Bsri

Business strategy and transformation professional focused on innovative business models driven by design thinking and powered by digital. Loves classic music

[Bsri @sribaskar_dT]

@narendramodi @anwaribrahim 🙏

[Bsri @sribaskar_dT]

@krithikasivasw 100% agree .

[Bsri @sribaskar_dT]

@elonmusk FSD is amazing experience. just completed a 1000 mile round trip with FSD. Santa mode is awesome ! More such festive vibes can be added (e.g. New year , Diwali) HoV lane recognition needs improvement… Thanks

[Bsri @sribaskar_dT]

@AndrewBolis GPT

[Bsri @sribaskar_dT]

@AndrewYNg Excellent insights !

[Andrew Ng @AndrewYNg]

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees.
- AI application layer: There is underinvestment. The potential is still much greater than most realize.
- AI infrastructure for inference: This still needs significant investment.
- AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble.

Caveat: I am absolutely not giving investment advice!

AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers.

I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund.

AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity.

As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). As market penetration grows — I’m confident it will, given how useful these tools are — aggregate demand for token generation will grow.

I predicted early last year that we’d need more inference capacity, partly because of agentic workflows. Since then, the need has become more acute. As a society, we need more capacity for AI inference.

Having said that, I’m not saying it’s impossible to lose money investing in this sector. If we end up overbuilding — and I don’t currently know if we will — then providers may end up having to sell capacity at a loss or at low returns. I hope investors in this space do well financially. The good news, however, is that even if we overbuild, this capacity will get used, and it will be good for application builders!

AI infrastructure for model training. I am happy to see the investments going into training bigger models. But, of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment.

Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the “technology moat” for training frontier models is weak. (That said, ChatGPT has become a strong consumer brand, and so it enjoys a strong brand moat, while Gemini, assisted by Google's massive distribution advantage, is also making a strong showing.)

I remain bullish about AI investments broadly. But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals. I don’t think this will happen, but if it does, it would be unfortunate since there’s still a lot of work in AI that I consider highly deserving of much more investment.

Warren Buffett popularized Benjamin Graham’s quote, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” He meant that in the short term, stock prices are driven by investor sentiment and speculation; but in the long term, they are driven by fundamental, intrinsic value. I find it hard to forecast sentiment and speculation, but am very confident about the long-term health of AI’s fundamentals. So my plan is just to keep building!

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

[Bsri @sribaskar_dT]

@PaariStudent1 @ThamizhachiAuth NB Ki k

[Bsri @sribaskar_dT]

@AndrewBolis Guide

[Bsri @sribaskar_dT]

@damianplayer Automate

[Bsri @sribaskar_dT]

@WHLeavitt Yes

[Bsri @sribaskar_dT]

@AndrewBolis Send

[Bsri @sribaskar_dT]

@WHLeavitt Yes

[Bsri @sribaskar_dT]

@DrTamilisai4BJP @BJP4India @BJP4TamilNadu @narendramodi @AmitShah @JPNadda @BJP4Telangana @annamalai_k @ReddySudhakar21 @NaMoInTamil @MenonArvindBJP Heart felt condolences,Madam. Your father promoted youngsters to get into politics . Om Shanthi🙏

[Aravind @aravind]

PM @narendramodi today gave an excellent speech at the AI summit in France. He started his speech with an "experiment" where you ask the AI to draw a person writing with his left hand.

But the AI will draw a person writing with right hand most of the time, he claimed, because it has been trained on that kind of data the most. The AI's response is biased because the training data is predominantly biased.

PM Modi pointed this out as an issue in AI, to be solved using open and unbiased training data. I was surprised at how much on top of AI he really was. You should listen to his short speech. It touched all the main issues in AI and gave recommendations.

I tried the experiment he suggested with Grok. And like he said, it always draws a person writing with the right hand due to its inherent training bias (as most images it must have been trained on would be right handers.)

I even tried asking it to draw Obama who's a leftie. I thought, may be, it would have been trained on some images of Obama writing with his left hand and can replicate. But the training bias is so much that it drew Obama too using his right hand.

PM Modi seems to have revealed AI bias to the world using such a simple experiment at the AI summit. Even take health analysis: Because the AI models are trained more on western, european health data, they may be wrongly diagnosing health issues for other populations. Or give wrong recommendations.

This is why India needs its own AI, trained on uniquely Indian data - diverse data from all over India to help reduce such biases.

[Bsri @sribaskar_dT]

https://t.co/SrszmpNPND An interesting read on Process management and AI

[Bsri @sribaskar_dT]

@pramodkvarma @NandanNilekani @dilipasbe @snjyjn Like your humility ! Thanks for your support !

[Bsri @sribaskar_dT]

@TheGopalan Need pdf link please

[Bsri @sribaskar_dT]

@Ashwinsampathk Very nice !

[Luiza Jarovsky, PhD @LuizaJarovsky]

🚨 The Dutch Data Protection Authority published the "AI & Algorithmic Risks Report," and it's a great read for everyone in AI governance. Below are its 8 key messages:

1️⃣ "The AI risk profile continues to call for vigilance from everyone – from Ministers to citizens and from CEOs to consumers – because (i) it is difficult to assess whether AI applications are sufficiently controlled and (ii) AI incidents can occur more and more frequently, especially as AI is increasingly becoming intertwined into society"

2️⃣ "Many new AI systems and risks (or possible risks) stand out. From experimentation by big tech companies to the widespread use of AI in situations where people are vulnerable"

3️⃣ "Information provision is essential for the functioning of democracy, but is under pressure from the deployment of AI systems. This applies to both moderation and distribution of content and, more recently, to content creation with generative AI"

4️⃣ "Conditions for adequate democratic control of AI systems are currently insufficiently met"

5️⃣ "Random sampling is a valuable tool to reduce risks in profiling and selecting AI systems"

6️⃣ "The entry into force of the AI Act (early August 2024) is a milestone, with concerns about (i) the long transition period (up to 2030) for existing high-risk AI systems within the government and (ii) whether robust and workable product standards will be in place in a timely manner"

7️⃣ "With regard to the further elaboration of the coalition agreement, the AP advises to continue to give priority to algorithm registration by government organisations and to discuss registration by semipublic organisations"

8️⃣ "The AP is committed to increasing the control of AI systems, in which (i) a proliferation of frameworks should be avoided and (ii) a recalibration of the national AI strategy can contribute to the further ecosystem for development and control of AI systems"

👉 Read the @toezicht_AP (Dutch DPA)'s report below.

🔥 To stay up to date with the latest developments in AI policy, compliance & regulation, join 35,000+ people who subscribe to my newsletter (link below).

[Bsri @sribaskar_dT]

@KanchanGupta Thanks for sharing this .