Devanshu Tiwari

AI & Business: Let's Learn and Grow Together.

[Boris Cherny @bcherny]

I talk to engineers at other companies every day and hear the same thing: one person is 10x'ing their output with Claude but the rest of the org hasn't caught up.

Watching teams adopt AI, I keep seeing the same 4 steps.

I mapped them out here: Steps of AI Adoption https://t.co/kQnRAUMKpP

[Devanshu Tiwari @Devanshu0055]

Congratulations to Pawan, Bharath, and the incredible team at Skyroot on achieving this historic milestone.

The entire country is proud of you.

P.S. I'm getting goosebumps watching the livestream from the library.

@PawanKChandana @SkyrootA

[Devanshu Tiwari @Devanshu0055]

Top 3 LLM in the World and their valuation.

1) @Kimi_Moonshot - $20 billion
2) @AnthropicAI - $965 billion (48.25X)
3) @OpenAI - $852 billion (42.6X)

[Devanshu Tiwari @Devanshu0055]

@Kimi_Moonshot Congratulations entire team of @Kimi_Moonshot and Big thankyou for supporting opensource community🙏

[Arena.ai @arena]

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.

This is a 17-place jump from Kimi-k2.6 (#18 -> #1).

In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.

The full model weights will be released by July 27.

Congrats to the @Kimi_Moonshot team on this major milestone!

[Aaron Levie @levie]

It’s truly wild that we’re getting this level of performance from open models. Congrats to Kimi team on this.

Every time we lower the cost of frontier intelligence, the use-cases that enterprises can take on just go up. There’s a tremendous amount of workflows that enterprises would love to deploy that are only gated by the cost of tokens.

Importantly for the startup ecosystem, the combined breakthroughs from open and closed labs enable a ton of value to accrue to the layer, which can leverage a variety of models to complete full tasks for customers.

This diversity of models and approaches means that the applied AI layer can tune models to their workflows and route intelligence appropriately. Huge win for all.

[Thariq @trq212]

ideal prompting technique is:
- thin prompts
- thick artifacts + context
- thin skills

[Demis Hassabis @demishassabis]

https://t.co/PTeDiv1b6L

[Chamath Palihapitiya @chamath]

When we look back, Alex Karp may have initiated an important preference cascade around AI sovereignty.

It’s worth noting what @Benioff and @satyanadella are both saying:

Your knowledge, as a company, is your sovereignty. If you lose it to someone else (anyone else) you are hollowing your organization out.

There are many ways to accidentally leak intelligence so you need partners and tools who can sign up for the complexity required to give it to you.

See Benioff below and see Satya’s essay linked below.

[Aaron Levie @levie]

A few thoughts on what we will see in AI structurally for the foreseeable future:

* Frontier intelligence continues unabated and pushes the industry forward continuously. The top labs will continue to buy the best and the most data, build the most compute, be at the forefront of improved training breakthroughs, and so on. A few different approaches stratify the market on pricing and capability, but overall competitive pressure brings down pricing on a per task basis. That said, we just ask more from the models over time - as one thing gets cheaper, we just use more - so frontier spend and use remains robust.

* Open weights rapidly absorbs frontier breakthroughs (and drives other breakthrough directions given the constraints), offering both lower cost intelligence and the ability to be post trained for specific workflows and domains. This creates a healthy counter balance to the frontier as you can run models “at cost” on a hyperscaler at any time, and tune models just for your tasks.

* The Applied AI layer has a huge opportunity to combine frontier intelligence with open or cheap closed models to orchestrate workflows in any given domain. Due to evals, deep domain context, being trusted with enterprise data and workflows, this layer can maximize performance and cost combination. The applied AI layer will also often have their own RLed models especially for high volume, predictable tasks in their systems.

* Individual enterprises will generally focus on their enterprise context, making sure they can get any AI system the right data and information to work with, in a continuously improving way. Some will go off and train their own models for specific areas of work (large banks, pharma, etc.) where they can get real alpha from doing so given the many tradeoffs, but most will spend energy on making sure they can get all of the gains from AI breakthroughs on their data and workflows.

Net net: even though some of this gets framed as zero sum, there’s just a ton of opportunity for all layers of the stack and approaches.

[Satya Nadella @satyanadella]

https://t.co/xv6csf1SbV

[Aaron Levie @levie]

One of the key architectural questions of the 21st century in business will be how you maximize your corporate IP in the form of decisions, insights, workflow patterns, and best practices in a world where so much intelligence is packed into AI models.

One might think these questions could just get bitter lessoned out of existence, but in reality they become even more germane as intelligence becomes more powerful. In a world where any firm also has access to frontier intelligence, understanding how you leverage it uniquely becomes a critical question.

That’s why so much value is left to be created between the enterprise and the underlying AI itself. Having evals for your workflows, ensuring that you can route models from different tiers of intelligence, capturing traces in a way that improve your own workflows, and making sure the value of your information compounds as AI gets better all become critical considerations.

Which is also why there’s so much opportunity right now in the applied AI layer. The companies that help figure this out for other enterprises will be in the best position to win the next enterprise workloads.

[Aaron Levie @levie]

Great post on some of the dynamics to think through for the future competitive advantage in world when AI models are shared amongst firms and packing so much for the intelligence of that industry.

This is going to become a core question for companies and the economy broadly over the next decade and beyond. If AI is trained on the best datasets in every single industry - like law, finance, healthcare, or life sciences - then how do you compete and differentiate in the future?

This is a great open question that I don’t think is perfectly knowable right now because of how fast AI progress is happening. But ultimately it stands to reason that if intelligence is abundant and broadly available to anyone in a field, then the companies that effectively use it the best and against a set of data and knowledge that grows in value over time, will be in a strong position.

There’s a huge reinforcing loop between the intelligence from models, a company’s own data, the connection of that data and AI in their workflows, and how employees ultimately interact with that system to create value. There’s no obvious point where this will become uniform across all companies in a vertical because each company will approach this in a different way, just as they already do with their talent and workflows. If anything, there will be compounding returns to those that do this best that accelerate their advantage over time.

Overall, super interesting question to see how this plays out over time.

[Praveen Neppalli @praveenTweets]

Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.

Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:

How do we bring agentic AI beyond engineering?

Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.

These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.

So we created something called Agentic Pods.

The idea is simple.

We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.

Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.

In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.

The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.

The biggest lesson? The best AI opportunities are rarely visible from the outside.

You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.

We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.

It's exciting times!

[Aaron Levie @levie]

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:

* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?

* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).

* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.

* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.

* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.

* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.

* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.

Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

[Hitesh Choudhary @Hiteshdotcom]

Someone just did something really important for the computer vision community.

A developer named Xuban spent 6 months building LibreYOLO, a fully MIT-licensed YOLO library. Free. Open. No strings attached.

Here's the context: YOLO, the real-time object detection framework, is currently dominated by one company. They distribute it under AGPL3, which sounds open-source but has a catch. Businesses using AGPL3 code in commercial products essentially have to open-source everything, or pay up. That company apparently charges around $10,000/year for commercial use. They even asked researchers for money at some point.

Xuban got approached for an acquihire. He said no.

His reason: he just wants people to use YOLO for free. That's it.

MIT license means you can use it in commercial projects, modify it, ship it, no licensing fees, no legal headaches. For startups building computer vision products in India or anywhere else, this is a big deal. $10k/year is not a small number if you're early stage.

Real-time computer vision is everywhere now. Surveillance, retail analytics, manufacturing defect detection, traffic monitoring. The fact that one company had a near-monopoly on the most popular detection framework and was quietly monetizing it through licensing is something most builders didn't even realize.

LibreYOLO is the answer to that.

[Praveen Kumar Verma @Alacritic_Super]

As a Forward Deployed Engineer,

Please learn:

• Python, Go or Rust
• System design & distributed systems
• APIs (REST, GraphQL, gRPC)
• SQL, PostgreSQL & Redis
• Docker, Kubernetes & Terraform
• AWS, Azure or GCP
• CI/CD & GitHub Actions
• Linux, networking & debugging
• LLMs, RAG & AI agents
• MCP, A2A & tool calling
• LangGraph, OpenAI Agents SDK or similar frameworks
• Vector databases (pgvector, Qdrant, Milvus)
• Observability (OpenTelemetry, Prometheus, Grafana)
• Security, OAuth & authentication
• Customer discovery & solution architecture
• Technical writing & documentation
• Production incident response
• Performance optimization & cost management

[Harshil Mathur @harshilmathur]

Friendly reminder: nobody has figured out how to fully use AI at scale yet.

Everyone is experimenting, regardless of how confident they sound on Twitter.

That’s what makes this moment so exciting.

[Aaron Levie @levie]

The deployment of AI in the enterprise beyond just interacting with a chatbot will unequivocally take real work to align AI systems to the underlying business processes they’re involved in and drive the desired outcomes.

Most workflows weren’t designed for AI agents to just drop into. Workflows today in the enterprise deal with fragmented data, legacy software systems that agents can’t connect with, institutional instead of documented knowledge, and more.

To deploy agents reliably at scale you need to get data cleaned up, modernize IT systems, figure out evals, drive change management for the new end state process, and so on. This also involves designing where humans remain in the loop (which will mean entirely new ways people interact with the workflows), and figuring out what a company’s new IP looks like.

This is why so many applied AI companies are expanding FDE efforts and launching deploycos, and why the FDE role will be one of the most critical jobs in tech going forward. There’s a tremendous amount of work to be done on this front.

[anand mahindra @anandmahindra]

Alex Karp’s CNBC interview is going to be widely discussed and debated.

My initial take on it:

Some will call Alex’s comments self-serving but there is an underlying argument he makes which I think is worth taking seriously: AI has three layers. Compute. Model. Application.

He argues that critical infrastructure doesn’t run on a model alone, that it needs an application layer sitting on top.

I think he’s right about the stack. I made a version of this argument in my recent letter to TechM’s shareholders this year: AI is like today’s smartphone: remarkable technology, but indispensable only because of the apps and experiences built on top of it. The ecosystem determines who creates lasting value, not the chip or the model underneath.

To be clear, I still believe India should pursue sovereign frontier model development. But if Karp’s hypothesis is right, and the model layer is commoditising, then the verticalization of the compute, models and applications needs to happen at the same time.

For AI services, from his arguments, it appears the more durable commercial and strategic edge is the application layer: model-agnostic, built on whichever open model fits best, carrying decades of enterprise workflow knowledge that no model provider owns.

As Karp says, the application layer “takes a large language model & makes it safe and precise…Everyone gets to ask the basic questions: who owns the data, where is it cashed, are the prompts secure, is this being transferred to you?” And “critical infrastructure does not run these models without an application layer.”

And this criticality is where the stakes are highest: defence, classified programs, regulated industries, where control over data, auditability and governance is non-negotiable, whichever model happens to sit underneath.

The application also has to enable business enterprises to preserve their ‘alpha.’

That’s where I truly believe AI service companies have the edge. Not necessarily in owning the model, but in owning what sits above it.

I’m keen to hear other reactions to his interview…