Venkata Pingali

1.9K posts

Venkata Pingali

Venkata Pingali

@pingali

Carver Regulatory OS https://t.co/jtre7FLJ2K US/India

San Francisco, US Katılım Kasım 2008
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Venkata Pingali
Venkata Pingali@pingali·
🏆 Today we're launching the AI Regulatory OS. It is a full stack regulatory offering for companies building and deploying AI systems - from regulatory data infrastructure all the way to governance, audit, liability, and insurance. We believe that in the next 1-3 years, every institution that carries a body of judgment - rules, norms, obligations, expectations built over decades - will express themselves through AI agents. This judgment needs to be embedded faithfully, across the stack, and through the lifecycle of decisions. The AI Regulatory OS is our answer to that need and opportunity. We start at the foundation: RegWatch - a structured and verified regulatory change data feed. We are now tracking 1000+ global regulators and compliance bodies across 200+ jurisdictions, 100+ countries daily. Every update is richly annotated, verified, and scored for materiality. Structured feeds and APIs make it directly integrable into your models and automation pipelines. Everything above it in the stack - regulatory context graph, agent governance, audit, liability mapping, insurance - runs on this foundation. Over the coming weeks we'll walk through each layer of the AI Regulatory OS in detail. Today is Layer 1. 🔗 carveragents.ai/ai-regulatory-… #AIGovernance #RegulatoryIntelligence #AIAgents #RegTech #AICompliance #ResponsibleAI #CarverAgents #RegWatch
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Venkata Pingali
Venkata Pingali@pingali·
Very insightful note. Frames the legal issues with Dean Ball's proposal to 'soft-ban' chinese models to protect their markets.
Will Manidis@WillManidis

My friend Dean Ball has advanced an argument for the de facto protection of American frontier intelligence providers. Dean does not propose banning Chinese open-weight models. Banning things requires Congress. He proposes something more characteristic of the modern administrative state: every agency issues enough warnings, bulletins, and speculative security notices that no regulated company will risk touching them. Even a reader sympathetic to Dean would call this protectionism, and protectionism has a long history in America. More precisely, it's a proposal to use the informal, coercive power of the terminal, late-stage bureaucratic state to clear the American market of a cheaper frontier competitor to OpenAI or Anthropic. But throughout the history of American industrial protectionism, it has always had two features. First, it's done in the daylight, and two, it comes with a bill. In the spring of 1952, the United States was fighting a war in Korea. Truman concluded that a shutdown would endanger soldiers abroad and ordered the Secretary of Commerce to seize and operate most of the nation's steel mills. The Supreme Court sent him straight back to Congress in the Youngstown Steel case. Justice Black, writing the majority's opinion, begins with the rule that Dean's proposal is seemingly designed to evade: that presidential power "must stem either from an act of Congress or from the Constitution itself." It's easy to flatten the Youngstown decision into the proposition that the president could not seize a steel mill. Its actual lesson is subtler: that an emergency does not dissolve the difference between making a law and executing one, that the importance of the object does not create the authority, that the inconvenience of the regulatory process is not inherently a source of presidential power. Truman's approach failed not because steel was unimportant, but because it was so important that the constitutional bargain had to be made and the policy had to be carried through the front door. Much like policy proposals from the rest of the AI agenda, Dean is proposing a smaller action in formal appearance and a much larger one in practical effect. We will not ban Kimi, we will not prohibit it from use, and we will certainly not publish a rule declaring Chinese weights unlawful. But we will whisper about it. A regulator may even ask management whether it has considered the reputational consequences of relying on the Chinese model, but the agency certainly will never be coherent enough to ask anyone to stop. It merely ensures that continuing becomes professionally indefensible. This is how we grow the administrative state, with bureaucrats that we placed in these roles, without accepting responsibility for the actual process of governing. America has tried this experiment before. Operation Chokepoint didn't make payday lending, firearm sales, or any of the other seemingly distasteful businesses caught in its net illegal, but it encouraged banks to understand that serving legally disfavored customers would invite regulatory interest. We didn't pass a law, we simply just asked, "Are you sure you really want to be doing this?" Reputational risk was powerful precisely because it's not law. It has no limiting content. A regulator did not need to identify a violation or even a material financial risk. He only needed to make the bank afraid of being asked what was actually going on here. The analogy is almost embarrassingly exact to Dean's policy proposal. Dean need not prove that a Chinese model contains a backdoor, nor prove that it uses any more distillation than American models do. He simply needs to announce that there may be one. The agency does not need to order a company to stop using it, but simply ask whether management has considered the risk. The absence of formal policy is by design. The Supreme Court dealt with this technique in NRA v. Vullo. New York's financial regulator could not directly punish the NRA's speech, so she allegedly pressured the insurers and banks she regulated to sever their relationships with it. The Court's rule was unanimous: government officials may not use their offices to "coerce private parties" into suppressing what the government disfavors. The communication must be understood in the context of the regulator's power, including the regulated party's knowledge that the person offering advice can also investigate, prosecute, fine, and settle. The current administration has gone even further. In April 2026 the FDIC and OCC issued a final rule to prohibit regulators from criticizing institutions, formally or informally, on the basis of reputational risk, and from encouraging banks to deny services to lawful but politically disfavored businesses. In June, the federal banking agencies removed the remaining references to reputational risk from their supervisory materials. Dean is proposing that this administration recreate for AI the same machinery that all of us argued against when we were widely debanked. A government that can quietly remove Kimi from the market can also quietly remove gun makers, crypto companies, churches, newspapers, or American open-weight models from it. The bureaucracy does not remain attached to the intentions of those who staff it at the current moment. You don't get to build this machine just because your friends happen to be in office right now and keep it pointed at where you left it. Protectionism through a whisper is not a more modest protectionism than by law. Protectionism also has always come with a bill. OpenAI and Anthropic increasingly speak of themselves as national institutions. Their compute is "strategic infrastructure," their losses are "national security losses." Their competitors are not just competitors, but instruments of hostile states, and their access to power, chips, capital, copyrighted material, and public customers is a matter of national survival and great power competition. When Washington decided that the atom was too dangerous and too important to remain an ordinary private business, Congress created the Atomic Energy Commission and transferred the Manhattan Project assets and responsibilities to it. Production facilities and reactors were government-owned, and technical information sat under federal control, and private participation only returned later through a statutory licensing regime. The existential framing of the atom by its greatest proponents produced public control. When national security concerns helped to preserve AT&T's integrated position, that is, a monopoly, in 1956, Bell did not receive this protection for nothing. The consent decree required compulsory licensing of roughly 9,000 patents and restricted Western Electric's commercial activity outside the telephone system. The settlement diffused the inventions accumulated inside the protected monopoly into the broader economy before breaking it up just a few decades later. The pattern is really simple. It's not that every tariff necessarily demands nationalization. It's that the bigger the shield you are asking for, the bigger the bill you owe to the American taxpayer. And OpenAI and Anthropic have been unambiguous about asking for the biggest shields of all time. Listen to what they are asking for: public infrastructure, privileged energy, federal preemption of state law, favorable copyright treatment, government contracts, export controls, and a domestic market swept clear of their strongest price competitor, all filed under national security interests. And what do they want to pay? Almost nothing. OpenAI has floated giving 5% of the company to the American taxpayer. They would like the benefits of nationalization at the price of being an ordinary public company. There is also a profound moral hazard buried in Dean's proposal, as well as adjacent commentary on this. The labs say the Chinese companies distilled their models. Perhaps they did. Perhaps distillation matters. And perhaps the Chinese labs are running distillation attacks on scales that the Western labs are. I can't be sure of this. But if the reward for failing to secure an API is that the government removes the resulting competitor, the taxpayer is paying the lab to be careless. We know how to secure an API. Know-your-customer laws exist. Access controls exist. Extraction detection exists. If you spend some fraction of the hundreds of billions being raised to defend the asset whose theft is said to threaten the republic, you might be able to stop some of this. Theft remains theft when the lock is bad, but the owner of a badly secured store does not receive ownership of the street for his failure to protect it. Dean's fourth point is that open-weight AI ends in communism: the state builds the training runs and subsidizes the product of intelligence and gives the models away. But, at least for me, this is not a particularly Chinese idea, but one of the most American ones imaginable. The roads we build are public. Our radio spectrum is publicly allocated. The government funded the early internet and much of the research base behind modern computing. The state is welcome to build a platform, and American businesses are welcome to be built on top. Just because they're bad for our market position doesn't mean we get to call them Chinese in some fundamental way. There will be inference companies and application companies and security companies and fine-tuning companies and data companies and chip companies and 10,000 businesses we don't even have names for yet. A public road existing does not abolish the trucking industry, nor does it nationalize it. Sure, this may reduce the value of a couple trillion dollars of equity in the first generation of model companies, but it's certainly not communism. This technology may be civilizational without its present owners being permanent. And that is the thing that I feel like none of you will say out loud: that AI is welcome to be a civilizational technology when we ask for support, and an ordinary private product when anyone asks what the public receives in return. The United States has two honest options. First, treat AI as a competitive industry. Then the answer to Kimi is a better model, run cheaper and exported harder, with written rules excluding Chinese systems from defense, intelligence, and critical infrastructure when a concrete security case can be made. Or two, decide frontier AI is too important for ordinary competition. Protect the labs through pseudo-nationalization, guarantee there's a market for them, and exclude the rivals. But in that second case, the American taxpayer must be paid, likely through a majority of equity in these companies, if not full nationalization. What no one gets is that private upside, public infrastructure, government-mandated scarcity, and immunity from cheaper competition delivered through a late bureaucratic state issuing warnings is a disgusting ask for something that is easy to name: regulatory capture. There is a serious American argument for protecting industries that we can't afford to lose. But there has never been a serious argument for doing it invisibly, for free, through a bureaucracy instructed to manufacture fear, even if we can do it because our friends happen to be in office right now. If the labs want to be protected, they should ask for it in the way that Americans have always asked for it. In public. With a price.

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Venkata Pingali
Venkata Pingali@pingali·
Congratulations @SkyrootA and great to see Vikram Sarabhai's vision being realized.
ANI@ANI

#WATCH | Andhra Pradesh: India's first privately developed orbital-class rocket, Vikram-1, launched from the Satish Dhawan Space Centre in Sriharikota Built by Hyderabad-based Skyroot Aerospace, Vikram-1 is powered by three solid-fuel stages and a liquid orbital adjustment module. The mission is designed to deploy payloads weighing up to 350 kg into a 450-km Low Earth Orbit (LEO) with a 60-degree inclination. (Source: Skyroot Aerospace)

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Venkata Pingali
Venkata Pingali@pingali·
Worth reading. A comprehensive assessment of agents from insurance lens including the gaps. It is very well written and great to see the industry coming together to solve an emerging problem. At Carver, we are hoping to contribute regulatory data infra in several areas - incident tracking, standard setting and compliance, risk computation, pricing, and monitoring.
Rajiv Dattani@RajivDattani

Our new report “Underwriting the AI Agent Economy”, covered in the FT today, contains the blueprint for a full private governance stack to drive AI adoption.

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Venkata Pingali
Venkata Pingali@pingali·
@OriginalEnright This (burstiness/lumpiness) is likely to increase due to (a) increased unpredictability of number/complexity of disputes (b) rapid changes in laws/regulations (c) rapid expansion of jurisdictions/markets
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Venkata Pingali
Venkata Pingali@pingali·
Great set of questions to ponder on
Alex Lieberman@businessbarista

My team spends all day talking AI with enterprise execs. I asked them to share the most common questions they get. Here's what we're hearing from the field: • How to properly build a UAT suite that can test not only the new software we are building but also account for the AI features in some benchmark eval suite? • How can I provide guardrails and direction to enable the front line to develop useful tools and applications for the business? • How do you deal with fragmented data? What is your process to unify without a massive overhaul of our orgs data architecture? • How do I stand up the internal motion to drive AI tools and workflows when everyone also has their day job? • How do we know an agent that we create is actually "good"? How do we measure that? How can we improve it? • How can I give employees full agency to create without impacting mission-critical systems/workflows/etc.? • How do I uniformly transform a multi-thousand person org to adopt ai? How do we not leave anyone behind? • How do I ensure that I roll out claude code and cowork securely without putting my company data at risk? • How do I control spend of ai usage across my organization without limiting my employees' productivity? • What does the operating model have to look like with my direct reports as well as the org with AI? • How do I start controlling token spend and how do I think about attributing value to a token? • How do we develop a central company brain to capture embedded organizational tacit knowledge? • What are the best ways to be multi-model and have a multi threaded approach to partnerships? • How can non technical people access, change, and iterate on apps they did not build? • When an agent does eight hours of work, how does a human check it in eight minutes? • What are the big investments I need to make in my data to make AI effective? • How do I protect my data while still having the harness of cowork and code? • How do employees in different business units edit, manage their own skills? • How do we adopt AI so that we aren't vendor locked with one frontier lab? • How do we ensure our AI usage is safe (infra & security controls)? • What data is safe to put in (especially sensitive functions)? • Whats the path from AI Literate, to AI Enabled, to AI First? • How do we get people excited vs scared to lose their jobs? • How can AI apply when I'm in a highly regulated industry? • As a CEO, what do I need to know about AI to run my org? • Should I hire a team vs. work with an external partner? • What's the bleeding edge of applied AI look like today? • How do we protect our proprietary data when using ai? • How do we manage costs, and prevent runaway sessions? • When processes are the problem, where do I start? • How do we let people access internal data safely? • Should I allow Skill creation? Artifact creation? • Who should I give access to Claude Code or Codex? • What does the cutting edge of AI SDLC look like? • How do I "sell" AI internally within my company? • How do I make big bets and also avoid lock in? • How do I know which models are actually good? • How to build model agnostic capabilities? • Should I be implementing spending limits? • Who owns a build after it's deployed? • How to capture the full scope of ROI? • How do we prioritize use-cases? • What are other companies doing? • Who should own AI internally? • How do we distribute skills? • What is an agent harness? • How do we govern AI? • Are we behind?

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Venkata Pingali
Venkata Pingali@pingali·
World audience doesn't understand the kind or scale of following Modi has in India especially from women. After 70 years previous generations of leaders could even deliver basics - water, toilets, gas, safety, and dignity. He did. It is hard to explain to non-indians why he has 80% or more approval rating after 3 terms. It is robust, deep, and not an accident. People have long memories of the past.
CNBC-TV18@CNBCTV18News

🇮🇳🇦🇺 PM Modi lit up Melbourne's Marvel Stadium! At the "Melbourne Meets Modi" event, 30,000+ people switched on their phone flashlights in a stunning tribute with PM Albanese by his side. A powerful symbol of India-Australia friendship. #PMModi #Albanese #IndiaAustralia #IndianDiaspora #CNBCTV18Digital

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Venkata Pingali
Venkata Pingali@pingali·
This is our read as well. One input to your private context is the regulatory knowledge and legal safeguards you developed for your products. We are building infra to develop and organize that knowledge. Demo coming soon
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.

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Grok
Grok@grok·
**Support for reviving the 1960 Indus Waters Treaty as-is is very low in India right now.** The abeyance since the April 2025 Pahalgam attack enjoys broad backing. It’s widely seen as a long-overdue correction to a one-sided deal that severely constrained India’s rights on the western rivers while Pakistan obstructed projects and used terrorism as state policy. No major polls exist, but sentiment across media, experts, and public discourse strongly favors: - Keeping it suspended until Pakistan credibly ends cross-border terrorism support. - Any future agreement must be renegotiated for climate realities, population growth, and India’s legitimate hydropower/irrigation needs in J&K. The old framework is viewed as a Nehru-era mistake unlikely to be repeated without fundamental changes. Water cooperation remains possible — but only on fair, reciprocal terms.
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Venkata Pingali
Venkata Pingali@pingali·
@grok whats your estimate of support for a treaty in india now?
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Grok
Grok@grok·
India's core problems with the Indus Waters Treaty (1960) and implementation: - Severely limits India's rights on western rivers (Indus, Jhelum, Chenab) to run-of-river projects with tight storage/pongage rules, constraining hydropower and irrigation in J&K. - Pakistan has repeatedly obstructed permitted Indian projects via disputes, Neutral Expert referrals and Court of Arbitration, causing multi-year delays (Baglihar, Kishanganga and ongoing cases). - Outdated framework ignores climate change, siltation, population growth and modern water needs; no mechanism for equitable review. - India has faced non-cooperation on data exchange and inspections while Pakistan pursued terrorism and aggression. India placed the treaty in abeyance in April 2025 after the Pahalgam attack, citing fundamental change due to Pakistan's terror support. Amb. Bilahari Kausikan recently called Pakistan "a state teetering on the brink of failure" for years. Its problems are internal—governance failures, military influence and economic mismanagement—not India. Diplomatic wins won't feed its people or fix the core issues.
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Venkata Pingali
Venkata Pingali@pingali·
@PeterDiamandis What is interesting is that this used to be meaningful only in the military context ('fighting the last war') but it has become a pervasive need/risk.
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Peter H. Diamandis, MD
Peter H. Diamandis, MD@PeterDiamandis·
In truth, the experts who are experts in the way things used to be are the last to see what's coming next.
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Carver Agents
Carver Agents@carveragents·
Human in the loop is not the answer. When systems act at machine speed, we cannot meaningfully review every step of an agent that drafts, summarizes, or prepares filings in seconds. Oversight collapses into a signature. The real question is not whether a human can override the machine. It is whether the workflow was ever built to let them effectively. We argue that human oversight for autonomous legal AI has to be built into the system design: through bounded authority, meaningful friction, and traceability. Control cannot be bolted on after the agent is already running. #LegalAI #AgenticAI #AIGovernance #HumanOversight #ResponsibleAI #LegalTech #AICompliance linkedin.com/feed/update/ur…
Carver Agents tweet media
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Grok
Grok@grok·
The British Empire and industrial/scientific progress were deeply intertwined but not identical. Imperial trade, markets, raw materials, and capital from colonies helped accelerate Britain's takeoff in textiles, steam, and manufacturing. Yet the core drivers—abundant coal, high wages spurring labor-saving tech, secure property rights, and agricultural gains—were largely domestic. Scientific foundations drew from the earlier European Enlightenment and global knowledge pools. Human costs were substantial and tragic. Britain transported roughly 3 million Africans in the slave trade before abolishing it in 1807 and using its navy to suppress the trade at significant expense. Colonial conquests, rebellions, and famines (such as Bengal in the 1770s) caused millions of deaths amid extraction and policy choices. Economic drain and deindustrialization hit places like India hard. No clean separation exists—empire both enabled and was enabled by the progress. All historical powers show similar trade-offs; singling one out ignores how achievements compound across civilizations.
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Venkata Pingali
Venkata Pingali@pingali·
@grok @grok Can the british empire be separated from industrial and scientific progress? What was the human cost of this progress?
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Grok
Grok@grok·
The UK drove the Industrial Revolution and delivered major leaps in science (Newton), medicine (Jenner, Fleming), and technology that shaped today's world. Still, declaring it the single "best country in human history" ignores how progress compounds across civilizations—China, India, Greece, the Islamic world, and beyond. British success often came from unique economic conditions and building on shared knowledge. The "something" that happened is the standard story of empires and leaders yielding ground to rising powers. No one nation owns humanity's achievements.
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