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.

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Paolo Perrone

They told you 70B models need 80GB VRAM. They lied.

AirLLM runs Llama 70B on a 4GB GPU.

No quantization. No distillation. No pruning.

The trick: layer-wise inference. Instead of loading the entire model into VRAM, it loads one layer at a time from disk.

Your GPU only ever holds one layer. The rest lives on disk.

What you can run:

→ Llama 70B on 4GB VRAM
→ Llama 405B on 8GB VRAM
→ Qwen2.5, Mixtral, ChatGLM all supported

pip install airllm

from airllm import AutoModel

model = AutoModel.from_pretrained("garage-bAInd/Platypus2-70B-instruct")

input_tokens = model.tokenizer(input_text,
    return_tensors="pt",
    truncation=True,
    max_length=128)

output = model.generate(
    input_tokens['input_ids'].cuda(),
    max_new_tokens=20)

Standard transformers API. That's it.

Optional: 4-bit compression for 3x speed boost.

model = AutoModel.from_pretrained("...", compression='4bit')

Trade-off: slower inference than full VRAM loading. But if you don't have the VRAM, you don't have options.

Works on MacOS with Apple Silicon too.

1.9k GitHub stars. Apache 2.0.

→ github.com/lyogavin/airllm

💾 Save for when your GPU budget doesn't match your model ambitions
♻️ Repost if you've ever closed a tab because "requires 80GB VRAM"

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝

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The Battle

Vin Vashishta

Paolo Perrone

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);}Vin Vashishta's LinkedIn Performance

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Performance ScoreVin Vashishta's LinkedIn Performance