Vikram Vaidyanathan retweeted
If you are a CIO or CISO, pay attention to what @nikesharora just said: "Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability."
Human bandwidth cannot remain the last line of defense for enterprise IT and Security.
Hybrid patching and modern SOAR only work if the enterprise IT service management layer underneath can actually coordinate, verify and remediate at AI speed without creating new risk as new grants, approvals, requests and changes are deployed.
Most teams simply do not have the capacity to do this consistently. That’s the gap.
We need AI Workforce for Enterprise IT and Security.
It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and security infrastructure, no one solution works. Let's break it down:
1/3
1. Vulnerabilites are being found and will continue to be found as models get more capable, the fragmented nature of the effort denies us the opportunity for a comprehensive find and replace option.
2. Even if all vulnerabilities could be found instantly and patches available - we will be reliant on the ability of enterprises to deploy all these changes at speed. It is not clear what the collateral impact of all these patches will/could be to our infrastructure of deployed en masse.
We will need a hybrid patch approach.
3. Virtual patching(VP) is needed in conjunction with direct patching but for a comprehensive (VP) solve you have to be lucky enough to have "modern frontier defenders" (Cyber companies that have vaulted infrastructure that can create patches at speed and push content rapidly to protect their customers) you need them for your network traffic, your traffic to applications on the cloud, your WAF, your end point.
4. There will continue to be patches that will need to be deployed directly but virtual patching will take the heavy load off.
2/3 Medium term solve:
Unfortunately token economics and current cyber architectures will not allow for defender swarms to contend with an offensive attack launched using agents. (Some x pundits who aren't steeped in cyber should take heed). Yes, theoretically we should aspire to have these agentic swarms defend us when we are under attack, unfortunately an attack is a sniper moment, chained vulnerabilities, cloaked attack followed by swift exfiltration. It's a speed program not a brute force problem.
To solve for this, enterprises need to get their SOC architecture right, logs, feeds, cyber products orchestrated via a modern SOAR for swift response. Only then can we deploy agents to detect and remediate. In addition, enterprises need to modernize and dare I say platformize their perimeter stack to ensure consistency.
Unfortunately, enterprises still seem to not see the urgency across the board, decisions are stuck, CIO/CISO's have a lot on their plate. While some honestly have bit seen the scale of such a problem before and it's hard convincing the organization that one needs to open the floodgates on modernizing the IT and security infrastructure. So we will continue to see environments being brought down to patch today it's for PHP, tomorrow it will be for the next one.
3/3 AI deployments are the"wild wild west" - LLM capabilities are expanding faster than we can build products to secure them. Sure an agent will jump across guardrails in a research lab, which is a lesson for all of us to deploy agents securely. But there is a lag between the discovery, the availability of that in a public model and the time needed for the cyber industry to build governance or an antidote. And no, dear frontier lab friends, the world cannot just trust you to build the solve. The infrastructure industry in tech builds and the cyber industry secured and provides assurance to the boards and organizations around security and safety.
AI security is the new attack surface. CEOs (present company included) want AI adoption, getting the pace of adoption wrong could be the last decision we make, at the same time "deploying it wrong" could have the same consequence.
There are billions of dollars being spent on coding, is your coding harness secure? Start there. If agents can jump guardrails, they can hide stuff in code.
Validate business outcomes needed, test your user case and ensure agentic deployments are well guard railed. Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability.
3/4
Across Confido's customers:
2–3x more patient encounters
98% of pending tasks completed
20–30% monthly cost savings
Full conversation on Intelligent Indians: youtu.be/5okmBLZnXms
@avnish_bajaj @VikramV23 @Ashwinkannan
Privileged to be leading Confido Health's Series A. Having known Chetan, Vichar and the team for a while, we were impressed with their 15X growth in a year and their impressive word of mouth and win rates vs competition. Their AI agents (Sara, Ryan) now help 1 in 50 Americans, by providing front office staff to US healthcare, and a great example of labour workforce augmentation.
Great to see DeVC, @Together_Fund, Blume Ventures and Justin Saks double down alongside us.
@avnish , @RajatAgarwal167 , @rbalaraman, @assh2win, @RTinkslinger
We've raised $22.5M in total Series A funding for Confido Health, including a new $12.5M extension led by @z47_vc.
Medicine has added decades to our lives, yet patients still lose weeks to paperwork.
We're building AI agents to give healthcare teams more capacity to care.
Vikram Vaidyanathan retweeted
Oolka will exceed 2 trillion monthly input token consumption this month. We see this getting north of 50 trillion monthly runrate by 2027. Don’t write off India AI story yet.
We reported on an important story around AI and compute demand in India earlier this week.
Question to academics, founders and VCs- why is there no demand for compute in the domestic market? So much so that govt is now allwping those empanelled with IndiaAI to sell it overseas.
Is it a question of red tape (access), paucity of capital or lack of training/consumer AI startups that need compute
Vikram Vaidyanathan retweeted
HTML might be the new Markdown, as @trq212 pointed out early this year. But collaborating on HTML is still painful.
At @Razorpay, as agents started generating interactive docs and decks, we needed a way for humans and agents to collaborate on them in real time. So one engineer built SlashDocs. The rest of the team (and our AI engineer Slash) kept making it better.
Today, it has replaces google docs for 50%+ of all new docs at Razorpay, changing how humans and AI agents collaborate.
And now we're open-sourcing it!
We've spent years building tools for humans to collaborate. Now we need tools for humans and AI agents to collaborate.
AI agents are changing how we work and collaborate, but documents are still stuck in the pre-AI era.
Agents can now create beautiful, interactive decks, dashboards, and reports in HTML. But the moment you want to collaborate on them, everything breaks down.
Google Docs wasn’t built for rich, interactive HTML or AI agents. Feedback gets scattered across Slack and email. Every edit means going back to the agent, generating a new file, and sharing another link. No real-time collaboration. No single source of truth.
We built SlashDocs to fix this.
Think Google Docs, but built for AI agents and the documents they create. Host HTML and Markdown in one central hub. Share documents, manage permissions, and comment directly on the content, just like Google Docs.
Agents can read feedback and update the same document in real time. No duplicate files, no endless back-and-forth, and everyone stays in sync.
What started as a small internal experiment in one team has now scaled across @Razorpay. 28,000+ documents created. Over 50% of all documents generated at Razorpay now live on
SlashDocs.
And today, we’re opening it up to everyone. Self-hosted. Agent-agnostic. Fully open source.
AI agents have changed how we create documents. It’s time we changed how we collaborate on them.
Try it: razorpay.com/slash-docs/
Vikram Vaidyanathan retweeted
What a day it was for team @atomicworkhq .
Yesterday, Atomicwork Fusion’26 raised the bar for CIO and CXO gatherings on Tuesday morning in SF. The excitement around AI Workforce and AI-Native Operating Model was unmatched.
Grateful to all the attendees, my stellar crew of Atoms and an amazing set of speakers 🫶💜🙏
Vikram Vaidyanathan retweeted
Hello Dreamforce, Meet AI Workforce.
Atomicwork AI Coworkers are live across the Salesforce Transit Center!
Vikram Vaidyanathan retweeted
A special moment for @Razorpay today. 🇮🇳
Our CPO @khilanharia presented Vulcan - Razorpay’s foundation model for payments to Hon’ble Prime Minister Shri Narendra Modi today.
A proud day for everyone who built it. And we’re just getting started.
Built in India. 🇮🇳
A lot more to come! 🔥
4/4
His advice for founders:
"Don't become an entrepreneur by mistake."
Full conversatuon on our YT channel - youtu.be/ItKSY0m7n7Q
@avnish, @VikramV23, @_sameernigam, @PhonePe
Vikram Vaidyanathan retweeted
Replying to @VikramV23
@VikramV23 @avnish @RajatAgarwal167 @rbalaraman and I are back in the bay this week and hosting our (now almost annual) summer soirée in the city
Looking forward to hosting founder and investor friends on 31st August! 🥂
RSVP: luma.com/6j07znxl
Vikram Vaidyanathan retweeted
4 billion payments. 3 trillion data points. One model trained on all of it.
Meet @Razorpay Vulcan - India’s first transformer-based AI foundation model for payments.
Built, trained and hosted in India, in partnership with @nvidia and @awscloud.
Until now, every payments problem was solved separately: routing, fraud, risk, personalisation and more.
We asked: What if one model could understand how money moves?
And like LLMs are trained on text to understand language, Vulcan is trained on payments to understand how money moves.
Already running in beta across 51,000+ businesses, Vulcan is delivering:
- 8–10% improvement in payment success rates - the ultimate measure of whether a payment simply works.
- 8x more international card fraud detected.
- 5x more fraudulent or disputed transactions identified.
- 1–2 lakh more purchases completed every month through better checkout personalisation.
And we’re just getting started. The best part? Every payment Vulcan sees makes the next one smarter.
We’ve spent years building the infrastructure that moves money for India. Now, we’re building the intelligence that understands and improves it. 🔥
Vikram Vaidyanathan retweeted
We're on the 101. 🛣️
One billboard for what we do. One for who we do it for.
AI coworkers for IT teams and @Zuora 's IT team moving faster because of them.
Look up on your way to work.
@amnigos @kirandarisi @parsurv
Vikram Vaidyanathan retweeted
There’s something a lot of people have been asking us to do since the day we announced our first products 5 years ago! Taking that decision today. We’re opening our sales and service network to dealer partners across India today.
We started with our own stores to build EV awareness, establish the brand and created India’s largest EV 2W customer base of more than 10 lakh! Auto retail has a lot of local nuances and as the industry has matured and EV acceptance has risen, we are now evolving our approach. Now over next few months, we will evolve our company stores to focus on brand and product experience, while dealer partners will become the backbone of local sales, service and scale.
We have spent the last month engaging with dealers across the country and the feedback is unambiguous: Ola’s product leadership, combined with their local execution, can significantly accelerate both our sales and EV adoption. Their level of interest and energy to partner has been very exciting for us!
For partners, the opportunity is powerful - strong brand pull, a large recurring service opportunity from India’s largest EV 2W installed base, and a portfolio spanning scooters, motorcycles and our energy products like Shakti and other upcoming ones. Our model has been designed for strong dealer economics and returns. Given the level of interest, we expect meaningful on-ground scale in time for the Diwali rush!
BVR Subbu, former President of Hyundai India and earlier a member of our Board and someone who is a mentor to me, will rejoin as senior advisor and help our team execute this vision.
Learn more here and apply at olaelectric.com/partner-with….
1/2
How can India play the next phase of AI better? What are our constraints and unique opportunities?
India now has 100 million plus weekly ChatGPT users, with coding queries at 3x the global median and data analysis at 4x, and yet we sit at roughly 76th on per-capita penetration, which means the constraint has never been demand.
2/2
But we see gaps:
1. Structural: consumption has run well ahead of the infrastructure required to carry it, and adding GPUs alone does not close a gap of that shape.
2. Placement: edge and cloud are one system rather than two budgets, and India should be investing in the edge at the rate it invests in the cloud.
3. Physical AI window: the inferencing layer for physical AI is being built over the next few quarters rather. India can play uniquely here - we have the physical data the world needs across domains.
So, we have the potential to leapfrog in physical AI (like mobile before?)
Every wave so far, we entered late and still scaled the largest, because we've built unique, made in India, for India solutions, and the timing for that in the AI world maybe coming together.
Had a chat with Ravi Annavajjhala of NXP Semiconductors on some of these bits.
Adding the full conversation here: youtu.be/1615Co3t5e4
@z47_vc, @avnish, @tarun_davda, @RajatAgarwal167, @rbalaraman
1/2
Can you build a chip company from India and sell it to one of the largest semiconductor companies in the world?
Ravi Annavajjhala has done it. Kinara, built out of Hyderabad, was acquired by NXP Semiconductors for $307M.
And that story deserves a victory lap and huge learning for us as an ecosystem! People hear "AI chips" and picture one company in California and a GPU the size of a fridge.
But that's the training side building the brain. Ravi spent seven years on the other half, inference at the edge, where the model does its job on the device itself. His company Kinara designed those chips out of Hyderabad, and NXP Semiconductors bought it for $307M. He explained the whole concept wonderfully with one analogy.
2/2
Training is like a child working through multiplication tables with a teacher. Inference is the same child solving new problems on its own.
More than 90% of training happens in the cloud, and inference is now moving to the device: which is where I pushed him. The cloud gets cheaper every year and connectivity keeps getting faster, so who needs edge?
His answer runs on four things:
1. Cost: A chip is a one-time investment, against token caps and a bill on every inference call.
2. Latency: A self-driving car or an assembly line can't wait on a round trip to a data centre.
3. Privacy: Your data stays on your premise.
4. Reliability: The chip keeps working when the network goes down. Cost, latency, privacy, reliability.
India won't win the training war, and it doesn't need to. AI has a training half and a running half, and the running half, billions of inferences a day on devices, is the one I think India wins.
That's the layer this whole conversation turns on. Full video here: youtu.be/1615Co3t5e4
Vikram Vaidyanathan retweeted
Earlier this week my day was made looking at this chart on LinkedIn.
Great company to be in!
Also glad to see 5 customers in the list :).
@RocketlaneHQ is indeed killing it in mid market.
More to come!!




















