[danielmarkbruce]

Domain expertise matters. Solving a problem for businesses with software is what most successful tech companies are doing - but it's very difficult if you don't have some insight into the process and problems of some group of businesses.

Observations as a regular human worked in the late 90's and around 2010 because there was lots of obvious stuff stuff to do when the web was taking off and mobile taking off. Outside those windows...domain expertise.

[moneywoes]

As an outsider is there an effective format for attaining domain expertise via interviews in the sales process?

[wpietri]

Selling things to people might give you domain familiarity, but it won't give you domain expertise.

I think a better approach is getting actual domain expertise in the door one way or another. E.g., a cofounder, an early hire, or a consultant. Thinking about the domain experts I've worked with in the past, I'd look for 10-20 years of work in the industry, hopefully in a few different roles or companies.

[eyphka]

For B2b, sales conversations and stalking users in their work is the process of achieving domain expertise, then product market fit.

You have to start with some hypothesis however.

[danielmarkbruce]

People seem to do it. I'm sure it's a lot of hard work but there are concrete examples. Stripe, Blend off the top of my head.

[throw_away1525]

I spent 9 years building domain expertise in a traditional engineering field and have many ideas for startups that would 100% be successful if I had the time/capital/grit/etc... not just me, anyone with my experience would be able to do it. It is incredible how absolutely awful most engineering software is. Meaning software for mechanical, civil, chemical engineers, etc... The business logic backends are basically all ancient codebases written in C++ or Fortran. CI/CD? Testing? Clean code? Yeah, no. You're having spaghetti code for dinner. And the greybeard who wrote that line of code 25 years ago and is still hanging out in a back office is reverting your changes if you touch "his" code. Imagine what someone could do using a language like Julia or Python/Numpy with modern software engineering practices could build. You would be able to iterate and add new features so quickly, none of the current players who all basically built their stuff in the 90s would be able to compete. I applied for a job at one of these companies and they earnestly asked me if I knew how to code in Delphi... um, no, have you guys heard of React or Electron?

[mlhpdx]

What makes you think the folks developing CAD/CAE software are so behind (nay, negligent)? No amount of React will solve topology or perform naming and matching (look it up, modern CAD can’t live without it).

A lack of respect for established success is both a superpower (don’t listen to the bozos) and foolishness. Knowing when to use each is what makes some pursuits blossom and others slowly burn out.

[throw_away1525]

Not talking about CAD/CAE, that's not my expertise at all. I'm specifically talking about chemical process simulation.

And React obviously gets used for the GUI instead of something like Delphi. As I said, for the business logic, there are amazing new languages available like Julia that allow devs to iterate significantly faster than in C++.

I guess I made a bit of a leap of faith assuming that it's just as bad in the other engineering disciplines.

[danielmarkbruce]

Are you saying the software products used by (for example) chemical engineers are no good? What are they using the products for? On what dimensions could the products be made better? Ie, faster, easier to use? Something else?

[throw_away1525]

Specifically chemical process simulators used by Chemical Engineers are all very, very long in the tooth. I sold and trained other engineers to use these products for years. They are fine in the sense that thousands of engineers around the world use them on a daily basis to get work done successfully, but they could be so much better. I'll give you one specific example. Engineers will spend hours, days, and sometimes weeks (I'm not joking) trying to get simulations to converge without any luck because they fail to manually generate 'good' initial guesses for the models. And because engineering design is an iterative process, you don't need to just generate good guesses once, you'll need to do it many times as you iterate towards a final design or solution. A neural network could be trained and used to assist generating these initial guesses. I built a prototype in Tensorflow that did exactly this and the results were very good - of course this was trained on a dataset generated for a very specific plant. But it worked, and it worked well. But all of the companies in this space are stuck in the early 2000s and just want to keep doing what they've always done, there is very little innovation going on.

[danielmarkbruce]

Chat? My email is my username at google's email service.