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.

I create content about deep learning | The DevRel and Deep Learning Dude

Hacking on LLMs, curating multimodal datasets, and writing a book about RAG.

Harpreet Sahota 🥑

i've literally been waiting for years to have an OPEN SOURCE model like qwen3-vl-embedding

i tested its multimodal retrieval capabilities on a corpus of 412 short video clips and the results literally blew my mind

here are the queries i tested:

1. a cartoon guy drinks merlot wine

i like this query because we see how it can retrieve based on semantics (a cartoon), text (the label merlot), and temporal action in the video (the cartoon guy drinks the wine mid-way through the video)

2. garfield runs out of the door

semantics are awesome here, the model knows i'm talking about the cartoon character...

3. a woman in a blue dress sleeping on a red bench

the skeptic in you might think that it's retrieving just based on the red color block in the 2/3 of the video...but notice the specific part of the query "a woman in a blue dress..." and this is only shown in 3s out of the full 10s

this is such a huge release 

and it's gonna open up SO much more for multimodal video retrieval this year

on my wishlist is natural language search of pcd datasets, who gonna ship that?

you can hack around with the model using the resources below

check out the docs: here: https://lnkd.in/gh7yfBNP

and the quickstart nbhere:  https://lnkd.in/gbinsx7p

🤖 Hacker-in-Residence @ Voxel51| 👨🏽‍💻 AI/ML Engineer | 👷🏽‍♀️ Technical Developer Advocate | Learn. Do. Write. Teach. Repeat.

for those who missed it, or were there in-person and know the trouble i was facing 😆, my ODSC webinar is now live on youtube!

in this session i discuss visual ai techniques for automotive damage detection. 

i used the cardd dataset, which is a public dataset built specifically for vehicle damage analysis and demonstrated how fiftyone helps you explore, visualize, and build models to detect dents, scratches, cracks, glass shatter, tire flats, and broken lamps.

i feel like its a super practical workshop with some insightful workflows for visual AI in automotive settings, but as the creator i might be biased.

only one way to find out for yourself, watch the webinar!

https://lnkd.in/ewfz5vZD

Open video: Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota on youtube.com

Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota

what happens when you combine siglip2, dinov3, and sam3 into one model? 

you get c-radiov4

NVIDIA AI's new model distills these three specialized teachers into a single efficient backbone.

it doesn't do classification or segmentation out of the box. 

instead it gives you rich feature embeddings and attention maps that serve as a foundation for any downstream task you want to fine-tune for.

trains across resolutions from 128px to 1152px with stochastic resolution training and shift equivariance to learn robust representations. 

now integrated with fiftyone for extracting embeddings, similarity search, attention visualization, and dataset curation.

use it as your feature extractor for custom vision tasks under permissive nvidia open model license.

sometimes i look back at the shit i get done and i genuinely impress myself

how teams really build Visual AI in the wild? what's the he messy reality?  

i don't know...and maybe its because nobody’s asking practitioners and compiling their answers so we can find out the landscape actually looks like. 

so, we at Voxel51 are kickin off the...

wait for it...

 State of Visual AI survey...vey...vey

for those out ther building visual AI systems i want your hot takes

what’s the hardest part about making visual ai a reality? 

let us know in this survey

it'll take you 30 minutes and is a much better use of time than baby sitting your runs and staring at your loss curves

i can't offer much beyond my heartiest thanks, but you will get early access to the full report with benchmarks, common pain points, and where you stand within the landscape. 

here's a link: https://lnkd.in/gnsADACy

MedGemma 1.5 supports detection, but for best results, you'll need to fine-tune. 

there's a kaggle competition using the model, i created a starter notebook to give you a jump start on how to fine-tune it for detection

checkout the starter notebook and additonal resources 👇🏼

cc Omar Sanseviero Megan Risdal Logan Kilpatrick

in the asu area and wanna get started with open source contributions? then don't miss this hack

On March 21, 2026, we’re hosting Agents World: Visual AI Hackathon @Arizona State University, together with Pavan Turaga 🚀 

This is a hands-on day: meet people, build with AI, eat pizza, and leave with something you created after a short workshop and a full day of hacking. 
You’ll work with the FiftyOne ecosystem and real models used in practice, like Qwen, Gemini Vision, Sharp, and more. 

If you’re an ASU student who wants to build, learn by doing, and work with real Visual AI tools, this is for you.

Registration is open:
https://lnkd.in/gBCHW5Fg

Hope to see you at ASU. I’ll be there with the amazing Voxel51 team:  Sid Mehta, Ritchie Martori, Jimmy Guerrero ! 🙌

looking forward to the next meetup in boston!

Join our in-person meetup at the Microsoft Research Lab at the Massachusetts Institute of Technology in Boston, Massachusetts on Feb 18 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Register here: https://lnkd.in/gi4kpSMB

Talks will include:

• SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

• Culturally Adaptive AI - Anku R. at Massachusetts Institute of Technology

• Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

• Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at Massachusetts Institute of Technology

• Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

#computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

Syed Abdul Sami Rizvi, Matthew Ebisu and 118 other attendees

View event: Feb 18 - Boston AI, ML and Computer Vision Meetup

Feb 18 - Boston AI, ML and Computer Vision Meetup

Inspired by the amazing Hugging Face’s 2025 AI Timeline, we created one for the Fiftyone community. The FiftyOne Community Development Timeline highlights more than 131 contributions from 2025, including datasets, models, plugins, and integrations.

You can filter by type or modality and jump straight to each integration.
https://lnkd.in/ev2kpCGN

Big thanks to the entire Voxel51 community for building all of this together.
Harpreet Sahota 🥑, Daniel Gural, Paula Ramos, PhD, Jimmy Guerrero, Derek Van de Streek, Nicholas Lotz 🚀

Gonna be a great series of talks, lots of technical depth!

Chief Community Officer @ Voxel51 | Startup Advisor

Join us on Feb 18 for the Boston AI, ML and Computer Vision Meetup! Pre-registration is mandatory to clear building security - https://lnkd.in/gHtBuH49

Talks will include:

* SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

* Culturally Adaptive AI - Anku R. at MIT

* Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

* Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at MIT

* Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

***********

Want to build better computer vision models? FiftyOne is an open source toolkit from Voxel51 (our Meetup sponsor) that helps you curate datasets, evaluate model performance, visualize embeddings, catch annotation errors, and eliminate duplicate images—all in one place.

“pip install fiftyone” is all it takes to get started - https://docs.voxel51.com/

#computervision #ai #artificialintelligence #machinevision
#machinelearning #datascience #physicalai

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