Didn’t find much discussion of this online, so was hoping to start some here.
For people who have founded hard-tech startups in particular: what were the worst experiences you faced? What parts of the hard-tech startup reality would make you tell someone to get as far away as possible?
Didn’t find much discussion of this online, so was hoping to start some here.
For people who have founded hard-tech startups in particular: what were the worst experiences you faced? What parts of the hard-tech startup reality would make you tell someone to get as far away as possible?
Engineering + math graduate whose goal is to maximize impact. I am currently deciding between two career paths, but have been struggling a lot to determine which would be more impactful:
Note: For personal and practical reasons, it is unlikely that I would found a highly sophisticated hard-tech company, i.e. one that requires advanced fabrication / other specialized technologies.
TLDR: Has anyone here faced or thought seriously about a similar decision? If so, how did you decide where you had more counterfactual impact?
One way I’ve tried is through estimating the number of “counterfactual days saved.” Here’s my crude analysis:
- If a robotics bottleneck takes 600 researcher-years to solve and 400 researchers are already working on it, adding me would move the solution from 600/400 = 1.5 years to 600/401 ≈ 1.496 years, or about 1.37 days earlier. If 50 startups benefit, and I work on three such bottlenecks over my career, that gives roughly 3 × 50 × 1.37 ≈ 205 startup-days saved.
- In contrast, if I found five successful simple hard-tech startups, and each brings a useful idea to market one year earlier, that is 5 progress-years saved.
This crude analysis is missing many important factors, but on first glance, it seems that the startup path is more impactful, assuming I am unlikely to be an exceptional researcher in robotics (which I think is probable).
If anybody has a better way of comparing impact between academic and startup paths, though, would deeply appreciate it — I have been stuck at a crossroads for quite a bit…
Specifically, for many hard-tech startups that do not require extremely sophisticated technology, if the first inventor had not existed, how much later would someone else likely have done something similar?
By “hard tech that does not require extremely sophisticated technology,” I mean physical products that could be created in a typical local makerspace (i.e. without specialized nanotechnology, advanced fabrication methods, etc.). For example, smart thermostats and basic robotics would fall into this category.
I would like to believe the answer is often “years later,” but I can also imagine the delay being only a few days to a month, because i) many hard-tech founders are actively looking for startup ideas; ii) many of the underlying problems are already well known; and iii) if the technology is relatively accessible, it seems especially likely that multiple people would try to solve the same problem around the same time.
Is this intuition correct? I'm looking specifically for rigorous quantitative analyses that try to estimate the “delay” for accessible hard-tech startups, not one-and-off anecdotes. If anybody knows of any rigorous analyses, it would be deeply appreciated.
Specifically, for many hard-tech startups that do not require extremely sophisticated technology, if the first inventor had not existed, how much later would someone else likely have done something similar?
By “hard tech that does not require extremely sophisticated technology,” I mean physical products that could be created in a typical local makerspace (i.e. without specialized nanotechnology, advanced fabrication methods, etc.). For example, smart thermostats and basic robotics would fall into this category.
I would like to believe the answer is often “years later,” but I can also imagine the delay being only a few days to a month, because i) many hard-tech founders are actively looking for startup ideas; ii) many of the underlying problems are already well known; and iii) if the technology is relatively accessible, it seems especially likely that multiple people would try to solve the same problem around the same time.
Is this intuition correct? I'm looking specifically for rigorous quantitative analyses that try to estimate the “delay” for accessible hard-tech startups, not one-and-off anecdotes. If anybody knows of any rigorous analyses, it would be deeply appreciated.
Note: I am referring only to technical biosecurity in this post, i.e. wetlab / comp bio, not policy roles.
I recently learned about the field of biosecurity, and it sounds very interesting! That being said, I’ve heard that the ratio of qualified applicants to available roles in biosecurity is extremely high, sometimes 50:1 or more, both for entry-level and more senior technical positions. Is that generally true?
If so, what is the best way for someone to currently contribute to the field? Is waiting for more opportunities to open up really the only reasonable response?
Also, do people think there are good reasons to believe that many more opportunities will open up soon? For example, are major philanthropists or funders beginning to pivot more seriously toward supporting biosecurity work?
Note: I am referring only to technical biosecurity in this post, i.e. wetlab / comp bio, not policy roles.
I’ve heard that the ratio of qualified applicants to available roles in biosecurity is extremely high, sometimes 50:1 or more, both for entry-level and more senior technical positions. Is that generally true?
If so, what is the best way for someone to currently contribute to the field? Is waiting for more opportunities to open up really the only reasonable response?
Also, do people think there are good reasons to believe that many more opportunities will open up soon? For example, are major philanthropists or funders beginning to pivot more seriously toward supporting biosecurity work?
A friend in a chemistry PhD program described research as sometimes reaching a point where, after you’ve tried the obvious fixes, it starts to feel like you’re just guessing. I’m curious whether this is a common experience among researchers, though.
I.e., encountering situations where you’ve already checked the protocol, reviewed the relevant theory, applied heuristics, tried all the promising approaches you can think of, and talked through the problem with others — but nothing seems to fix the issue, forcing you to make speculative guesses.
How often does this happen in your experience? For example, what percent of projects does this occur in? When it does happen, how long might you spend in that “guessing” phase before finding a solution (i.e. is a couple months not uncommon)?
I.e. situations where you’ve already checked the protocol, reviewed the relevant theory, applied heuristics, tried all the promising approaches you can think of, and talked through the problem with others — but nothing seems to fix the issue, forcing you to make speculative guesses.
How often does this happen in your experience? For example, what percent of projects does this occur in? When it does happen, how long might you spend in that “guessing” phase before finding a solution (i.e. is a couple months not uncommon)?
I'm a mechanical engineering undergraduate considering a career as a research scientist at a national lab and had a few questions:
How does this percentage breakdown change as one moves from research scientist → senior research scientist → principal research scientist?
Do "research scientists" typically get to pursue their own projects, or are they mostly contributing to projects led by "senior" / "principal" research scientists? At what level do scientists typically start leading their own research programs?
How difficult is promotion from research → senior → principal scientist?
Do national labs still hire people to work on relatively fundamental, curiosity-driven topics that may not have an obvious near-term application (e.g., soft robotics, novel materials concepts, etc.)? Or is this very rare, i.e. most hiring is for work that is tied to specific needs?
How competitive is it to become a research scientist? What academic benchmark is most comparable (top R1 faculty, strong public R1 faculty, etc.)?
For principal scientists, what fraction of funding typically comes from base lab funding vs. external grants? How much pressure is there to continually bring in funding at each career stage?
Insights into any of these questions would be appreciated.
I'm a mechanical engineering undergraduate considering a career as a research scientist at a national lab and had a few questions:
How does this percentage breakdown change as one moves from research scientist → senior research scientist → principal research scientist?
Do "research scientists" typically get to pursue their own projects, or are they mostly contributing to projects led by "senior" / "principal" research scientists? At what level do scientists typically start leading their own research programs?
How difficult is promotion from research → senior → principal scientist?
Do national labs still hire people to work on relatively fundamental, curiosity-driven topics that may not have an obvious near-term application (e.g., soft robotics, novel materials concepts, etc.)? Or is this very rare, i.e. most hiring is for work that is tied to specific needs?
How competitive is it to become a research scientist? What academic benchmark is most comparable (top R1 faculty, strong public R1 faculty, etc.)?
For principal scientists, what fraction of funding typically comes from base lab funding vs. external grants? How much pressure is there to continually bring in funding at each career stage?
Insights into any of these questions would be appreciated.
For context, am an undergrad exploring energy research and careers. However, I am hesitant to pursue a career in battery research for the following reasons:
- Fission is growing more accepted. If, over the next century, a substantial fraction of electricity generation (during evenings or cloudy periods) ends up coming from fission, would better batteries still matter that much? The main counterargument I can think of is that batteries could significantly reduce emissions in the meantime while fission scales up, but is this really true?
- Regardless, even if batteries became cheaper or longer-lasting, would deployment be bottlenecked more by politics / lobbying from fossil-fuel companies?
- In general, what research directions would you recommend to someone who is new to the energy space and trying to maximize their impact (is it battery research? Or something else)?
Apologies in advance for the naive questions…
For context, am an undergrad considering a PhD in some field of energy research, with the goal of helping reduce global GHG emissions.
One field have identified is battery science, but I am hesitant to pursue a PhD in it for the following reasons:
- Nuclear power is growing more accepted. If, over the next century, a substantial fraction of electricity generation (during evenings or cloudy periods) ends up coming from nuclear, would better batteries still matter that much for reducing GHG emissions? The main counterargument I can think of is that batteries could significantly reduce emissions in the meantime while nuclear scales up, but is this really true?
- Regardless, even if batteries became cheaper or longer-lasting, would deployment be bottlenecked more by politics / lobbying from fossil-fuel companies?
- In general, what research directions would you recommend to someone who is new to the energy space and trying to maximize their impact (is it battery research? Or something else)?
TLDR: Would pursuing a PhD in battery research actually help reduce GHG emissions?
Apologies in advance for the naive questions…
I am a math + CS undergraduate mulling over the ethics of two potential career paths:
1. A PhD in robotics, particularly in continual learning / creating human-like intelligence in robots.
2. Joining an industry team working on automating scientific discovery (e.g. Anthropic’s Discovery team or similar efforts).
One concern I have is that both paths might advance AGI timelines. In particular, it seems possible that architectures developed for continual learning in robots or long-horizon scientific agents could transfer to more general-purpose AI systems.
Is this a valid concern, and is it a common view within the AI safety community? I.e. would mainstream AI safety researchers view either of these directions as meaningfully contributing to AGI capabilities? Or are there strong reasons to believe that work on either of i) continual learning in robotics or ii) scientific AI agents would not significantly advance general AGI capabilities? Would appreciate honest perspectives.
TLDR: Is it very likely that either of robotics / scientific discovery capabilities research meaningfully accelerate general AGI capabilities? If so, why?
(For reference, I have read quite a bit of the AI safety literature, but don’t find alignment research particularly enjoyable. Hence, my [perhaps futile] hope that robotics and / or AI-for-science do not meaningfully advance AGI. If people think either area materially accelerates AGI capabilities, though, I’m happy to steer clear…)
I am a math + CS undergraduate mulling over the ethics of two potential career paths:
1. A PhD in robotics, particularly in continual learning / creating human-like intelligence in robots.
2. Joining an industry team working on automating scientific discovery (e.g. Anthropic’s Discovery team or similar efforts).
One concern I have is that both paths might advance AGI timelines. In particular, it seems possible that architectures developed for continual learning in robots or long-horizon scientific agents could transfer to more general-purpose AI systems.
Is this a valid concern, and is it a common view within the AI safety community? I.e. would mainstream AI safety researchers view either of these directions as meaningfully contributing to AGI capabilities? Or are there strong reasons to believe that work on either of i) continual learning in robotics or ii) scientific AI agents would not significantly advance general AI capabilities? Would appreciate honest perspectives.
Hi all, math + CS undergrad considering whether to pursue a PhD or industry work in robotics. Made a post yesterday in a similar vein, whose replies made me wonder what the purpose of theoretical robotics labs is. (For clarity, I refer to “theoretical robotics” as work on planning, learning, control, reasoning, general algorithms, etc.)
Genuinely not trying to offend anyone — I just want to understand, why do these academic labs continue to receive substantial funding? Why are they considered useful? (For instance, is the main value of academia in providing an environment to pursue moonshot ideas that industry is unlikely to invest in?)
For context, I’m an math + CS undergraduate considering a PhD, but I’m still unsure which CS subfield to pursue. One area I’ve been exploring is theoretical robotics (which I loosely define as work on general algorithms, learning, planning, and intelligent behavior). Some of my electrical engineering friends chose industry over academia because they believe industry work is far more impactful. Is this actually true? And if so, what important roles (if any) does academic theoretical robotics still play?
One role I can imagine is providing an environment for pursuing high-impact moonshot ideas — though unsure if this actually happens in practice.
For reference, my main goal is helping automate physically demanding labor (e.g., construction, mining, agriculture), though I’m open to contributing at any level of the stack; hence why I’m drawn to more theoretical work on algorithms and intelligence. I would be grateful for critical, honest perspectives. If robotics in academia is largely disconnected from practical impact today, realizing that now would be extremely valuable for making career decisions.
TLDR: What important roles (if any) does academic theoretical robotics have?
Curious in particular: how often do researchers directly interact with policymakers, and what does that interaction typically look like? Is it mostly presenting research findings to policymakers (perhaps once a week), or something else?