Enhancing Productivity In The AI Era A Guide For Teams Of All Levels
Created March 31, 2026 · Last updated March 31, 2026
I believe that constructing a diverse team consisting of members from junior to senior levels is a critical factor in driving a company’s productivity. Successful companies will not eliminate junior positions; rather, they will structure them differently and focus on developing a range of vital skills in their developers.
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For context to this post, allow me to share that my first job in the software industry was back in 1977. Yes, that’s right, I’ve been around for a while. Over the years, I’ve founded multiple startups, employed hundreds of people, and navigated countless technology transitions. Even though I spent many of these years as a CEO, each startup began with my hands-on involvemet, putting together a proof of concept, writing code, designing architecture.
Now, even in retirement from my executive role in software, coding remains a passionate pastime for me. AI has emerged as a significant game-changer in my process. I’ve incorporated AI tools into my workflow as valuable new team members. In fact you will learn that I communicate with them much as I communicated with mid-level developers in the past. They’ve managed to increase my productivity by at least five to six times, possibly even more.
Let’s take a simple example. I’m a fan of the Joplin note keeping system, using it on a desktop Mac, a Microsoft Surface ARM laptop, and an iPhone. I use the paid Joplin Cloud service to sync all of my notes.
ChatGPT has become an indispensable research tool. Thus, I found myself frequently switching between ChatGPT and Joplin, copy-pasting text multiple times a day. While there are a few AI plugins for Joplin, none quite did what I wanted, so I built my own.
My first programming language was Fortran and over the years, I’ve used at least half a dozen different languages. Despite being relatively new to Typescript, with the help of Cursor.ai and ChatGPT-5, I managed to create a working plugin that met my needs in just about 4 hours. I treated the AI like I would a mid-level developer, supplying instructions at a similar level of detail as I would to a human developer. Just like a regular code review, if the AI seemed off-track, I simply suggested a new line of thinking.
Leveraging GitHub and frequent commits, I was able to create a safeguard against AI errors. If necessary, I could easily revert to an earlier commit, modifying my instructions or prompts for the AI to take a different route. This iterative process bears a resemblance to my experiences in managing development teams — guiding them through roadblocks and assisting them in moving to the next step.
Sure, I didn’t spend a lot of time pouring over the code, but there are various tools to help with that. After having a working version of the plugin, I switched my AI to Claude and asked it to review the code. Treating each AI as just another team member. Next, I switched back to GPT-5 and asked it to write and run unit tests. When these passed, I switched back to Claude and requested a review of the unit tests. Finally, I used Claude to check my code for potential security issues.
Without AI assistance, the process would have likely taken me a week or more, manually typing in all of the code, searching for coding examples, grappling with unfamiliar TypeScript syntax.
Now, I realize that this works for me because I have close to 50 years of experience. But how do we expect a junior developer to do this if we train them in the same old ways? We can’t. We need a different approach where learning syntax is not the be-all and end-all. Instead, the focus should be equipping them with analytical skills, honing their ability to compose effective prompts for AI, and understanding various development stages. In essence, we should teach them to do what I did.
I do not think that developers necessarily need to grasp the intricate technical distinctions between, say, a binary and sieve-style sort. Instead, their strength should lie in asking the right questions and giving clear, detailed prompts when interacting with AI. For example, given ‘x’ volume of data and given that Javascript is running in the browser, they should ask:
- “What’s the most efficient way to sort the data?”
- “Should we be using a standard Javascript module or implementing our own custom sorting function?”
- “Can you create a checkpoint mechanism to ensure we do not run out of memory during the operation?”
- “What’s your suggested approach for testing the sorting component for correctness and performance?”
- “Could you devise a mechanism to integrate timing logs into the code to provide real-time performance insights?”
- “What edge cases should we consider during implementation?”
- “Can you generate a fallback strategy in case the primary sorting method fails?”
Through thoughtful, detailed prompts like these, developers can leverage AI optimally to design robust, efficient sorting implementations for their Javascript applications.
Just as Cobol, PL/I, Fortran, and MVS were fundamental tools when I was getting started, today’s technology landscape is dominated by AI, specifically large language models. These AI tools present an unparalleled opportunity to maximize productivity across all levels of a team. It’s crucial to understand that to maintain a balanced cost structure, an appropriate mix of team members — ranging from junior to senior — is needed. Therefore, regardless of their seniority, every member of a team must embrace and efficiently utilize these tools. It’s not merely an option, but a necessity in the current tech-dominated era.
P.S. To address the inevitable question — yes, I utilized AI to refine and clarify this post. I composed it in my Joplin notebook and employed the ChatGPT plugin, mentioned above, to assist with the editing process.