🦾Jepson Taylor

I build stuff that doesn’t exist, an AI explorer. 
Unfortunately I have too many connections so please follow me instead. 

人工智能 (Chinese)
人工知能 (Japanese)
कृत्रिम बुद्धिमत्ता (hindi)
الذكاء الاصطناعي (Standard Arabic)
কৃত্রিম বুদ্ধিমত্তা (Bengali)
искусственный интеллект (Russian)

I watch the watchers, I direct the learners. 

I am passionate about machine learning. My life's mission is to further the boundaries of what is possible with data science. I spend as much time as I can working with deep learning, NLP, and all forms of data science. I currently have a passion project I am managing where I am combining the power of genetic programming with deep learning to automate network design. I think there is a trend happening in data science where human expertise and creativity are being replaced by powerful new methods (i.e. deep learning, big data). 

I am most well known for my machine learning work with the data science community and with HireVue. With the community, I enjoy posting opinions on jobs and tutorials on ML methods. I also enjoy speaking and presenting to all types of audiences. 

I've enjoyed working across engineering, product, sales, marketing, and customer success. This wider POV has helped shape the way I think about innovation, from the innovation farm to the customer's table ;)

CEO/founder VEOX

VEOX Inc is revolutionizing the AI industry by creating an AI-driven algorithm factory, compressing decades of progress into moments. Our technology automates AI science research, continuously improving algorithm efficiency and outperforming human-made algorithms. VEOX is not just building another AI company; we're catalyzing an explosion of technological advancements that will reshape the entire tech landscape. Join us in accelerating intelligence beyond human limits and leading the next wave of AI breakthroughs.

Jepson Taylor

Joe Reis 🤓

Chief Operating Officer

Best-Selling Author | Data Engineer and Architect | Recovering Data Scientist ™ | Global Keynote Speaker | Professor | Podcaster & Content Creator

I write. I build. And I’m working on something new for the data community.

More soon. ✍️📚⚡️

Ternary Data are veteran data engineers and “recovering data scientists” who have firsthand experience with the challenges of succeeding with data. We offer practical data engineering advice.

"I’ve had conversations this week with data engineers, founders, and investors. The universal vibe is that something changed in the last month or two.

I’m not talking about a gradual improvement. I mean a big change. The models got really good, seemingly overnight. What used to be a decently capable coding assistant that could write passable SQL is now producing production-quality work: pipelines, configurations, strategy documents, the works. Every new point release seems to surprise even people who use these tools daily. And I use and abuse them daily.

Multiple friends have independently told me the same thing: “I don’t know what happened, but something shifted.” The models crossed some threshold where the gap between “neat demo” and “this replaces actual work” effectively closed for a huge swath of tasks.

If you’re not feeling this shift, it’s probably because you’re not using the latest models. And if you’re not using the latest models, I hate to be that guy, but you’re woefully behind and out of step. Use the latest models ASAP."

Read more:

Joe Reis

Open article: The Reckoning Is Already Here by joereis.substack.com

"Data modeling is in trouble. But perhaps not for the reasons you might suspect.

That’s the clearest signal from the Practical Data Community 2026 State of Data Engineering Survey. We asked 1,101 data professionals about their tools, challenges, and outlook. The section on data modeling stopped me cold. 89% report at least one pain point with their modeling approach. Only 11% say modeling is going well.

Is this a technology or skills problem? Not according to the data. The top complaints are organizational: pressure to move fast (59%) and lack of clear ownership (51%). Data modeling is failing because organizations won’t give it the time and attention it requires."

TL;DR - it's your boss's fault ;)
Read more:

Open article: The Organizational Crisis in Data Modeling: Why 89% of Engineers Are Struggling by practicaldatamodeling.substack.com

The Organizational Crisis in Data Modeling: Why 89% of Engineers Are Struggling

"If the data industry has been teaching and preaching the same way for four decades and the vast majority of practitioners are still struggling, clearly the approach hasn’t worked. At this point, we cannot keep blaming the practitioners. Beating people over the head and telling them they’re stupid for not adhering to 40-year-old practices, or dogmatically blaming them for not rigidly following data modeling methodologies is unhelpful at best, and gatekeeping at worst...

Doing the same thing over and over again and expecting different results is the literal definition of insanity. Yet, the data industry keeps insisting that if we just shout louder at people, they’ll finally get it."

This weekend, I get a bit more unhinged than normal. But this needs to be said.

Read more:

Open article: The Insanity of Data Education by joereis.substack.com

When I first started writing my book on data modeling, I felt like the data modeling world was stagnant and tribal. For the past few years, I’ve been giving talks around the world on data modeling, articulating it in various forms. Reflecting on my enthusiasm for combat sports, it finally dawned on me that Mixed Martial Arts already had the template for where the world of data was heading.

This brand new chapter sets the stage for why the old ways of data modeling are failing and officially introduces the concept of Mixed Model Arts (the title of my upcoming book).

Preview here (fully available for paid subs): https://lnkd.in/g6DgrPkr

One of my biggest pet peeves on LinkedIn... I can't believe I have to keep posting about this.

Do you have ODBC Stockholm Syndrome?

We’ve been using ODBC and JDBC for decades. We’ve accepted that moving data from a data warehouse to a dataframe is slow. We’ve accepted the "serialization tax" of converting columnar data into rows for transport, only to convert it back to columns again on the other side.

But why are we still doing this in 2026?

I sat down with Ian Cook (Co-founder of Columnar and core Apache Arrow developer) to discuss ADBC (Arrow Database Connectivity), the new standard poised to finally replace our legacy row-based protocols.

Watch here: https://lnkd.in/gYnv7Pye

Open video: From ODBC to ADBC: Modernizing the Data Stack for AI and Analytics w/ Ian Cook on youtube.com

From ODBC to ADBC: Modernizing the Data Stack for AI and Analytics w/ Ian Cook

My friends at bauplan are giving a lunch and learn about next-generation lakehouses for AI agents, their new Claudio AI project, and more. They're definitely some of the sharpest people in the data biz, so you should check this out if you can.

When: Wednesday, Feb 18 at 12pm MT
Where: https://lnkd.in/g4_qmP6z (Google Meet, also streamed live on YouTube)

Utah AI/ML and data nerds! Have we got an event for YOU!

Kyle Nesbit (CEO of Credible and BigQuery OG) is traveling all the way from Colorado to talk about giving ML and AI systems meaning. What do I mean? Here's the blurb from Meetup:

Modern ML and AI systems are great at generating information — but they’re surprisingly bad at understanding what enterprise data actually means. Business logic, undocumented assumptions, and tribal knowledge are scattered across one-off SQL and people’s heads — and often never make it into models, agents, or applications at all.

This talk dives into Engineering Meaning: a new systems approach for turning business intent into a first-class, versioned, testable asset — and delivering that meaning in context to AI systems, applications, and analytics.

See what I mean? It's pretty cool. You'll learn a lot, and maybe meet some decently cool people too. Only one way to find out 👇 

When: Tuesday, Feb 24 at 6:30pm MT
Where: Weave, Lehi

Signup here:

Open article: Giving Data value to AI, Tue, Feb 24, 2026, 6:30 PM | Meetup by meetup.com

Giving Data value to AI, Tue, Feb 24, 2026, 6:30 PM | Meetup

One thing I love doing is speaking to students. Kishore Aradhya's data engineering class is always a treat.

As part of my graduate class in Data Engineering at Northeastern University this semester, we kicked off the guest lecture series with Joe Reis.
 
Joe consistently inspires my class to think differently and always from first principles. His call to adopt, lean into, and leverage AI coding tools is particularly relevant, as it's transforming the development landscape in a fundamental way and reshaping expectations for entering the workforce. He presented some very real, practical approaches, discussing specific examples of what he is seeing being played out with some very senior developers in this space.
Joe offers a unique perspective that resonates deeply with the students. Over the last six semesters, he has graciously served as a guest lecturer, fostering engaging conversations and enriching their learning experience. 
 
Our second inspiring guest last week was Veronika Durgin, VP of Data at Sack's. She recounted her journey over the years, including how she fell in love with SQL (coincidentally, it was Valentine's Day) and working with data - the continuing fundamental challenges in data governance, data management, and data quality, and how all of these are even more challenging and acute as we race to enable, adopt, and accelerate AI across the enterprise. She offered a unique perspective on how this is playing out in century-old organizations like Sacks with deep legacy footprints. Veronika's unique, down-to-earth style and approach have always resonated with my students, making her message and lessons relatable.
 
Looking forward to more great guests as I continue teaching Data Engineering, realizing how its importance has grown dramatically over just the last couple of years.
 
#Northeastern University MGEN #DataEngineering

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Joe Reis 🤓's LinkedIn Performance

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