About I4M

Education

Hi, I'm Levi Johnston. I have an associate degree in Internet Programming, and a bachelor of science degree in information technology with a focus on web development. During this time, I took an interest in data - building data ETL processes, aggregations, fine-tuning and collaborating with business leaders to find the right KPIs to nudge employees in the right direction to align with company vision/mission statement, though my interest in programming, and side-projects still had me programming in a variety of languages. I discovered that through the practice of many of my hobby projects and self-education I was equipped to pass nearly all of the tech degree curriculum without the classes. I decided to challenge myself and earned a master degree in business with a focus on technology management. This gave me a deep understanding of leading, strategy, competition, risks, and operations.

AI introduction

At this time, my work shifted from aggregations, to revamping old financial processes and using advanced queries to pinpoint files at-risk of being out of compliance. I was leading a team of compliance analytic developers, but was keeping tabs on machine learning papers, and practices because of how interesting it was - though very entertaining, very little actionable material to apply. Improved object recognition, Entertaining images, Target finding out a man's daughter was pregnant before he did, etc.

Generative AI Teething Years

Many technologies emerged during this period. Inputs and outputs for text, image, video. The demos were impressive, but actual use of the final products left something to be desired. I kept the Turing Test in the back of my mind - when will will they be able to put a human tester-to-human chat and a human tester-to-LLM chat and the tester wouldn't be able to tell the difference more than random chance? That is when the final product will be as good as the demos, and that is when the final product will be more than a search engine replacement or a simple toy.

AI Competence and Prime Market

Frontier models are now passing the Turing Test! Every day there is a new technique of getting the most out of AI technologies, and new, more capable, models from big tech companies are emerging monthly.

$500 billion - the amount expected to be invested in just the AI infrastructure in 2026.

1.6% - the percentage of GDP expected to be invested in AI infrastructure in 2026. (500b/31.8t)

0.4% - The percentage of GDP used during peak funding years for the Apollo Program and the Manhatten Project.

I have been keeping tabs on AI/ML/LLM for more than a decade. Now is the time to invest in upskilling your employees and now is the time to figure out how this massive outlay of new infrastructure can help your company stay ahead of the curve. The entire world is investing in their employees, which means your competitors are too. Some sage advice for business leaders: keep up, or get left behind.