[pkpkpk2]

RE: "Having a PhD means that you've spent years on a certain subject. You cannot just read a book and be up to speed on biomedical research and CRISPR."

Isn't that what books are for? To compile, document and share knowledge some people spent years to figure out?

[Odenwaelder]

There is a huge gap between having knowledge from books and being able to do original research, let alone solving a biomedical niche problem using CRISPR. This gap is usually filled with an advanced degree, where you spend years in the lab, keep track of the latest research in the field, try to find your niche and solve the actual problem.

I mean, by all means, try it. But life sciences are not computer science. The approach is entirely different and quality of the work you need to do is different. It looks much, much easier than it is. (Which is, on a different note, why I believe the whole pseudoscience crap such as anti-vaxxers is gaining so much traction)

[pvaldes]

> But life sciences are not computer science

Exact

1) The use of materials is totally different

Computer programming is a stuf that uses reciclable electricity and "eating your own dog food" kind programs, cheap to produce or free to copy, easily available and in many cases free except by the hardware (Hardware that can be hired, "clouded" or increased gradually).

You don't need to pay a dime for using R, C, Perl or Python and you can obtain the four, ready to use in your computer in less than a half hour. If you need a microscope, there is not an equivalent open source stuff replacement available.

In Biology the matherials will be sold to you only in packages of 10 Kg each pigment, with a caducity date, even when you would need to mix just 10 grams of each one. The spendable one-use only stuff is not free and the price for a single kit is incredible. You will pay it in any case because is indispensable for validating your work and you plan to use 500 of those kits the next year so you are a captive client from this company (that could decide to stop selling you if you try a way to lower the price, and will sue you if you try to copy the formula and make the product by yourself).

2) Documentation is not free and obtaining it is time consuming

In computer programming, you dont need to spent weeks to be delivered to you, or plan a travel to Peru to collect samples of a plant virus. You don't need to spend days just to reach the documentation navigating a miriad of closed or pay per viewed journals, at 50 dollars to peek in each paper.

You can expect to learn something and use it for years. Fortran is still there. You can program automatically your computer to make a hundred of safety copies each working day. In biology you can not clone your amazonian beetles collection, is unique and will be atacked and reduced to dust by real bugs from day one if you do not protect it.

3) There is not an obvious, linear path to success

Errors are random events that you can't always control

In biology you will lose eight months of your research because your samples travelling from Swedden to Madrid end somehow stuck in a Lithuanian airport for ten days and now are defrozen and unusable. You needed this research to assure the new funds and keep running, and now you have three months to obtain new samples, redo and fix it.

You can lose years chasing a dead end or be superseeded by a genius in some part of the planet that discovered the same as you first, or a different and better way to do it.

4) You don't just buy a lab and hire a team to create "something" nice.

Unless you are in the bussiness of teaching science you design the lab according of the exact product what you are trying to create. Any machine that you ordered and will not use enough frequently later is a hole in your presupuest. Any timed out kitt that you bought in excess quantity is your money ending in the dumpster bin.

You need to hire somebody familiar with what "hardware" is trendy and works and what machines are outdated since ten years even if you see it in each faculty and in propaganda.

[iongoatb]

Describing the current state of CS and comparing it to the current state of biotech/genetic engineering is like comparing apples to oranges. In the beginning of the computer revolution the obstacles were extremely similar to the current obstacles (or opportunities) with biotech. The materials were expensive, documentation wasn't abundant, people didn't think personal computers would ever be a thing or that people would interact on the internet using social networks, and server/computing costs were extremely capital intensive before cloud computing.

There is a great opportunity at this moment, similar to the opportunity that Bill Gates and Paul Allen had. Mainframe computers were expensive, yet they found ways to practice and become highly skilled. There wasn't documentation like there is now, yet hacking culture found ways to build cool and effective stuff. Obviously there wasn't a linear path to success - people thought personal computers would never be necessary, yet Apple and Microsoft were huge successes that no one ever expected. There are people that see opportunity in fields like biotech and bioengineering, and there are people that only see the obstacles. The former create amazing things, while years later the latter are envious that they didn't have similar vision and courage.

You made my point.

[pvaldes]

>There is a great opportunity at this moment

I wouldn't advice to invest in the company of this guy, but is their money, not mine, so... do as you please. Honestly, I would love to hear that he became billionaire.