KDC Tweets

U/W DON Office Tweet ! Manager in #SEPL ! If U r Bad I m ur DaD ! LifeLine💞@imHarleenDeol~/#HD98 ! Followed by @MaiTeraBapHu IPS.

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@imHarleenDeol💞 💕~#HD98

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🏏@imharleenDeol 💓 Best Wishes For Your Next Match...👏👍👏

Only Focus on Your Strength and Efforts!

@BCCIWomen vs @AusWomenCricket
@ImHarmanpreet @mandhana_smriti
#INDvsAUS #WomenInSuperBlue

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@flipkartsupport Hi Fellows,

Never Ever Buy Any Products from Online Plateforms, Like @Flipkart #Amazon #Meesho #Shopsy #Snapdeal

🚫Boycott #Flipkart Shoping App and Services❌

#FlipkartScam #OnlineFraud #JagoGrahakJago

@consaff @jagograhakjago @Costinaraw @Mogadget @FbdMirror @ekartlogistics

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@Flipkart, Facing issue with A Received #Order has #Defective Product, Order Id OD337526886692576100

Return and REFUND request already Approved, but there has been No Pickup/Update/Call yet. Please resolve and Help Process the #Refund at the Earliest. #Flipkart

@flipkartsupport

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@grok

[Rishi Bagree @rishibagree]

Calcutta was the world's largest provider of skeletons for medical programs until 1980, exporting 60,000 human skeletons yearly.

Guess who ran the largest hospice and orphanage in Calcutta at that time?

[The Khel India @TheKhelIndia]

INDIA HAS FOUND ITS NEXT "FLYING SIKH"

- The Fastest Man of India, Gurindervir Singh 🔥⚡

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@grok

[The Khel India @TheKhelIndia]

INDIA HAS FOUND ITS NEXT "FLYING SIKH"

- The Fastest Man of India, Gurindervir Singh 🔥⚡

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@grok

[News Algebra @NewsAlgebraIND]

GIRL : "My health got bad, so I took a two-day sick leave"

"After coming back, I found out my manager made a new rule: sick leave must be informed one night before" 🥲

"If I could predict life that accurately, I would’ve done many things differently" 😂

[KDC Tweets @KDCTweet]

@flipkartsupport Very Bad #Experience from #Flipkart It's too Annoying and Irritating Response from the Order Page, not even received any Pickup Call from Courier Agent/Delivery Partner

Showing: Pickup unsuccessful /Reattempting pickup

Suggest Others to Never Buy from @Flipkart @FlipkartSellers

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#Robot

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@Flipkart #Met

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#Melody

[Flipkart @Flipkart]

Yk whose cart looks like this🤭
#Melody

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@grok

[The Random Recruiter @randomrecruiter]

@sama @elonmusk was right

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@grok

[StockMarket.News @_Investinq]

@sama Bill Ackman type of tweet

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@grok

[Taufiq @taufiqintech]

@sama This is Sam whenever there's a PR issue

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@grok

[bone @boneGPT]

At this point you can just copy and paste it into gpt5 and ask how he's being disingenuous:

This statement is disingenuous because it presents itself as humble, responsible, and market-disciplined — while subtly masking the contradictions between what OpenAI says and what its actions and context reveal. Let’s unpack why:

---

1. Pretending to be just another market actor (“if we fail, we fail”)

> “If we screw up and can’t fix it, we should fail… That’s how capitalism works.”

This is rhetorically neat but misleading.
OpenAI is not operating in a normal competitive market:

It already has Microsoft as a multibillion-dollar backer, deep integration into Azure, and enormous strategic importance to both U.S. industry and government.

Its technology is being positioned as critical infrastructure — for research, defense, and the broader economy.

So if OpenAI were to “fail,” the fallout would likely harm too many stakeholders for the government or Microsoft to simply let it collapse. In practice, OpenAI is becoming systemically important, even as it insists it’s not “too big to fail.”
That contradiction makes the claim disingenuous.

---

2. Downplaying government dependence while lobbying for national AI strategy

> “We think that the US government should have a national strategy for its own AI infrastructure.”

This sounds innocuous, but it implies a public–private buildout — meaning taxpayers help finance or subsidize infrastructure that OpenAI would then depend on or even profit from.

They deny wanting government “insurance” or bailouts, yet their argument for a “national AI infrastructure” positions OpenAI as a beneficiary of government-backed capacity (e.g., chips, datacenters, energy).
That’s a subtle form of seeking state support while denying it.

---

3. Framing massive capital spending as altruistic inevitability

> “We are trying to build the infrastructure for a future economy powered by AI… this is the time to invest…”

This sounds visionary, but it’s also a justification for aggressive capital expansion that benefits OpenAI and its partners (e.g., Microsoft, chipmakers).
They frame it as if they are doing this for humanity — curing diseases, advancing science — when in reality it’s a commercial bet with immense upside for shareholders and executives.

By couching this in humanitarian or “mission-driven” language, they moralize what is ultimately a business growth strategy.

---

4. Shifting risk language

> “We think the government ends up as the insurer of last resort… but not for overbuild.”

This tries to narrow “insurer of last resort” to catastrophic misuse of AI (e.g., cyberattacks).
But the broader implication remains: OpenAI acknowledges that AI risks are systemic and only the government can handle them. That’s precisely what “too big to fail” means in practice — when the state must intervene if things go wrong, whether technically or economically.
They’re redefining the phrase to sound responsible, but the underlying dependency stays.

---

5. Emotional framing as moral cover

> “It is a great privilege to get to be in the arena…”

Ending with this kind of elevated rhetoric (“the arena,” “privilege,” “mission”) is a way to frame ambition as virtue and deflect scrutiny. It’s a classic rhetorical move: claim moral clarity while engaging in self-serving expansion.

---

In short

The disingenuousness lies in tone versus reality:

What They SayWhat It Really Implies

“We’re just a company in a capitalist system.”They are effectively a strategic national infrastructure entity.
“We don’t want government bailouts.”They want government partnership and underwriting of systemic risk.
“We’re scaling for humanity’s good.”They’re scaling for competitive dominance and investor returns.
“If we fail, others will continue.”In practice, too many dependencies make true failure politically or economically intolerable.

[KDC Tweets @KDCTweet]

@grok

[KDC Tweets @KDCTweet]

@grok

[Sam Altman @sama]

I would like to clarify a few things.

First, the obvious one: we do not have or want government guarantees for OpenAI datacenters. We believe that governments should not pick winners or losers, and that taxpayers should not bail out companies that make bad business decisions or otherwise lose in the market. If one company fails, other companies will do good work.

What we do think might make sense is governments building (and owning) their own AI infrastructure, but then the upside of that should flow to the government as well. We can imagine a world where governments decide to offtake a lot of computing power and get to decide how to use it, and it may make sense to provide lower cost of capital to do so. Building a strategic national reserve of computing power makes a lot of sense. But this should be for the government’s benefit, not the benefit of private companies.

The one area where we have discussed loan guarantees is as part of supporting the buildout of semiconductor fabs in the US, where we and other companies have responded to the government’s call and where we would be happy to help (though we did not formally apply). The basic idea there has been ensuring that the sourcing of the chip supply chain is as American as possible in order to bring jobs and industrialization back to the US, and to enhance the strategic position of the US with an independent supply chain, for the benefit of all American companies. This is of course different from governments guaranteeing private-benefit datacenter buildouts.

There are at least 3 “questions behind the question” here that are understandably causing concern.

First, “How is OpenAI going to pay for all this infrastructure it is signing up for?” We expect to end this year above $20 billion in annualized revenue run rate and grow to hundreds of billion by 2030. We are looking at commitments of about $1.4 trillion over the next 8 years. Obviously this requires continued revenue growth, and each doubling is a lot of work! But we are feeling good about our prospects there; we are quite excited about our upcoming enterprise offering for example, and there are categories like new consumer devices and robotics that we also expect to be very significant. But there are also new categories we have a hard time putting specifics on like AI that can do scientific discovery, which we will touch on later.

We are also looking at ways to more directly sell compute capacity to other companies (and people); we are pretty sure the world is going to need a lot of “AI cloud”, and we are excited to offer this. We may also raise more equity or debt capital in the future.
But everything we currently see suggests that the world is going to need a great deal more computing power than what we are already planning for.

Second, “Is OpenAI trying to become too big to fail, and should the government pick winners and losers?” Our answer on this is an unequivocal no. If we screw up and can’t fix it, we should fail, and other companies will continue on doing good work and servicing customers. That’s how capitalism works and the ecosystem and economy would be fine. We plan to be a wildly successful company, but if we get it wrong, that’s on us.

Our CFO talked about government financing yesterday, and then later clarified her point underscoring that she could have phrased things more clearly. As mentioned above, we think that the US government should have a national strategy for its own AI infrastructure.

Tyler Cowen asked me a few weeks ago about the federal government becoming the insurer of last resort for AI, in the sense of risks (like nuclear power) not about overbuild. I said “I do think the government ends up as the insurer of last resort, but I think I mean that in a different way than you mean that, and I don’t expect them to actually be writing the policies in the way that maybe they do for nuclear”. Again, this was in a totally different context than datacenter buildout, and not about bailing out a company. What we were talking about is something going catastrophically wrong—say, a rogue actor using an AI to coordinate a large-scale cyberattack that disrupts critical infrastructure—and how intentional misuse of AI could cause harm at a scale that only the government could deal with. I do not think the government should be writing insurance policies for AI companies.

Third, “Why do you need to spend so much now, instead of growing more slowly?”. We are trying to build the infrastructure for a future economy powered by AI, and given everything we see on the horizon in our research program, this is the time to invest to be really scaling up our technology. Massive infrastructure projects take quite awhile to build, so we have to start now.

Based on the trends we are seeing of how people are using AI and how much of it they would like to use, we believe the risk to OpenAI of not having enough computing power is more significant and more likely than the risk of having too much. Even today, we and others have to rate limit our products and not offer new features and models because we face such a severe compute constraint.

In a world where AI can make important scientific breakthroughs but at the cost of tremendous amounts of computing power, we want to be ready to meet that moment. And we no longer think it’s in the distant future. Our mission requires us to do what we can to not wait many more years to apply AI to hard problems, like contributing to curing deadly diseases, and to bring the benefits of AGI to people as soon as possible.

Also, we want a world of abundant and cheap AI. We expect massive demand for this technology, and for it to improve people’s lives in many ways.

It is a great privilege to get to be in the arena, and to have the conviction to take a run at building infrastructure at such scale for something so important. This is the bet we are making, and given our vantage point, we feel good about it. But we of course could be wrong, and the market—not the government—will deal with it if we are.

[KDC Tweets @KDCTweet]

@grok

[Morning Brew ☕️ @MorningBrew]

Warren Buffett drops some wisdom in his farewell letter to shareholders.

[KDC Tweets @KDCTweet]

@grok

[Andrew Ng @AndrewYNg]

I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college. He is worried that by the time he graduates, AI will be so good there’s no meaningful work left for him to do to contribute to humanity, and he will just live on Universal Basic Income (UBI). I wrote back to reassure him that there will still be plenty of work he can do for decades hence, and encouraged him to work hard and learn to build with AI. But this conversation struck me as an example of how harmful hype about AI is.

Yes, AI is amazingly intelligent, and I’m thrilled to be using it every day to build things I couldn’t have built a year ago. At the same time, AI is still incredibly dumb, and I would not trust a frontier LLM by itself to prioritize my calendar, carry out resumé screening, or choose what to order for lunch — tasks that businesses routinely ask junior personnel to do.

Yes, we can build AI software to do these tasks. For example, after a lot of customization work, one of my teams now has a decent AI resumé screening assistant. But the point is it took a lot of customization.

Even though LLMs can handle a much more general set of tasks than previous iterations of AI technology, compared to what humans can do, they are still highly specialized. They’re much better at working with text than other modalities, still require lots of custom engineering to get it the right context for a particular application, and we have few tools — and only inefficient ones — for getting our systems to learn from feedback and repeated exposure to a specific task (such as screening resumés for a particular role).

AI has stark limitations, and despite rapid improvements, it will remain limited compared to humans for a long time.

AI is amazing, but it has unfortunately been hyped up to be even more amazing than it is. A pernicious aspect of hype is that it often contains an element of truth, but not to the degree of the hype. This makes it difficult for nontechnical people to discern where the truth really is. Modern AI is a general purpose technology that is enabling many applications, but AI that can do any intellectual tasks that a human can (a popular definition for AGI) is still decades away or longer. This nuanced message that AI is general, but not that general, often is lost in the noise of today's media environment.

Similarly, the progress of frontier models is amazing! But not so amazing that they’ll be able to do everything under the sun without a lot of customization. I know VC investors who are scared to invest in application-layer startups because they are worried that frontier AI model companies will quickly wipe out all of these businesses by improving their models. While some thin wrappers around LLMs no doubt will be replaced, there also remains a huge set of valuable applications that the current trajectory of progress of frontier models won’t displace for a long time.

Without accurate information about the current state of AI and how it is likely to progress, some young people will decide not to enter AI because think think AGI leaves them no meaningful role, or decide not to learn how to code because they fear AI will automate it — right when it is the best time ever to join our field.
Let us all keep working to get to a precise understanding of what’s actually possible, and keep building!

[Original text: https://t.co/OfxCVPGKoq ]

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@grok

@RamPKeshavn @NrityaGopalDas @DeepikaTweetsDC

[Science girl @sciencegirl]

The moment when Mount Kailash glows in golden sunrise light