How will business run in 2030
Peter Tippett
Building the Framework for AI Agencies while building our own to test it out
The Future of Business Transformation: Where are we at now
I’ve been using AI for a while now, and I wanted to reflect on its impact and where it stands today, especially for me.
The Good
From an engineering perspective, it’s been a huge help, particularly when I’m coding in my usual language, Go. Since I’m familiar with the language, I can easily determine if AI-suggested code is usable or should be ignored. I estimate that AI has increased my productivity by over 20%.
The real game changer for me, though, has been in writing. Programming is my first language, but English has always been a challenge—I actually failed it in school. AI has improved my ability to articulate my thoughts far more effectively. I often write a lot about what I’m working on, always grounding my thoughts in first principles, and tools like ChatGPT and Notion AI have been great for organizing and enhancing my writing. They especially shine when I ask for different perspectives on my work or need to pull in outside information for deeper insights. Recently, I tried Google’s NotebookLM with its podcast function, and it was impressive—it took a whitepaper from our site and delivered an exceptional first draft, which helped inspire this blog the link to this is below.
One thing that’s clear to me is that Large Language Models (LLMs) are well-trained in handling written content and excel at synthesizing information. However, you still need to know what works and what doesn’t.
The Bad
Recently, I wanted to build a new front-end using the latest tools—Next.js, React, and Tailwind. While this isn’t my usual area of expertise, I’ve done some JavaScript-based work before and even used Wix. I figured it wouldn’t take long to get up to speed with these tools, especially with AI to assist, since I’ve worked with Next.js in the past.
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I was hoping to find a GUI interface to help with the views, one that would align well with the underlying code. After a few days of experimenting with different AI tools, I realized that front-end development is still being done the traditional way—designing the UI, and then handing it over to an engineer to code, just as I do with Go.
Even when I found tools that seemed promising, they were often built using outdated code, relying on last year’s code rather than the latest models and the code they built was just too complex to manage and not as refined as hand-done code.
This makes sense because the coding landscape evolves rapidly, unlike word-based models, which remain relatively stable.
So now it is back to coding the old way as I learn and once I have enough knowledge then will bring the AI in to be my co-writer.
Final Thoughts
In the end, I believe that software engineering will be more challenging for AI to keep up with due to the fast pace of coding evolution.
However, business processes haven’t changed much over time, which is why AI models are better equipped to provide valuable outcomes in that space. This fits the premise of this group of articles in why AI will help businesses to evolve.