new projects start out by drafting what the press release might be if the project were finished, and then working backwards from the press release to what the product must be. (Most products envisioned in this way never actually get developed, which is exactly the point of the exercise.)

George Whitesides has advocated for a similar way to approach science: first write the paper, then conduct the experiments. Lots of scientists I know find this repulsive, or contrary to how science should be practiced, but it always seemed shrewd to me—if you can’t make an interesting paper out of what you’re doing, why are you doing it? (Exploratory work can be tough to outline in this way, but there should be several potential papers in such cases, not none.)

4. Single-Threaded Leadership

As organizations scale, it becomes tougher and tougher to allow teams to work autonomously, and responsibility and authority for almost all projects ends up bestowed upon the same small number of people. This makes insightful innovation hard: to quote Amazon SVP of Devices Dave Limp, “the best way to fail at inventing something is by making it somebody’s part-time job.” Amazon’s solution is the idea of single-threaded leadership (STL, probably someone’s idea of C++ humor). Organizations need to be arranged such that individual teams can respond to their problems intelligently and independently, planning and shipping features on their own, and each team needs to have a “single-threaded leader” solely responsible for leading that team.

Instituting STL takes a good amount of initial planning, since dividing up a giant monolith into loosely coupled components is tough both for software and for humans, and it’s not in the nature of authorities to relinquish control. If done properly, though, this allows innovation to happen much faster than if every decision is bottlenecked by reliance on the C-suite. (It’s sorta like federalism for businesses.)

This idea matters for labs, too: some research groups rely on their PI for scientific direction in every project, while others devolve a lot of authority to individual students. The latter seem more productive to me.

5. Bias Towards Action

Humans are by nature conservative, and sins of commission frequently feel worse than sins of omission—making a bad decision can cost you your job, while not making a good decision often goes unnoticed (the “invisible graveyard”). To counteract this, Amazon expects leaders to display a “Bias for Action.” In their words:

Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.

Again, this echoes classic startup advice: “launch now,” “do things that don’t scale,” &c.

6. No Powerpoint!

In June 2004, Amazon banned PowerPoint presentations from meetings, instead expecting presenters to compose six-page documents which the entire team would read, silently, at the beginning of each meeting. Why? A few reasons:

I’m pretty sympathetic to these criticisms. Most high-stakes academic events today revolve around oral presentations, not papers—although papers still matter, doctorates, job offers, and tenure are awarded largely on the merits of hour-long talks (e.g.). As a result, I spent a ridiculous amount of my PhD just refining and hearing feedback on my presentations, much of which had nothing to do with the underlying scientific ideas. Perhaps this focus on showmanship over substance explains why so little science today seems genuinely transformational. (It’s also worth noting that presenting, much more so than writing, favors charismatic Americans over the meek or foreign.)

There are, of course, more than just these six ideas in Working Backwards, but I think this gives a pretty good sense of what the book is like. Overall, I’d recommend this book: it was interesting throughout (unlike most business-y books), and even if nothing in its covers is truly new under the sun, the ideas inside are good enough to be worth reviewing periodically.

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