Anti-advice: the opposite of what experts tell you

How, through systematic thinking, you can become your own best mentor

“Do you have any advice for me?” It’s a question that one of us (Ed) commonly gets after seminars and at conferences. I never know how to answer the question. Should I recommend that the advice-asker take more risk, or less? Well, the answer depends on whether the advice-asker is already too timid or too foolhardy. I can’t answer the question without knowing the advice-asker’s personality and experiences. Should I suggest that someone work more, or rest more? Well, if someone is pushing themselves to the point of illness, giving the first recommendation might be cruel. But if someone is slacking off, recommending the latter might be counterproductive. Often, the best I can do is to suggest that someone treat their own life as an experiment - try something, and then analyze the data.

If doing X helped, do more of it. If doing X wasn’t helpful, stop. (Indeed, there might be a period of time in your life, where you have “protected time” for this kind of self-experimentation - such as when you are a student - if so, take advantage of it!) By trying out different strategies or practices - meditating daily, reading a book a week, writing 10 pages every morning - and then documenting and analyzing the outcomes, you can, over time, learn how your brain works. Over time, you begin to know yourself. You might eventually form intuitions about your mind, which help you choose paths that are better for you, with less struggle.

1. Treating your life as an experiment

Each of us (Ed, Claire, Nina) has benefited from such self-experimentation. We’ll now give a few examples. Through such experimentation and analysis, one of us (Ed) learned lots of tricks about his mind. (As in past essays, we’ll switch into singular first-person voice, for these personal narratives!) I learned that I needed to put my tasks on a calendar, rather than on a todo list. A calendar forced me to schedule tasks to begin at specific times, and to block off enough time to finish each task. In contrast, a todo list could grow endlessly, without enough time being reserved to complete any task. Forcing myself to assign tasks to specific times, curiously, led me to develop a productive use for procrastination. If I couldn’t complete a task in the time allotted, I would need to find a new time to finish it (or perhaps I would reschedule the task entirely, before beginning it). I decided that if I procrastinated a task 3 times, then I would analyze why this task needed procrastination: perhaps it needed to be broken down into parts, or increased in priority and given more time, or perhaps canceled altogether. Most usefully, quite often I would realize that I was missing a preparatory step: I was trying to do something before I was ready, and needed to do some other task first. In short, I turned procrastination into a way to analyze tasks for hidden assumptions. (If your goal is to climb an icy mountain, but nobody’s invented the ice axe yet - perhaps your most urgent goal should be to invent the ice axe.)

As a second example, I had trouble sustaining my attention to read scientific textbooks in college (and at one point, I was even diagnosed with ADHD). I tried different strategies - highlighting text, reading text out loud, studying with friends - until I discovered that if I copied books by hand (summarizing when possible, of course), tagging each thing that I read with the kind of knowledge it was (e.g., labeling a sentence with “@” for example, “Δ” for definition, “A” for assumption, “R” for result, etc. - a strategy I called “semantic tagging”), I could force myself to pay attention to the knowledge. Soon, I began going beyond mere attention and started drawing arrows between concepts as I realized what kind of knowledge each sentence was, linking them into emergent networks of insight. In this way, I made my way through many entire textbooks. Also, summarizing helped me remember content better (and I also got, in the end, a useful reference sheet). I could go on - such experiments led me, over the years, to sleep early and wake early, to have a “people advisory board” to give me perspective on difficult situations, to tune my caffeine intake, and more.

Years ago, another of us (Nina) spent time experimenting with ways to become more productive. Because of this period of my life, I regularly get asked for tips on efficiency, but I am hesitant to answer, since different people might be unproductive for different personal reasons. And, different such reasons might benefit from different productivity tools. At the beginning of my journey, I tried conventional tools like Apple Notes and Google Calendar, but they didn’t address the personal reasons I was being unproductive. Indeed, Notes felt ‘messy’ - I couldn’t find a good way to visualize the priority of my tasks. To solve this, I tried bolding text to denote task importance, but that felt too binary. Then, I switched to Google Calendar, and while I could color-code tasks by type and priority, I found that scheduling tasks for particular times was too rigid for my hectic and dynamic schedule (curiously, the opposite of Ed’s finding, driving home the point of this article!). Finally, I switched to a Notion page, with color-coded blocks representing tasks that I could shift around from day to day. This has been my trusty setup for six years now.

That all being said, I notice that many people asking for productivity advice already know what they have to do, and just do not want to do it! No shame here; I’ve definitely felt this way in periods of my life before. It is important to note that the fix here is not simply task tracking, which, for some people, doesn’t help them attain the motivation to do the work. In this situation, reframing your mentality around work might be a better bet. How can you do that? Back in freshman year, when I felt this way, I ran self-experiments to understand what was at the root of this issue. I would permute a variable about the work - for example, choosing assignments that vary in the amount of writing I committed to do - and see how I felt. I noticed that when I would tell myself that I would not feel guilty if I did not do the work, I felt a lot better about doing the work. For you, though, it may be different. Improvement requires honest self-awareness and a willingness to experiment long enough to figure out what the real problem is.

For another of us (Claire), self-experimentation has been useful for choosing a career path. Many people offered me advice on the ‘right’ thing to work on - what sort of job would be best, or how to gain optionality. Much of it sounded cliché. So rather than accepting or rejecting it, I tested it. I took a gap semester and deliberately set up contrasts: I worked as a machine learning engineer at a neurotech research startup (a focused research organization (FRO) in SF), then pivoted to a systems internship at Apple - a completely different field, scale, and culture. I ran shorter-term experiments too - rotating through wet lab, hardware, and computational work to see which held my attention. Through analysis and a lot of writing, I was able to pinpoint specifics: I loved research engineering but not pure engineering, and I found that FROs delivered on their promise of doing scalable science faster. Perhaps most unexpectedly, the gap taught me that confidence doesn’t come from accumulating personal proof, but rather, for me, it came from watching other people who, when faced with a hard problem, simply decided they’d figure it out. That reframe changed me more than any single job did. It turns out the cliché advice (”explore broadly,” “try things,” “gain optionality”) wasn’t wrong, but it was useless to me until I had the experiential ground truth to parse it.

As a second example - on a more personal level, a high school friend pointed out to me that I relied on changes in environment to fix internal problems: choosing colleges, cities, and jobs as though the right place would make me the right person. She was ultimately right, but it took a few rounds of experimentation - and experiment failure - to appreciate her insight: moving away from MIT to SF didn’t fix the things I wanted fixed, and then coming back to MIT didn’t either. What worked was changing behavior, consciously, within an environment: joining a dorm with a stronger community helped me overcome loneliness more than moving to a different city, starting a radio show helped me spend more time on what I loved than just surrounding myself with music people and hoping for growth, and structuring weekly rituals with friends helped more than endless scheduled hangouts. Such insights might seem banal, but I couldn’t have received them as advice. I had to fail first. (As the old saying goes, six months in the lab can save you an afternoon in the library.)

Of course, self-experimentation works best when you can rapidly see the outcome of a choice - feedback must arrive fast enough for you to choose your next action appropriately, given what you learned from the outcome of your last choice. But sometimes a clear outcome is quite delayed, relative to the time of making a decision. One might, in such a situation, pursue a heuristic such as regret minimization: will I regret the negative outcome of trying, vs. not taking the risk at all? But sometimes the stakes are big, and the outcome is delayed for years or even decades. Should you quit your job and start a company, or stay in your current job? Should you do a PhD, or go into industry? Should you pursue idea X, or idea Y, for the next 5 years? In such situations, where the outcomes might not become clear for years, you can’t rely only on your own future experiences - you might need to rely on the experiences and thoughts of others. You might need… advice.

As we noted above, though, the advice-giver might tell the advice-asker exactly the wrong thing. For every entrepreneur who needed encouragement amidst their startup’s struggles, another would have been better off moving on from the dire reality of their situation. For every dropout who quit school, another might have benefited from more educational structure. The advice-giver almost has too much responsibility: to give good advice, they must have an almost otherworldly knowledge of their listener - indeed, more knowledge about their listener than the listener themselves might have. So, the advice-asker has a hidden burden to bear and some tacit tasks to pursue.

First of all, the advice-asker must choose who to ask for advice from. The advice someone gives will be biased by their own experiences and actions - many of which will not apply to the asker. For example, the advice-giver may have grown up or made their choices or achieved their goals in an earlier time, and now the world is different. What was a good decision in 1995 or 2005 might be a bad one today. Second, the advice-asker must choose the right question to ask. Of course, quite often, the advice-asker doesn’t know enough to choose well - that’s why they are asking for advice in the first place. Asking for advice with too narrow a scope, for example, could backfire. Imagine that an advice-asker asks an advice-giver whether one should join company A or company B. But perhaps, the question they really should be asking is whether they should join a company in the first place, vs. starting their own.

By asking the wrong question, the advice-asker would artificially limit the scope of answers that they could receive, or even send the advice-giver down the wrong track entirely. Perhaps a good advice-giver will recognize this and invoke a higher-level framing in their response. But there are lots of potential advice-givers with important lived experience, or stunning accomplishments, who might not know how to project their value into their listener’s reference frame, or possess the empathy to figure things out on the fly. So in the end, the advice-asker needs a good strategy in order to make the most of the advice they receive. Ideally, the advice-asker doesn’t ask a question that is impossible to answer, and then simply follows the response without questioning.

2. When you hear advice, simulate experiments on your life

We started this essay by talking about experimenting on your own life. Listening to advice might best be followed by simulating an experiment on your life. This could take many forms, which are thankfully learnable and teachable. For example, if you are wondering which path to take and want to seek advice, you might start by making a tiling tree of all the possible paths you could take and then ask people for feedback on each path. Or, if you ask various advice-givers for advice on a question, and you get several different answers, you might create a tiling tree to organize those answers, and then use the tiling tree method to extend the tree to generate alternative answers that perhaps no advice-giver would think to give.

A simplified strategy would be to ask someone for advice, and then visualize, in as much detail as possible, what would happen if you did the opposite. Don’t just visualize the consequences, but also visualize the emotions associated with those outcomes. Depending on the topic at hand, you might need to think many years out. If, after you do that visualization, you realize that the opposite path looks better than the path you were advised to follow, you just might decide to take the opposite path! (By the way, this is a generally useful strategy for problem solving: consider how people are trying to solve a problem, and then visualize what would happen if you pursued the most opposite possible idea. You can read about an example of this strategy working in the story of inventing expansion microscopy¹.)

In our own lives, this way of thinking has proven useful. We’ll again give some examples, switching to the first person singular voice. For one of us (Ed): when I started my lab at MIT, I recall some senior scientists telling me that I shouldn’t collaborate too much with other scientists when doing my research. After all, to be awarded tenure at MIT, I’d need to develop an identity distinct from those of other scientists. And, things were already off to a bad start - I was rejected from most of the places I applied to for faculty jobs, including all the original departments at MIT that I had applied to². But then I considered the opposite situation: if I were building tools and collaborating with many people to generate them and to apply them to problems, my group could become a hub within a huge network, generating lots of impact. I tried to visualize what this would look like - 1, 5, 10 years out. My group ended up collaborating with other groups on everything from molecular tools for controlling the brain with light to new strategies to noninvasively help treat Alzheimer’s to nanofabricated neural probes and beyond. We sent technologies out to other research groups tens of thousands of times over the years (working with partners like Addgene), sharing as freely as possible³. The strategy worked - we became a hub in a vast network that could generate innovations sustainably and usefully. (And, I did get tenure at MIT.)

For another of us (Claire), I kept hearing versions of ‘be realistic’ - about what I could change, about what was within my control, about what someone my age should expect. The advice came from a kind place, but I noticed it mostly functioned as permission to not try. When I considered the opposite, that I actually could change most things about my situation if I was willing to act and make big changes around me, things started moving. For instance, everyone told me to stop overcommitting and learn to say no, but the real fix was the opposite: instead, I leaned into committing hard to specific things I actually cared about, like a radio show, a demanding fabrication class, and weekly dinners I hosted - and, naturally, I stopped spreading myself thin across obligations I didn’t care about. The problem was never volume; it was direction. Similarly, I worried my interest in neurotech was just momentum from high school, and the standard advice was to stick with what I knew. Instead, I deliberately pivoted to jobs in completely different fields and cultures, like computer systems or robotics, which ended up confirming I actually did love neurotech, while also teaching me what I didn’t enjoy (e.g., pure software engineering). I needed the opposite path to validate my original one.

For another of us (Nina) - I get told often that I should temper how I present my goals. For context, a major life goal of mine is to cure Alzheimer’s disease. Friends and mentors have often told me that when applying for fellowships or speaking to other scientists, I should take more effort to make my goals sound reasonable. And on one hand, I get it: overly ambitious goals can make you sound unrealistic. But I have never had so much conviction about anything in my life. And I’ve come to realize that in interviews and conversations, that belief actually excites the people whom I’m talking to. I don’t completely ignore the advice, though; I recognize that my mentors’ concerns come from a place of worry that people will think I don’t understand the sheer complexity of biomedical research when I state something so ambitious. This is why I always follow up my excitement with context, for example, sharing my thoughts on experiments, funding policies, and changes in the field as a whole that I believe are needed to get there.

Lesson learned: the opposite of good advice, could be better advice. For any advice you hear, consider the opposite of it – the anti-advice, if you will. Of course, don’t just blindly do the opposite: the opposite of good advice could also be terrible advice. Instead, visualize - that is, simulate in your mind - the outcome of following the advice that you hear, as well as the outcome of following the opposite of that advice. If you have a mentor and are getting advice from them over an extended period of time, you might repeatedly apply this method in order to generate many instances of anti-advice over the years. (If you have a mentor whose anti-advice that you generate from their advice is repeatedly better than the advice they give you, you might consider them to be an anti-mentor. Pro tip: don’t tell them they’re your anti-mentor.)

3. Solving a bigger problem can be easier than solving a smaller one

One corollary of this way of thinking is, “don’t take requests.” As the old saying goes, if Henry Ford asked customers what they wanted, they would all say they wanted a faster horse. His customers just didn’t have the concept of an automobile in their minds. Steve Jobs famously didn’t like market research: how would his customers know they wanted a phone with a touchscreen when they had never seen one that worked? You can consider this as a form of frame reshaping: someone providing a request to you will implicitly control the range of variables to be considered (e.g., a request for a faster horse limits the frame to that of transportation animals); you can reshape the frame (in the horse example, by broadening the frame to include all forms of transportation, including mechanical possibilities). Of course, it’s great to learn what people want - but don’t blindly implement what you learn; take a step back and think about the bigger context of the request: what does the request mean amidst the state of the world, and when augmented by imagined possibility?

Curiously, solving a bigger problem is sometimes easier than solving a smaller problem: the reason is, when you take a step back from a narrowly scoped problem, and consider a bigger problem space, you might also gain access to a bigger solution space - and sometimes, that bigger solution space will enable a far more efficacious, low-risk, or practical outcome than the original narrow solution space would have permitted. To continue the horse/car example: broadening the space of transportation methods beyond just animals, to include machinery innovations, makes the problem far easier to solve. Building a car turned out to be quite practical, whereas breeding a faster horse would have been extremely difficult, given the science of the time. Of course, if what you end up doing doesn’t cleanly fit into people’s expectations, you might need to work on it alone (or with trusted, inner-circle colleagues) for a while. It might take time until what you’re doing has matured to the point where it connects with people’s needs. Hence the need for “skunk works,” where people work in relative isolation, protected from too much external critique, in the short term. Perhaps this is why “involuntary collaboration”⁴ is such a frequent pattern in revolutionary innovations - it allows each step to be done in isolation, with information sharing helping the whole to emerge over time, as far more than the sum of the parts.

Another nice corollary of this way of thinking is that you can learn to be your own mentor. You can propose some advice to yourself, and then visualize the outcome of following it - and then repeat the exercise for the opposite advice. It’s as if you have your own mentor and anti-mentor constantly offering you help at all times. (And, as you progress, you might start generating advice tiling trees, as a systematic way of generating all the possible kinds of advice that you could give yourself.) Of course, you can be your own meta-mentor, as well - finding a set of mentors for yourself, and then assembling all that you learn into your own framework. As you walk around and see potential solutions, actions, and possibilities, you can constantly think, “What if I did the opposite of that?” It can become a reflex that yields on-the-fly creativity, to visualize the opposite of what you see before you. It becomes a fun game you can play to help you question everything, generating innovations as you go throughout your day.

Generating anti-advice can be difficult, in the beginning. Indeed, it can be hard to take advice in general: a common saying is that if a piece of advice is hard to hear, that means you should take it. But perhaps advice is hard to hear because it’s bad advice, or perhaps there’s something in your internal state that you need to process before you’re ready to take any advice at all. Sometimes you need to have the advice broken down into steps - what you hear at first may present too big a leap. Comfort is not always a bad thing - it can help you realize what you need to do to get where you want to go. By considering advice and its opposite, you can optimize for the big picture.

This essay was generated as the result of many years of observation and 20 years of operation of the Synthetic Neurobiology group⁵. But as we wrote it, we encountered synergistic documents, both in our own past and by others. Claire wrote a less scientific-research-focused post in the past, which we’ve summarized when applicable, at points throughout this article⁶. There are additional notes at that link, on giving/getting advice in less career-based situations. We also came across an article by Slate Star Codex, which presented a related concept, to ‘reverse any advice you hear’⁷.

In any case, when asked to give advice, one of us (Ed) usually offers a few pieces of advice (with copious caveats, as you might guess). But then I end by saying, ‘whenever you hear advice, consider doing the opposite.’ I then add, paradoxically and half jokingly, ‘including that advice, which I just gave you.’ Sometimes it lands.

A student at a lunch I hosted at a university I was lecturing at, emailed me a few months later. At the time, she’d been considering starting a company, but faculty at her university advised against it. She converted their advice to anti-advice, founded the company, and became its chief science officer. It ended up bringing forth life-saving ideas to the world. She’s now working on her second startup, which aims to bring forth therapeutic technologies in a second problem space. Perhaps you will also save lives or change the world by following anti-advice or finding anti-mentors.

And perhaps through struggling with such processes, you will also learn to give better advice to others down the road.

1. Engineering Serendipity!

2. Once again… engineering serendipity

3. https://www.addgene.org/Edward_Boyden/

4. Involuntary Collaboration

5. Link to lab website: synthneuro.org

6. Claire’s post some things I know about advice

7. This Slate Star Codex post also mentions “should you reverse any advice you hear”