Akshay Umashankar

GTM at @Abnormal security; previously PANW, Bain

[soha ventures @sohaventures]

Some of my favorite ETA/search resources compiled from my bookmarks (1 of 16 posts):

1. SDE vs EBITDA vs Cash Flow - @guessworkinvest
https://t.co/arJtUCMSJD

[Dean W. Ball @deanwball]

amazing to do a bit on this website and then see it get obsoleted in the same hour

[market participant @LaurelonCapital]

Many are saying OpenAI’s $10B private equity joint venture is giving a “desperate” feeling

Vendor financing dressed up as venture capital. Guaranteed returns that smell like a Ponzi. A company burning $14B this year buying distribution it can’t earn organically.

I’d push back…

TPG, Bain Capital, Advent, and Brookfield put up ~$4B. They get preferred equity with a 17.5% minimum return, board seats, and early access to OpenAI’s newest models.

OpenAI gets instant distribution into hundreds of portfolio companies. No traditional enterprise sales cycle. No selling company by company.

The part people are missing - a huge number of software companies are privately held. Thoma Bravo alone owns 75+ software companies generating $30B in annual revenue. PE firms collectively did $1.8 trillion in buyouts last year.

These companies are shifting to heavy token consumption. AWS knows this. They run a dedicated PE sales function - advisory programs, transformation consultants, the whole apparatus. Azure and GCP do the same.
OpenAI is running the cloud distribution playbook. Wholesale tokens to PE portfolios at scale.

The difference is exclusivity.

AWS doesn’t ask PE firms to put up $4B and take board seats. They give the advisory away. OpenAI is asking for capital commitment, which buys preferential access, dedicated forward deployed engineers, and a financial incentive for the PE firm to actually push adoption across its portfolio.

A PE firm that invested $4B in this JV isn’t running a parallel RFP with Anthropic. That’s the point.

The 17.5% return looks like a red flag until you look at it closely. It’s a preferred equity hurdle, not a coupon on debt. Priority on returns before common shareholders. Standard PE structuring. The number is high, but OpenAI is buying a captive distribution channel into enterprises they’d otherwise spend years trying to reach.

Anthropic is running a parallel deal with Blackstone, Hellman & Friedman, and Permira. ~$1B. Common equity, no guaranteed return. More of a Palantir-style consulting venture.

Both companies doing this simultaneously tells you where enterprise AI is heading.

The bear case is real. 95% of enterprise AI pilots fail to deliver ROI. $14B in projected losses this year. Profitability not expected until 2029.

All true… and OpenAI’s enterprise business is already $10B of $25B in revenue. 40% and growing to 50% by year end. The distribution problem is what’s left to solve.

Anthropic wins bottoms up. Developers love Claude. The product sells itself in technical orgs.

OpenAI could win top down. The PE JV is a bet that enterprise AI follows the same pattern as cloud - whoever locks in institutional relationships and procurement contracts early owns the market for a decade.

Orlando Bravo called AI valuations a bubble and walked away. He could be right. But the firms that showed up are managing the math differently - $4B for a preferred return and a front-row seat to deploy AI across their entire portfolio.

Distribution, not desperation.

[ib @Indian_Bronson]

The single most important thing is to just keep at it.

Whenever you're stuck, take screenshots, explain where you are to a regular LLM (pay for one!), and get unstuck. They're infinitely patient and very adept.

By the end of weekend you can be 100x more capable.

[Akshay Umashankar @AkshayUmashank3]

@nickco @FlorinTatulea @Replit DM’d!

[Akshay Umashankar @AkshayUmashank3]

@NauHaumau Sora

[Akshay Umashankar @AkshayUmashank3]

@cgihou Done, thanks

[Akshay Umashankar @AkshayUmashank3]

@cgihou filed for my passport renewal 3 weeks ago (Tatkal). Confirmed that police verification is complete. Yet, the status page on VFS shows an error.
Can you help?

[Akshay Umashankar @AkshayUmashank3]

@JDonovanWx @SickosCommittee @CFBHome @BarstoolBigCat did you write this?

[Akshay Umashankar @AkshayUmashank3]

@nicochristie Would love a code to play around with this. The demo looks super impressive. My team would get a ton of leverage out of this tool

[Akshay Umashankar @AkshayUmashank3]

@nicochristie Fascinating, would be for my team in a heartbeat

[Prompter @PromptLLM]

Prompts that changed my life. Part 9

[nick.eth @nicksdjohnson]

Recently I was targeted by an extremely sophisticated phishing attack, and I want to highlight it here. It exploits a vulnerability in Google's infrastructure, and given their refusal to fix it, we're likely to see it a lot more. Here's the email I got:

[Alex Prompter @alex_prompter]

Steal my prompt to work backward from your ideal outcome and map the exact path to get there.

----------------------------------------
REVERSE ENGINEERING STRATEGIST
----------------------------------------

REVERSE GENIUS PROTOCOL: Outcome-to-Path Mapping System

You are now REVERSE STRATEGIST - an expert system designed to work backward from ideal outcomes to create precise, actionable pathways to achievement. This system uses reverse engineering, constraint mapping, and milestone sequencing to transform ambitious goals into executable plans.

REVERSE ENGINEERING METHODOLOGY

When presented with any desired outcome, implement this backward-mapping process:

1. OUTCOME CRYSTALLIZATION
Refine the stated goal into a precise, measurable end state
Define clear success criteria and evidence of achievement
Identify the core components that constitute "success"
Establish the exact parameters of the desired outcome

2. PREREQUISITE CHAIN MAPPING
Identify the immediate prerequisites for the final outcome
For each prerequisite, map its own prerequisites
Continue backward until reaching currently available resources
Create a complete dependency network from end to beginning

3. CONSTRAINT IDENTIFICATION
Map all potential obstacles and limitations
Identify resource constraints (time, capital, skills, connections)
Detect environmental or external limiting factors
Recognize internal constraints (habits, knowledge, psychology)

4. CRITICAL PATH CONSTRUCTION
Identify the sequence of must-complete milestones
Determine rate-limiting steps in the process
Map the minimum viable path from current state to goal state
Identify potential shortcuts and acceleration opportunities

5. LEVERAGE POINT DETECTION
Locate high-impact, low-effort actions
Identify force-multiplying resources or connections
Detect threshold moments where small inputs create large outputs
Find catalytic actions that activate multiple pathways simultaneously

IMPLEMENTATION STRUCTURE

Structure your reverse engineering analysis in this sequence:

PHASE 1: OUTCOME DEFINITION & PREREQUISITES
🎯 PRECISE OUTCOME
[Clearly defined end state with specific success criteria]

⬅️ FINAL PREREQUISITES
[Direct requirements that must be satisfied immediately before achievement]

⬅️ SECONDARY PREREQUISITES
[Requirements needed to fulfill the final prerequisites]

⬅️ FOUNDATIONAL PREREQUISITES
[Fundamental elements needed to begin the journey]
PHASE 2: PATHWAY CONSTRUCTION
🚧 CRITICAL CONSTRAINTS
[Major limitations that must be addressed]

🛣️ MILESTONE SEQUENCE
[Ordered sequence of achievement points from start to finish]

🔑 KEY LEVERAGE POINTS
[High-impact actions and resources that create disproportionate progress]

⚡ ACCELERATION OPPORTUNITIES
[Potential shortcuts or parallel processes to speed achievement]

PHASE 3: EXECUTABLE STRATEGY
📋 ACTION PLAN
[Specific, sequenced steps from current position to desired outcome]

📊 PROGRESS METRICS
[Indicators to track advancement along the critical path]

🛠️ REQUIRED RESOURCES
[Tools, skills, connections, and assets needed at each stage]

⚠️ CONTINGENCY ROUTES
[Alternative paths if primary route encounters obstacles]

ACTIVATION
When presented with a desired outcome, begin your analysis by saying:

"I'll apply the REVERSE GENIUS PROTOCOL to work backward from your ideal outcome and map the precise path to achievement. By starting at the end and identifying each prerequisite step, we'll create a clear roadmap from your current position to your goal."
Then implement the reverse engineering methodology to create a precise, actionable pathway from current state to desired outcome.

[Sebastian Siemiatkowski @klarnaseb]

Yes, we did shut down Salesforce a year ago, as we have many SaaS providers—an internal estimate is about 1,200 SaaS shut down.

No, I don't think it is the end of Salesforce; might be the opposite.

Here is what actually happened and how/why we originally intended to NOT share it publicly:

At Klarna, we decided early to explore the potential of AI and LLMs—mostly ChatGPT—while being open to testing all things that seemed to be trending.

We encouraged all employees to do so and allowed them to pursue ideas organically rather than following "management direction" on exactly what they should be building.

In the early days of ChatGPT, we heard a lot:
"this tool allows you to feed all your PDFs, all your data sources to a LLM!"
However, the old universal truth of data scientists still holds true, even in AI: "shit in, shit out."

Feeding an LLM the fractioned, fragmented, and dispersed world of corporate data will result in a very confused LLM.

We started instead exploring a few key concepts: What of our data was actually valuable? What data was duplicative, incorrect, or contradicting? Why was it like that?

While people nowadays can criticize things like Wikipedia, we also reflected on the fact that it is a remarkable achievement—having over 20,000 people collaborate on the largest graph of knowledge that is still fundamentally of high quality, accessibility, and accuracy. What could we learn from this?

A Swedish company, @neo4j, and @emileifrem introduced us to the beautiful world of graphs.

We further explored data modeling, ontology, and, of course, vectors, RAGs, and many things.

Key to our explorations became the conclusion that the utilization of SaaS to store all forms of knowledge of what Klarna is, why it exists (docs), what it tries to accomplish (slides, tickets, kanban boards), how it is doing (sheets, analytics), who is it dealing with (CRM, supplier management), who works here (ERP, HR) and what it has learnt was fragmented over these SaaS—most of them having their own ideas and concepts and creating an unnavigable web of knowledge that required a tremendous amount of Klarna specific expertise to operate and utilize.

We also recognized that enterprise software has a standard set of features that are vital for it to operate—features such as audit, versioning, access and edit management, and similar universal needs. We need them as well, but that fragmentation again adds friction, admin overhead, and more.

So, we decided to start consolidating; to put things together, connect our knowledge, and remove the silos. The side consequence of this was the liquidation of SaaS—not all of them, but a lot of them. And not for the license fees, even though those savings have been nice, but for the unification and standardisation of our knowledge and data.

So no, we did not replace SaaS with an LLM, and storing CRM data in an LLM would have its limitations. But we developed an internal tech stack, using Neo4j and other things, to start bringing data=knowledge together.

Ultimately, we found this very interesting, but more importantly starting seeing serious productivity gains. We allowed our internal AI to use this knowledge, and we realised with the help of @cursor_ai we could quickly deploy new interfaces and interactions with it.

So, I discussed with one of my board members: should we share this publicly?

We decided not to. We hold no grudge against SaaS (not true—I hate some of it, but won't tell you which one). But we are a payments company and a neo bank, there is limited value for us to share this externally.

However, Klarna, being a bank, holds quarterly calls with its investors, and in passing on of these calls, I mentioned that we had removed some SaaS software including Salesforce. It turns out that the recording was leaked to @SeekingAlpha, and they put out a news post about it. And from there, it went crazy.

Suddenly, @Benioff was asked on stage why Klarna was leaving Salesforce. I was tremendously embarrassed.

So, to summarise, what does this mean? Will all companies do what Klarna does? I doubt it. On the contrary, much more likely is that we will see fewer SaaS consolidate the market, and they will do what we do and offer it to others. Those are likely to be your next SaaS.

And it is very likely that Salesforce will be one of those companies. As highlighted many times, they do so much more than CRM today and hence have the opportunity to become that hub of knowledge that modern companies will seek.

But there are also risks for them and others; a lot of our large enterprise SaaS providers suffers from a fallacy. They started as companies with a clear opinion of how to do things, but over time, as they try to satisfy every whim of any random person working at any large enterprise, they become somewhat of a glorified database and lose their opinion. Opinionated software is worth something, as opinions represent an experience of what works, what produces results. And this is the ultimate value.

So I hope with sharing this we can clarify a lot of speculation and misunderstandings and in the end same thing as is always true, just like when mobile came along, we talked about mobile first, now you need to be AI first. Of course all SaaS companies will need to learn adopt and evolve. But if they do there is tremendous opportunity ahead.

[Akshay Umashankar @AkshayUmashank3]

@robertmays Done, thank you!

[maharshi @maharshii]

i have noticed that LLMs like claude and gpt-4o work really well with this prompt, it instructs them to 'contemplate' for a bit before giving the final answer.

[Siqi Chen @blader]

if you could press a button that cures your child’s brain tumor in exchange for ending your life immediately, every parent would hesitate for zero seconds before fighting to be the first to press it

the cruelest thing is that no such button exists.

but there is always a move 👇

[Akshay Umashankar @AkshayUmashank3]

@nikesharora try Uchiba - sister restaurant, same chef. 5 minutes away from Uchi, and most importantly also has the fried milk :).
Seeing a few open reservations for every night this week.