🎯 Mark Freeman II

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

Tech Lead, GTM Engineering

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

Hannes Mühleisen (co-founder of DuckDB) doing his slides like SQL queries is so damn cool.

The most powerful communication is often what's not said.

Dev 🤝🏼 GTM | O’Reilly Author | [in]structor (33k+ Learners)

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Joe Reis recently inspired me to consider Claude Code as a writing tool when he challenged the notion of context graphs.

He essentially took the Foundation Capital article that has been going viral and ran it through Claude Code.

It instantly transformed a very theoretical article into an entire code repo you could explore on his public GitHub (then used that as a tool to critique).

A thought then popped into my head:

"What if my writing was so clear that you could throw an entire article into Claude Code and it would one shot a working POC of the presented ideas."

Welp, the implementation chapter of our book (Ch. 7) is public on GitHub and is stored as a README file.

So let's see if my writing is clear enough for AI to one shot it.

I downloaded the repo to my local machine, got rid of every file except the README, the JSON dataset, and a folder of diagrams from the chapter.

Then I prompted: "This is my technical chapter of the book. Implement it."

I couldn't believe it... In about 10 minutes it was spinning up a Docker image, loading data into Postgres, and then running my tests.

Keep in mind I spent a couple intense weeks building that codebase for the book.

To be clear, I'm not advocating for such code to be useable, but rather it's now a tool that I can use to further validate my writing.

Present your novel ideas.
But show the working POC.

It's the same as in academia where you have research papers that include code and data for repeatability.

The bar has risen for everyone now. Talk is cheap.

Crazy Idea: I use claude code on a live stream where I'm building an app from scratch.

But...

People on the live stream can enter prompts on my behalf while I'm building to either help or mess with the project.

I think the chaos would be both fun and hilarious.

You didn't hear it from me, but the coolest talk at Data Day Texas is happening at 2:55PM in room 204 👀

Live coding exercise for my conference talk at Data Day Texas? You bet!

Saturday - 2nd Floor - Ballroom 204

Code: The Untapped Metadata Source Driving Most Data Failures

Over the past three years, collaborating with organizations implementing data contracts and writing the O'Reilly book on the topic, it has become abundantly clear that data is surprisingly the wrong surface to address systemic data issues —we actually need to focus on code. Code not only defines what data is captured and moved through software systems, but it's also the ultimate source of truth in how a business operates and its documented changes over time. This is evident in how some of the most significant internet outages can be traced not only to code changes, but also to how those changes affected underlying data models. In this talk, I share case studies and lessons learned in extracting metadata from code and using it to enforce expectations across software systems and data products.

Remember back in the ancient internet times when you could use this cool thing called HTML to customize your Myspace profile.

Just randomly hacking away at sections to see what changed. Copy and pasting random code and hoping it works.

Vibe coding feels eerily similar.

What data scientist experienced in 2023 with the realease of ChatGPT is the same experience software engineers are experiencing today with Claude Code.

Back in 2022 I was transitioning from data science to data engineering, and a major project that led to that promotion was building and deploying a natural language processing pipeline.

Back then I was using SpaCy, which was an opinionated framework for implementing the latest production-ready NLP models.

Yeah... I open up ChatGPT and immediately see the "advanced" work I put into production was now antiquated.

While I wouldn't throw an LLM at my production project back in 2023, I was intimately aware of what was required to get business value out of text.

What ChatGPT showed me was that a large portion of the difficult parts of NLP were now just a simple API call... and more importantly, highlighted the inflection point if the momentum of change followed.

Fast forward to today, and so many software engineers built side projects over the holiday break and experienced the exact realization I had back in 2023.

"This LLM accomplished way more than I expected, and I can see an inflection point if this trend continues."

Imagine what will be accomplished in the next few years.

Vin Vashishta

AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

I have helped over 9,000 senior++ ICs and executive leaders advance and stay relevant with high-end AI capabilities. Courses & Certifications: https://datascience.vin

Upskill your workforce to be AI-ready in 3 months: seminars@v2ds.com

Conference talks, brand partnerships, and thought leadership: partnerships@vinvashishta.com

Over 85% of AI projects fail to deliver a return on investment because they start with technology, not a business problem. I’m fixing that by empowering companies and training people to stop experimenting with AI and start monetizing it.

Define, align, and manage the business and technical transformations that turn C-level AI visions into products, productivity, and revenue. Since 2012, V Squared AI has worked with large enterprises to deliver over $4 billion in value.

My frameworks are built from working in the trenches. My practitioner-to-strategist journey allows me to bridge the critical gap between technical teams and the boardroom, ensuring that AI initiatives are not only technically sound but also commercially viable.

Partners & Disclosures: Dell Pro Precision Ambassador, NVIDIA Partner, & Gartner Ambassador.

Speaker’s Bio:

Vin Vashishta is an award-winning AI thought leader, best-selling author of ‘From Data to Profit,’ 3X founder with a successful exit, and CEO of V Squared AI, one of the first pure play data and AI consulting firms. The Wall Street Journal, Fast Company, and The Information have showcased Vin’s AI expertise.

He brings 30 years of experience in leadership, technical strategy, and hands-on technical product delivery. He has trained, productized, and monetized information and AI for 15 years and previously worked in software engineering and organizational leadership roles.

Founder & AI Advisor

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

Technology teams solve technical problems. Business teams solve business problems. If the technology being created doesn’t deliver value, that’s a business problem and the business’s fault. That mindset got me laid off twice.

It’s why Yan LeCunn got pushed aside at Meta and why thousands of technical leaders are being sidelined at other businesses. When the technology delivered does not create the value that business leaders expect, they rarely take the blame.

I’m business, product, and monetization-first today, but I wasn’t always. I used to think that value wasn’t my problem until the second time I was the one who dealt with the consequences of bad technical strategy choices.

Excluding firms that are building the AI industry, US companies spent an estimated $86 billion on AI in 2025, according to IDC. This year, IDC estimates that AI spending outside of tech will reach $131 billion.

In 2026, that spending comes with heightened expectations. Investors will be asking non-tech companies tough questions, and that pressure trickles down to every part of the business.

“To me it’s…the most important question that you should be asking a company because if they don’t have a strategy for this (AI), they’re probably going to get left behind.” Heath Terry, Global Head of Technology Research at Citigroup.

Accountability for value should start at the top, but as we’re seeing at Meta and many other businesses, it typically hits technical teams and their leadership first.

Technical leaders must be in the room where strategy is decided. They must become strategic partners, not just operational and tactical order takers.

Technical teams must ask tough questions about how every initiative creates value for the business. They must be more value-centric than ever and push back when prioritization strays from driving growth and cost savings.

Don’t rely on another team to take ownership for value creation. Most in the business don’t understand the AI and agentic monetization paradigms. They don’t see the right opportunities, so they can’t connect technology with value creation.

Most don’t know the difference between use cases where AI will generate significant returns and AI cost centers where AI is the wrong technology to implement.

Businesses need technical teams and leaders who can turn AI, information, and agents into top and bottom-line impacts. Don’t be afraid to step forward because we’re the ones who deal with the consequences when it doesn’t.

At CES 2026, I saw the tech world’s vision for the future of work. It’s bleak for traditional jobs, both physical and knowledge work. By 2028, Hyundai will produce 30,000 humanoid robots annually.

Robots will outnumber people in most of its factories by 2032, and that’s where nearly all major manufacturers are headed. Warehouses at Amazon and Walmart have already started a similar transformation.

A head chef will oversee more robots than people in restaurant kitchens that are being redesigned for this transformation. A head waiter will manage a fleet of food delivery robots to serve even the busiest restaurant.

AI kiosks of every shape and function will replace attendants, with a single person supervising high-traffic entryways, like those at Disneyland. Autonomous checkout will replace most cashiers.

AI will generate synthetic data for new AI to be trained with. More AI will orchestrate the agentic development lifecycle. Simulations will test and improve the models that automate all those robots, cars, and kiosks.

The data centers that people are building today will be put up much faster by construction robots overseen by a handful of construction workers.

Everyone I spoke with said the same thing: “It’s not that the robots don’t work today. We can’t produce them fast enough or at the right price point, yet.” Robotics will see a similar boom in the next two years as data centers are seeing now.

That’s why metals and other resources are being stockpiled, and their prices are skyrocketing. Most companies are preparing to provide the next gold rush with robotics picks and shovels.

We should be reskilling people now. The future of work requires more people who can repair and maintain all this AI and the new AI hardware platforms. We need people who can manage AI workers.

It’s important to be proactive here because reacting after job losses take hold isn’t a version of reality we want to experience.

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🎯 Mark Freeman II

Vin Vashishta

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taplio

The Battle

🎯 Mark Freeman II

Vin Vashishta

Followers

Posts

Engagements

Likes

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);}🎯 Mark Freeman II's LinkedIn Performance

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Performance Score🎯 Mark Freeman II's LinkedIn Performance