ananddhanabal

#gratitude #blessed

[ananddhanabal @ananddhanabal]

History has taught us for 3,000 years:

🚫 Do not use warhorses for donkey tasks.
🚫 Do not bring the wrong blade to the real fight.

The unit economics of TOKENMAXXING work exactly the same way.

In SaaS, the 100th seat was free. In AI, the 100th inference costs the same as the first.

Through experience, we are educated to choose the right model for the right usecase, but the lesson learnt is ...

‼️ NOT all token outcomes have the same VALUE.
‼️ NOT all VALUE has the same IMPACT. Period

Consider the reality of "Tokenmaxxed" workflows: Burning 10 million tokens in Claude Opus to draft a basic PowerPoint isn't leveraging AI. It is firing up a $500K Lamborghini 🏎️ to drive 200 meters for coffee ☕.

But, burning 10 million tokens on a strategic deck that drives a 10x revenue multiplier? That is pure value creation.

What matters is: WHICH MODEL -> for WHICH OUTCOME -> at WHICH COST.

For the CXO, AI inference cannot be allowed to drive margin compression. Tokens are the new multiplier for ARR and NRR. The two driving factors are : Pricing and Capital Allocation.

1️⃣ Pricing: SaaS vendors must embrace hybrid pricing (Platform + Seat + Outcome-based). It’s not just outcome-first; it’s value-first.

2️⃣ Capital Allocation: Embed an intake process tied directly to token economics. Every enterprise AI initiative must pass through a VALUE RUBRIC, a filtering rule where any workflow with under $50K in projected impact gets routed to a lightweight model (like Haiku or Flash), reserving heavy frontier models strictly for high-yield outcomes. Treat compute like capital: institute quarterly token reallocations.

Remember:
‼️ NOT all OUTCOMES yield the same VALUE, and
‼️ NOT all VALUE delivers the same IMPACT.

To keep it simple: Not every PowerPoint inference needs Claude Opus. More to come on Compute
#AI #EnterpriseAI #FinOps #AIeconomics #GenerativeAI

[ananddhanabal @ananddhanabal]

https://t.co/0sXFhqDcMw

[ananddhanabal @ananddhanabal]

Jensen has upgraded the recipe from baking Layer cakes to brewing AI factories. In AI factories, TOKENS are the currency of exchange and cost to produce tokens is called "𝗠𝗮𝗿𝗴𝗶𝗻𝗮𝗹 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲."

Here’s what everyone got wrong, including me.

When the cost to produce dropped from $𝟭𝟵.𝟵𝟵 𝘁𝗼 $𝟬.𝟵𝟵 𝗽𝗲𝗿 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗧𝗢𝗞𝗘𝗡 we assumed it is a Royal Flush moment for VC who invested early in AI

But the assumption is WRONG.

1. 𝗬𝗼𝘂 (𝗦𝗲𝗿𝗶𝗲𝘀 𝗔) ♣️ - Best mover advantage and you get to architect at the unit economics on todays rate ($0.99)

2. 𝗬𝗼𝘂𝗿 𝗰𝗼𝘂𝘀𝗶𝗻 (𝗦𝗲𝗿𝗶𝗲𝘀 𝗗) ♥️- started startups 2 years ago,  built on yesterdays price at $19.99 got doomed on both cost structure and valuation  That’s not just a pricing strategy shift, a call to move from selling AI to selling workflow.

3. 𝗬𝗼𝘂𝗿 𝗴𝗿𝗮𝗻𝗱𝗽𝗮 (𝗛𝘆𝗽𝗲𝗿𝘀𝗰𝗮𝗹𝗲𝗿𝘀) ♦️ - Doesn't care. He owns the casino, the power grid, the customer, the data, and the distribution.

"𝗧𝗵𝗲 𝗛𝗼𝘂𝘀𝗲 𝗔𝗹𝘄𝗮𝘆𝘀 𝗪𝗶𝗻𝘀." But there’s one catch: The House only wins if the casino stays full. If tokens get too cheap too fast, Grandpa's holding a massive mortgage on empty data centers

At the end of the day, 𝗧𝗵𝗲 𝗪𝗵𝗮𝗹𝗲𝘀 🐳 (𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝘂𝘆𝗲𝗿) A♠️ — doesn’t know what a token costs, doesn’t care. He’s buying outcomes. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝘄𝗶𝗻𝘀, but that doesn't mean all AI companies will survive. It's not about the cards — it's about the bet.

Appreciate if we have reality check from experts Ethan Mollick and Tomasz Tunguz

As intelligence becomes a commodity, are 𝗱𝗲𝗲𝗽 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻, 𝗽𝗿𝗼𝗽𝗿𝗶𝗲𝘁𝗮𝗿𝘆 𝗰𝗼𝗻𝘁𝗲𝘅𝘁/𝗱𝗮𝘁𝗮, 𝘁𝗿𝘂𝘀𝘁, 𝗮𝗻𝗱 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘀𝘁𝗶𝗰𝗸𝗶𝗻𝗲𝘀𝘀 the key defensible moats left for startups — or is something even bigger at play on the casino floor?

[ananddhanabal @ananddhanabal]

@apoorv03 @sundeep @altcap @apoorv03 - Thanks for uploading, Great one

[ananddhanabal @ananddhanabal]

Most people think better AI means paying less. The truth is different.

A $500 TV today is much better than a $500 TV from 5 years ago.
AI run backwards. 
• When the model becomes much stronger (the newest top models), the price stays high or even goes up.
• Companies keep those margins to build the next better models

Right now the best model sets the best price. From model perspective, the real unlock for AI adoption isn't better models. It's open-source and real competition.

#AIReflection,#TOKENOMICS,#AIEconomics,#AIAdoption, #AILeadership

[ananddhanabal @ananddhanabal]

🦈 𝐒𝐡𝐚𝐫𝐤𝐬, 🐳 𝐖𝐡𝐚𝐥𝐞𝐬 & 🐬𝐃𝐨𝐥𝐩𝐡𝐢𝐧𝐬: 𝐖𝐡𝐨 𝐑𝐞𝐚𝐥𝐥𝐲 𝐖𝐨𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐒𝐮𝐩𝐞𝐫 𝐁𝐨𝐰𝐥?

4 quarter game-4 hours streaming around $800M in ad revenue- roughly $8 million per 30 seconds. From an executive leader perspective, capital allocation on ads is super critical as 120 million people are watching livestream. This year, AI was on the field and the scoreboard is not just for Seahawks vs Patriots; it is for AI Advertisers vs AI Diffusers.

There are two categories of Ads:

1. 𝐀𝐈 𝐀𝐝𝐯𝐞𝐫𝐭𝐢𝐬𝐞𝐫𝐬 — AI Companies competes for consumer subscription.
2. 𝐀𝐈 𝐃𝐢𝐟𝐟𝐮𝐬𝐞𝐫𝐬 — Big Brands diffuse AI for higher margins.

What I have read and noticed: brands that went all-in on AI, improved production speed by 50% and saved millions in VFX costs — but created AI fatigue, and customers struggled to identify what brand is beyond the AI commercial.

From a unit economics perspective, ROAI (Return on AI Investment) looked great, but other key enterprise metrics such as CAC (Customer Acquisition Cost) and Customer LTV (Lifetime Value) told a different story.

My take: The winner is not the one that has the best advertisement, but the one who can sail through the Sharks, Whales, and Dolphins

(A) Sharks — The Big Brands fighting for the same customer attention.
(B) Whales — The Capitals, the big platforms or the media buyers.
(C) Dolphins — It’s me, the customer, loyal to the brand.

The equation is A + B + C. My reflection: Google (Gemini helping visualize family dreams), Budweiser ("American Icons"), and MrBeast & Salesforce ($1 million puzzle) genuinely differentiated themselves. They sailed through successfully and achieved measurable outcomes.

They didn’t just use AI as a tool. They established personal connections, kept experiences and placed humans at the center of the design.

If I counter-argue: What if two brands compete on the same thesis — can they still sail through? Yes, they can. But the winning strategy is your proprietary customer data, and how you mesh it with post-game conversion funnels and your genuine brand purpose.

Which Super Bowl ads do you think have captured the equation (A + B + C)?

[ananddhanabal @ananddhanabal]

Deterministic Thinking Systems (DTS)

https://t.co/fX4PGUXM2I

[ananddhanabal @ananddhanabal]

@aaditsh Thanks for sharing, Any thoughts on enterprise use cases using MCP?

[ananddhanabal @ananddhanabal]

@JamesLucasIT The true definition of Marvelous :-)

[Tekraj Awasthi🧑‍💻🇳🇵 @trawasthi_ai]

Not able to understand Machine Learning and Deep Neural Networks well?🤔

Here are 6 brilliant visualizations of the ML & Neural Network models, including 3D viz. of LLM 🚀🧵

[ananddhanabal @ananddhanabal]

“The CIO has changed materially…CIOs are not just managing a portfolio of applications…they are also building competency in software engineering, in areas like cybersecurity,” Google Cloud CEO Thomas Kurian says https://t.co/qJKew9DCiv

[Navalism @NavalismHQ]

"You’re going to die one day, and none of this is going to matter. So enjoy yourself. Do something positive. Project some love. Make someone happy. Laugh a little bit. Appreciate the moment. And do your work."

@naval

[ananddhanabal @ananddhanabal]

via @NYTimes https://t.co/9Fyir9kBNO

[Ali @AliTheCFO]

If you enjoyed this thread, retweet it so other people can benefit!

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[ananddhanabal @ananddhanabal]

@barbaraminer4 , Thanks for the great sermon today. Experience our God’s presence through prayer. Very thoughtfully rendered.

[ananddhanabal @ananddhanabal]

When we are caught up in achieving a goal, we can forget to enjoy the process. Here’s how to change that. https://t.co/OZm9OVhPzU

[Databricks @databricks]

Introducing Apache Spark 2.3: Now Available on Databricks Runtime 4.0 Beta
https://t.co/vL3nkS1pTK

[Vikram Takru @VikramTakru]

After watching the @Amazon Alexa Super Bowl ad, if you wished that Alexa was enabled for your enterprise? KloudGin Intelligent ERP Cloud is integrated with Amazon Lex, and did I mention that @KloudGin has not lost its voice :)

https://t.co/IC1JZuSKB7 https://t.co/YZgyVH0suX

[ananddhanabal @ananddhanabal]

https://t.co/OeO64gz69i