By "worker bee" I meant someone not in management.

No one knows everything: (1) Designing, growing, and managing the LAN for AWS? Well, if haven't been specializing in that for the last 10 years, then call Cisco, some network systems management vendors, etc., and hope that the company will wait as you become an expert in that work. (2) Same for the AWS connections to the Internet points of presence. (3) Large scale C++ programming, from design and documentation through testing, deployment, and bug tracking and fixing; again would want some years doing just such work. (4) Configuration and management of large scale use of virtual machine? If haven't been doing a lot of that recently, then call VMWare and hope that you will have time to become an expert. (5) Large scale front end Web site programming with hundreds, maybe thousands, of Web pages, sending thousands of pages a second, writing, updating pages continually, many a day, lots of details about JavaScript, getting bugs out, making it fast enough, getting it tested and documented, making sure it runs the same on lots of Web browsers, arranging the CDN help, working with Akamai on stopping DDOS attacks, making the security good enough for a big financial institution, keeping up on security threats, etc., working with the Web site back end staff to make the site fast enough. A lot of work, managing lots of work and people. Better have been concentrating on that, especially for financial services, for the past 15 years. (6) Handling system management, installations, updates, monitoring, security, performance, designs for growth for a large server farm -- better have been doing that for 15 years, with some one operating system family, and grown up in that work as server farms grew at Amazon, Microsoft, Google, Facebook, etc.

See, there are a lot of narrow specializations. For each of them, for the high level positions, need to have been specializing in those for years. Then with that career track, there is a big risk: That specialization may shrink or die. Then don't have comparable experience in another such narrow specialization and are in career trouble. Nursing and K-12 school teaching are much more stable.

I was willing to do whatever for a salary that would let me live without losing money. E.g., at IBM, cost of living was so high, commuting distances so long and expensive, and salary so low that I actually lost money working there. Heck, I saved money working myself and my wife through our Ph.D. degrees but lost money working at IBM. I can't afford to take a job that costs me money.

I am highly qualified in some specializations. For nearly everything that people hope to get from AI/ML now, my background and track record is highly superior. E.g., not many people want to do curve fitting with 1 million variables and 100 trillion observations! So, right, I've never programmed a high end NVIDIA GPU for curve fitting! For data science, my background, education, accomplishments, are superior, by a LOT.

But, generally, an organization should expect a new hire to learn over some months about the specific, narrow topics of importance at that organization. Then what is needed is a good, broad background, all the way from assembler, ring security and gate segments, capabilities and attribute control lists, enough in number theory to understand RSA, PKI, and VPNs, etc., and I have that. It remains, for analytical work, now likely considered part of data science, optimization, simulation, and control remain among the most powerful tools, and I'm quite good with those. E.g., going to do supply chain optimization with AI/ML? Not a chance! Usually it's a problem in stochastic dynamic programming. How many data science people know that? I do.

Here's one: A company wanted some revenue projections. AI, ML, data science? F'get about it! What was known? (A) The current revenue. (B) The revenue at the planned maximum share of the market. That was it!

Okay, assume that over time, always there are (1) some customers being served and (2) some target customers yet to be served. They talk to each other. So we have a case of viral growth. Assume that the rate of growth is directly proportional to the number of customers in (1) and to the number of target customers in (2). So, let t denote time in, say, days, and y(t) be the revenue at time t. Since we know the current revenue, that is y(0). Let b denote the maximum revenue. Then at time t, the number of customers being served is proportional to y(t) and the number of target customers is proportional to (b - y(t)). So, at time t, the rate of growth is the calculus first derivative of y(t), that is, y'(t) = d/dt y(t). So, we have that for some constant of proportionality k

y'(t) = k y(t) (b - y(t))

That's an initial value problem for a first order linear ordinary differential equation. Yes, there is a closed form solution. Find that and consider some other qualitative items, pick a value of k, and that's the projections.

Clean enough. Justifiable.

Get a lazy S curve that roughly approximates a lot of viral growth examples back to, e.g., TV sets in US homes.

Okay, I did that on a Friday. The next day, Saturday, was a BoD meeting. The graph of the revenue projections was presented. Two representatives of Board Member General Dynamics asked for the projections to be justified. That was at about 8 AM. By noon or so, the General Dynamics guys had lost patience with the company, returned to their rented rooms, packed their bags, obtained airline reservations back to Texas, and as a last chance returned to the BoD meeting. Then a C-level guy guessed I'd done the projections, made some phone calls, found me in my office, and asked if I could come to the BoD meeting. I came, explained the projections, used an HP calculator to do the calculations of y(t) for several values of t, and reproduced the values on the curve. The General Dynamics guys stayed. Had they left, with their pending equity check, the company would have died. So, I saved the company. And I was the only one at the BoD meeting who could understand the differential equation. The company was FedEx, and I was Director of Operations Research. That was the second time I saved the company.

I've done good work on a lot of analytical projects.

No doubt the situation now is much the same as when I did those projections: Such work can be really important, and not many people in business know how to do it.

So, yes, there are some specialized things I know how to do.

For computing more narrowly, I just wrote 24,000 programming language statements using Microsoft's .NET Framework, ADO.NET, and ASP.NET. For a Web session state store, I wrote my own. I also did all the design work. Such things are current and popular now.

But, having people regard such a background as useless is not good. For me, a much better path is just my startup. For that startup, I'm a sole, solo founder, 100% owner, doing all the work. My qualifications are just fine for my ambitious startup, in information technology, with those 24,000 programming language statements, and apparently new algorithm or two, and some really nice, crucial, original applied math. Curious that my qualifications are no good for anything else!

I had no trouble doing the work in the IBM Watson AI group: I published along with the rest of the group, but I also as a sole author did by a wide margin the best research of the group. As I was walked out the door, the claim was that that work was "not publishable". That was a deliberate lie. Well, I sent the paper in to a really good journal, and it was accepted and published relatively quickly and without revision. For just the software part, I did the best work done in the group and won an award. And I was our lead on our joint work with GM Research and wrote the paper we gave at an AAAI IAAI conference at Stanford. Curious that my qualifications are not good now.

Have I written a smartphone ap? Nope. My startup will work fine on smartphone just via a standard smartphone Web browser. But writing smartphone aps is not nearly all there is to information technology now!