Puru Dwarakanathan

Passionate about Technology & Innovation | Capital Markets | Early Stage Investor| LimitedPartner@LoyalVC| VenturePartner@WFC | Classic Rock aficionado

[Puru Dwarakanathan @purushottaman]

@EugeneNg Inspirational. Thanks for sharing.

[Puru Dwarakanathan @purushottaman]

@thisisnisakaran @thisisnisakaran thank you for following back. Looking forward to engaging conversations.

[Kyle Harrison @kwharrison13]

Hype is not an effective long-term distribution mechanism. It's a short-term market pull.

Companies born inside a hype cycle are typically in for a rude awakening when the tide goes out on their hype cycle and they realize how little they know about selling.

[Reads with Ravi @readswithravi]

Marc Andreessen on the power of relentless action:

“The world is a very malleable place. If you know what you want, and you go for it with maximum energy and drive and passion, the world will often reconfigure itself around you much more quickly and easily than you would think.”

[Reads with Ravi @readswithravi]

James Clear on balancing gratitude and ambition:

[Michael Bloch @michaelxbloch]

The mid-game is selling AI labor at human-labor prices.

The end-game is using that labor to build the network, context, and trust that become the real company.

Great piece from @jrwoodbridge on what “post-agent companies” might actually look like.

[Puru Dwarakanathan @purushottaman]

@devlikesbizness @Compounds365 @PEoperator second that:)

[Fede Sandler @FedexSTi]

El "Engaño" del Promedio: Por qué $NU y $MELI son máquinas de tiempo. Encima hoy reporta $NU AH. Parece joda el timing.

Hoy en clase vamos a analizar por qué mirar el ingreso promedio (ARPAC) puede darte una visión totalmente distorsionada de la realidad. El secreto para entender a los gigantes de Latam no está en el promedio, sino en la maduración de cohortes.

El caso de $NU :Muchos inversores ven el promedio de $11 y asumen que eso vale cada cliente. Gran error.

Como muestra el slide:

Un cliente nuevo (Año 1) genera solo $4.

Un cliente maduro (Año 5+) genera hasta $25.

Si solo miras el promedio, estás a ciegas. No puedes ver si el negocio está mejorando o si los clientes que captaste en 2024 están subiendo por la rampa más rápido que los de 2021. La clave es la velocidad de la rampa, no la foto de hoy.

¿Cómo aplica esto a Mercado Libre ($MELI)? $MELI es el master del "Land and Expand":

Land: Te atraen con el Marketplace (compras un producto, margen bajo).

Expand: Una vez dentro, te convierten en usuario de Mercado Pago y, finalmente, de Mercado Crédito.

Al igual que en $NU, la rentabilidad de $MELI no es lineal. Un usuario que usa el ecosistema completo es exponencialmente más valioso que uno nuevo. Cuando los cohortes más jóvenes (como México o Chile) alcancen la madurez que ya tiene Brasil, la rentabilidad total va a explotar.

Conclusión: El promedio de $11 es una trampa que infla el flujo de caja del primer año y arruina cualquier modelo de valoración (LTV). No hay que inviertir en promedios; hay que invertir en empresas que saben mover a sus clientes hacia arriba en la curva de valor.

Abrazo buen dia

[Nicolai Tangen @NicolaiTang1]

'Done is better than perfect' said President of the The World Bank Group, Ajay Banga, on our Investment Conference about what makes a winning culture. I must say I agree with him! Tune in here: https://t.co/32KXG7haVt

[Jaya Gupta @JayaGup10]

https://t.co/5vDrndcmFn

[Puru Dwarakanathan @purushottaman]

@ShahRathin Great write up.. thanks for sharing the shifts the industry is heading to.

[tobi lutke @tobi]

https://t.co/6i6h3sKi3x

[Vidhya Srinivasan @VidsSrinivasan]

The Universal Commerce Protocol is taking a major step in building the future of agentic commerce with the expansion of its Tech Council. Welcome to @Amazon, @Meta, @Microsoft, @Salesforce and @Stripe.

The success of UCP is an industry-wide effort that requires a true ecosystem approach. Welcome to the new partners joining us to build the future of agentic commerce! 💪

[Puru Dwarakanathan @purushottaman]

@jainarvind 100% agree to your rationale and insights. I would also argue that processes are not yet mapped or ripe for Claude cowork to work efficiently. We need to establish an approach, how we can start mapping the processes across the functions without the tedious effort.

[Jaya Gupta @JayaGup10]

https://t.co/Lsuyx2hvpA

[Brett Caughran @FundamentEdge]

Great take from Aaron Levie (who is full of great takes on AI, highly encourage a follow).

Hearing a lot of the same in my conversations on Investing AI.

Principally the big shift in enterprise strategy. The bulk of internal AI work over the last 36 months has been about feeding research context (filings, transcripts, sell-side reports, internal notes) into internal multi-model chatbots. Then distributing that tool out to investment teams, building internal prompt libraries, and leveraging the "let a thousand flowers bloom" approach to use case best practices. Nothing from this strategy has really driven meaningful changes to the institutional investment process. Though there have been incremental interesting use cases.

There's a growing realization that agentic workflows require a new enterprise strategy, and can now for the first time serve a coherent, institutional-grade operating chassis for the investment process. As Aaron says, this requires "a shift to targeted automation efforts applied to specific areas of work and workflow", which, if not obsoletes, at least de-emphasizes the prior iteration of grounded chatbots.

However, has the potential to lead to incredibly dramatic shifts in the way investors work.

[Aaron Levie @levie]

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise.

Some quick takeaways:

* Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow.

* Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated.

* Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs).

* Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these.

* Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs.

* Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy.

* Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems.

* Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been.

One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise.

This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

[Kpaxs @Kpaxs]

High-agency people seem to have insane luck. They don't. They just tried 47 things while everyone else tried two and gave up. The conviction that reality is negotiable is generative, it makes you creative. Because if you believe there's always another angle, you start looking for angles other people don't see.

[Elon Musk @elonmusk]

Starlink is alien-level technology

[a16z @a16z]

Inference (not training) is projected to drive data center buildout.

More charts: https://t.co/OgJQcBSNjy