Be in a field where tech is the limit

I worked in bio tech for 4 years. Amazing people and problems.<p>Worst pay, top heavy salaries.<p>When a phd makes 80k a year and a “ML&#x2F;AI” data scientist is lucky to make 100k you won’t find any progress like software<p>They need to cut the top heavy executive bloat, respect the mid tier with better pay

Sigh.<p>Speaking as someone who has spent the last six years of their career working on advanced physics in various technology sectors (including biotech) and then trying to make various 2D-xene materials work for semiconductors, I’ll tell you one thing:<p>They pay you shit and if you think you’re all treated badly in FAANG, hoooboy, at least nobody has nearly caused deaths in the lab through negligence!

&gt; <i>Innovation happens in fields where our ideas are limited by our means to pursue them.</i><p>Citation needed. I&#x27;m pretty sure innovation is happening in all fields.<p>&gt; <i>Software is no longer such a field, our brightest minds should be going elsewhere.</i><p>Citation needed. Pretty sure innovation is happening in software too.<p>This post is... utterly fact-free, and really just reality-free. It literally says nothing besides &quot;biotech excites me&quot;. The author had no need to add a bunch of false generalizations on top of that.

&quot;Software is simply the encoding of human thought, and as such has an almost unbounded design space&quot;<p><a href="https:&#x2F;&#x2F;twitter.com&#x2F;cdixon&#x2F;status&#x2F;1385928617943838721" rel="nofollow">https:&#x2F;&#x2F;twitter.com&#x2F;cdixon&#x2F;status&#x2F;1385928617943838721</a>

&gt; Innovation happens in fields where our ideas are limited by our means to pursue them. Software is no longer such a field, our brightest minds should be going elsewhere.<p>As a relative dummy, I guess I&#x27;ll remain in software.

Do you want to know the real &quot;problem&quot;?<p>It takes a long time to develop biotech, test it, approve it, market it, and make money. There is also a limited market (ie the people sick with that condition, specifically in rich countries). The reason tech companies make money, grow&#x2F;iterate, pay more, is because they are in a field that does not require the same oversight and moral safety obligations (maybe they should to an extent) as well as being marketable to basically everyone in rich countries.

I think he&#x27;s got it exactly wrong — the reason we have seen a lot of &quot;non-tech tech&quot; companies is that software still fundamentally kinda sucks. We have become so used to it we don&#x27;t always notice, but software is a fragile nightmare to work with. It&#x27;s like trying to build skyscrapers with tinkertoys, and it&#x27;s a miracle we can do as much as we do. Software needs a leap; AI&#x2F;ML might be the start of it, not sure yet.

There are a lot of comments in this thread discussing biotech salaries. They&#x27;re not universally true. Source: I&#x27;m the VP of data science at Recursion, a biotech company focused on drug discovery, so I manage or have visibility into hiring in the domains that most people on this site would likely be interested in.<p>The biotech industry (which is made up of at least three rather different verticals: tools, diagnostics, and therapeutics) is changing quite a bit today. There are certainly companies that have less of a technology or data emphasis or who are still trying to figure out the value those could bring, and those companies are far less likely to pay well in SW&#x2F;DS roles. There are others that either from their inception or more recently realize the value these approaches can deliver and compensate accordingly. I personally find the new wave of biotech startups that are focused on being hybrids of experimental and computational capabilities extremely exciting (which is why I&#x27;m at one) and these are the firms where software and mathematical skill sets are most likely to be valued.<p>You&#x27;ll probably still make more on Wall Street than you would in biotech. But you don&#x27;t have to be _badly_ paid in order to work on a meaningful mission. OP is, IMO, correct that biology is entering a phase in which computational skills are a rate-limiting factor in our ability to make advances (note: not _the_ limit -- experiment is still absolutely critical), and it&#x27;s a super exciting and impactful field to be in.<p>Shameless plug: Recursion is hiring a TON of positions in data science and machine learning, engineering, and elsewhere. Check us out: <a href="https:&#x2F;&#x2F;www.recursion.com&#x2F;careers" rel="nofollow">https:&#x2F;&#x2F;www.recursion.com&#x2F;careers</a>. (Contact info is in my bio.)

I’m not convinced by this - “Innovation happens in fields where our ideas are limited by our means to pursue them” - but it’s written as if it’s self-evident. Why would it be like this and what is the argument that it is? By what measure is computing stagnant today compared to the 60’s, for example?

i think going in a field where they&#x27;ll pay you plenty without noticing you barely spend time working is the play<p>the brightest don&#x27;t spend their time &amp; energy making others wealthy

Software engineering is becoming a base layer for all fields. Meaning there will be a lot more cross disciplinary software engineers extending a wide range of companies and industries including biotech, agriculture, space, etc.<p>As such it isn&#x27;t mutually exclusive to be a software engineer while working in a field where tech is the limit. (But even so in my opinion, software itself is still just getting its bearings)

Every single superficial website or app released today is built on layers upon layers of incredible continuous advances in the software world. Just in the past decade the fields of cloud computing, AI&#x2F;ML, data warehousing&#x2F;analytics, distributed systems, real-time communication, geo syncing of data and computation, mobile&#x2F;embedded development, chipsets, compilers all evolved beyond recognition.<p>&quot;Ideas&quot; were and still are largely worthless. They are absolutely not the bottleneck in software today. There are a billion implementation-level problems that are still unsolved, and there will always be new ones.

Fields where tech is the limit are fields that don&#x27;t care about tech. As result, working as a software or tech IC in these fields is a grind. Never again -- no thanks!<p>Like, I&#x27;ve heard senior leadership at a &#x27;computation-focused&#x27; biotech company outright call engineers &#x27;bad people&#x27; only to quickly correct this to &#x27;bad at <i>being</i> people&#x27;, which is <i>so</i> much better!

Software is still at the 4 elements stage of learning (Earth, Wind, Fire, Water).<p>We have capability based security as a model for having computers that don&#x27;t get taken out by any flaw anywhere, but... like doctors who refused to believe that washing hands helped save lives, most programmers don&#x27;t believe in it, or have never heard of it.<p>Computers used to be leading edge because anyone could just get a machine and start hacking away at it, with physical hardware being the only limit. Now our operating environments are about as secure as a forest during high fire season... only little spark, and poof... your house is gone.<p>We&#x27;re about 10 years out, not because of a lack of ideas, but because of a lack of adoption of technology that works, instead of the old stuff extended way too far on a bad local maxima.

This also applies so hard to grassroots community organizing. They are doing all their scaling purely through manual human strategies. Which is great, but even small amounts of tooling can help these groups to organize better and avoid burnout -- burnout and frustration kills movements, because good-feels and passion is pretty much the only thing holding people together (never money, like in a regular field)

Computer science is nowhere near its maturity as a whole. It needs more people with ethics and empathy. We&#x27;ve seen what has happened without that factored in; we&#x27;re living in it.<p>Innovation as a goal sounds noble initially, but in my experience it&#x27;s like chasing the wind. Faithfully doing what is already known to be good seems better for everyone. It might even be the quicker road to innovation.

I am interested to learn about fields where software engineers can help to bring about innovation and push those fields forward, providing some of the tech that is missing there. I would like to apply my skills in a transdisciplinary manner and work on projects that are not just B2B SaaS products.<p>One option is research software engineering, where SWEs team up with researchers to produce better code for models and simulations. Are there any research fields where synthesis of domain knowledge, programming skills, and computational thinking could bring great benefits?

What a horrible article - the tech startup world is booming and ML, amongst other things, will create vast opportunities. This is the same drek I heard back in the early 2000s after the dot com bust. Literally heard people say, &quot;I think all the great ideas in tech have already happened.&quot;<p>Most of the time when someone is saying something that amounts to &quot;I can&#x27;t imagine what else we could make&quot;, it&#x27;s a failure of their imagination that&#x27;s the problem.

This seems to be largely a matter of taste? A field where ideas are the limits can be great for somebody who wants their success to be driven and measured by ... their ideas?<p>Sure, it can be frustrating to be banging your head against the same wall as everybody else, but there are people that thrive in such a setting. The most extreme example might be pure mathematicians.

Wife is a PhD Animal Geneticist, but works as government inspector on slaughterhouses. Makes 3-4 times the money her research colleagues do.<p>I&#x27;m not even sure what to think of it, honestly.

&gt; Biotechnology sounds to me much like computing in the 60’s.<p>One thing I&#x27;ve always wondered about biotech... I imagine there are many non-obvious correlations and interactions in medicine, which would be easily detected using nothing more advanced than Excel-spreadsheet level data analysis.<p>Making up an example: people with a certain DNA trait&#x2F;allele who also have a diet with a high amount of XYZ tend to not develop disease ABC as frequently as most people. Even if we don&#x27;t know the pharmacological reason <i>why</i> that is, it would still massively benefit lots of people, right?<p>So it always seems to me like tech from 2007 was ready to tackle this problem. Dump in a bunch of anonymized data, find correlations, repeat.<p>But I feel like I never hear anything about this type of work. Is it happening, but not publicized much? Is it actually not as simple as it sounds? Does nature simply not work in this way?<p>Even if 95% of diseases are just &quot;bad luck&quot;, I assume that other 5% is made up of environmental factors we don&#x27;t yet understand, but could easily learn using well-known data processing techniques?

The sentiment is only half right.<p>The important part is “and there is promising tech on the horizon”<p>I think trying to get a startup based on space travel at relativistic fields would be pretty difficult.<p>Steve Jobs was a master of this. Seeing promising tech trends that were just about ready, and putting them together at just the right time to make innovations that were world changing.

&gt; Innovation happens in fields where our ideas are limited by our means to pursue them. Software is no longer such a field, our brightest minds should be going elsewhere.<p>I feel like the author has an overly one-dimensional definition of both innovation and what it means for someone to be one of &quot;our brightest minds&quot;.<p>There are two different goods and two different talents at play here. The first is taking an idea that already has been had and making it possible. The second is inventing new ideas. Both are goods, but each requires very different talents.<p>Biotechnology desperately needs people who, given a great idea, can break technological barriers and enable it. If we accept that software is bottlenecked by ideas, then software desperately needs people who can radically change paradigms. &quot;Our greatest minds&quot; consist of both types of people.

Why is there no innovation in healthcare?<p>Because better technology won&#x27;t get the entrepreneur a satisfying reward.

Fields where tech is not at least seen as a key differentiator can be very frustrating. Expect to work in a culture where basic best practices aren’t used or even known. Expect decision makers not to understand how good software can speed up or empower research, and allocate resources accordingly. Expect salary and career growth to reflect that you are viewed as a commodity.<p>Your enjoyment of it hinges on whether you can be happy collecting a paycheck doing the bare minimum and satisfying your tech itch outside of work (FOSS, side hustles). For some, that is perfectly acceptable or even ideal, especially if you can get away with working fully remote.

Hmm, the richest country in the world just failed to build an app where you can schedule appointments. Tech might be easy for some people, but there is still a lot of room for innovation in this field.

Depends what you optimise for I guess, maybe fine if you are looking for novel problems or starting a startup, but in general with regards to your career, I believe Patio11’s point on this is more accurate: you want to be part of the profit center [<a href="https:&#x2F;&#x2F;www.kalzumeus.com&#x2F;2011&#x2F;10&#x2F;28&#x2F;dont-call-yourself-a-programmer&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.kalzumeus.com&#x2F;2011&#x2F;10&#x2F;28&#x2F;dont-call-yourself-a-pr...</a>]

Ah but this is only true for people who’s competitive advantage is technology!<p>For someone who is relatively better at ideating, I would argue the opposite is true.

biotech != computing<p>There are some important differences that are often overlooked by those coming from the world of computing...

Being in a field (or startup) where tech is <i>not</i> the limit is super frustrating.

How about being in a field where you are paid enough to live.

Most innovation happens these days in the gaps between disciplines. Biology||Chemistry -&gt; Biochemistry. Whole new fusion disciplines emerge.

&gt; Software is no longer such a field, our brightest minds should be going elsewhere.<p>I thought this in the 1980s

I&#x27;ve always loved science. I wish I could switch to biotech, but that would mean basically starting over.

That is not a new thought.<p>Chomsky was talking about this some 15 years ago.

AI and simulation are absolutely HPC-bottlenecked.

Couple of things to throw out there:<p>1) If you are able to bring a major drug to market 3 months earlier, it&#x27;s worth billions. Hence the continued interest in computational approaches.<p>2) Salaries in the pharma&#x2F;biotech biz are set nationally. Yeah, there are variations by geography, but less than one would expect. Thus, a PhD with x years can look up the salary range per region, etc.<p>3) The data is confusing and the error range(s) are unknown. So, many&#x2F;most of the models are retrospective rather than prospective and if the initial guess at the biological target or model fails, everything else is a waste of time. Google for all of the failures re: Alzheimers.<p>3b) As we can&#x27;t test on humans (at least not ethically), we&#x27;re totally dependent on animal models being good predictors of human behaviour. But, while chimps are like 98% similar to humans, the difference has resulted in catastrophic failures in Phase 1 testing. Diseases by the score have been cured in mice...<p>4) Computational modelling occurs at the start of the process, which is the most efficient. I think they had a sequence for the mRNA vaccine a few days after the Chinese published the data. Getting it made, stable and deliverable is where the time was consumed. And then the various clinical trials are significant costs in time and money. Hard to trust a model for a new class of disease or mechanism.<p>5) Computational methodology has been (over)sold since the 60&#x27;s. Yeah, there have been successes but they&#x27;ve been way fewer than hoped and people have grown rather jaded when presented with the latest breakthrough. ML&#x2F;AI isn&#x27;t really new as it was studied in the 90&#x27;s, but there&#x27;s way more data. See (3) above.<p>6) The crystal doesn&#x27;t always form. The reaction yields brown oil rather than white powder, or doesn&#x27;t scale. Chemistry is messy. And there&#x27;s a lot of material design problems that have not been amenable to modelling. There are new ways of gathering information (CryoEM), but we still need more&#x2F;better.<p>7) We need newer software and better parameterization. Both of these trace back to academic work on Vaxen, maybe SGI&#x27;s. Visualization software is probably the most valuable tool right now, with broad acceptance in the research stage.<p>7b) Physics might bite us in the ass. MD software, for example, tries to model explicit protein, ligand and solvent atoms&#x2F;molecules. Even given revised software and parameterization, entropy or chaos might prevent accurate numbers or what we can calculate might not be pertinent.<p>I could go on (and on), but I wanted to leave you with an upside... If anybody DOES deliver the goods, they&#x27;ll be bloody heroes. Fame, fortune, the whole gig - like CRISPR and the other advances that have occurred. So, if you and your buddies are smart and dedicated, it&#x27;ll beat the snot out of selling ads on handhelds in terms of making a difference.