@ikuberan
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Silicon Valley weirdo. Building internet money
SF Bay Area
Joined March 2009
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Kuberan Marimuthu (Kube) retweeted
Today we’re launching a library of Amex-curated, ready-to-use skills in Perplexity Computer to eligible U.S. @AmexBusiness Small Business Card Members. The skills are pre-built workflows that handle everyday business tasks, like forecasting cash flow or generating marketing campaign.
Small business owners already do the work of whole teams. They can put AI to work by choosing a task in Computer and adding details about their business, without having to write instructions from scratch.
Learn more: pplx.ai/amex
Kuberan Marimuthu (Kube) retweeted
One of the most interesting things about startups is that there are exceptions to practically every rule. E.g. it's bad to be a solution in search of a problem. Except .5% of the time it's good. The ultimate test of an investor is to see these exceptions.
There is no task more ecologically valid than real work and real results
Intelligence is too cheap to meter, but the data that teaches it what good work looks like is only getting more expensive. In RL, verifiability of outcome drove the capability spike in coding. But most of the knowledge work today has no benchmark. Rubrics today only measure output and ignore the actual business outcome. So just how can we turn real work into a RL task?
Ownership gives us a data advantage in three ways: (1) Signals directly tied to the outcome. Benchmarks today measure a fraction of a job’s day-to-day, typically an artificial single task. (2) Signal diversity from owning and operating multiple businesses in a single vertical. Many businesses, particularly in the same space, face the same problems. (3) An incentive-aligned data flywheel; the people generating the signal are the people who benefit most from the outcome. As we compound value for our businesses, we produce more high-signal outcomes.
An outcome comes months after the hundreds of emails and thousands of decisions behind it. Frontier labs can generate long trajectories but have to invent the reward. Businesses see the real reward but never assign value to the actions that lead to it. Because Nexus runs inside the businesses we own and operate, we have both, and that lets us understand the steps which lead to outcomes. We redefine the structural limitations of credit assignment. There is no task more ecologically valid than real work and real results.
The more businesses we operate, the more results we see, and the faster we close the gap between benchmark tasks and real work.
Models improve and harnesses change. Evals grounded in outcomes will outlast it all.
To AI that lasts.
Congrats @alextaubman Super bullish on this acquisition 🚀
Today, Long Lake completed our $6.3B acquisition of Amex GBT.
Long Lake acquires and transforms generational businesses with AI across the American services economy.
Amex GBT is the travel partner for 17,500 businesses in 140 countries. Last year, Amex GBT booked 35 million trips for 10 million travelers.
This marks Long Lake’s 40th acquisition, and our family of companies now employs nearly 30,000 people.
We founded Long Lake three years ago with the thesis that AI is going to change every company, but there’s a large overhang between AI capabilities and how most businesses leverage AI tools.
We partner with strong, profitable, and growing businesses to help them deploy AI and improve their customer service.
Our first cohort of companies has doubled their EBITDA in less than two years through topline and productivity growth, all while increasing headcount. (We’re proud to say that we’ve never conducted a layoff.)
We’re excited to share a bit more about what we’ve been up to.
In 1965 all the computing on Earth added upto less than one iphone
…
The computing used to train AI now doubles every 6 months
Stop flexing "% of code written by AI." Ask the real question: how much of your line runs without a human in it?
A software factory isn't "engineers using AI." It's a production line where agents write, review, test, and ship, while humans design the line instead of working it.
Kuberan Marimuthu (Kube) retweeted
The founder who ships ugly and learns will always beat the founder who perfects and waits.
AUC for teams is the right metric
It tells you a lot when someone has moved around a lot in their career.
Also tells you a lot when there is low company turnover.
A team that has been working together for 3+ years is significantly higher throughput and more resilient than a team that has just formed.
A key mistake I see investors/employees make all the time is to simply ask about team size as opposed to team longevity.
AUC for teams is the right metric.
Played around with it today. Love the way every step is being converted into a Jev prompt to decide on what screen action to take. A very cost effective and super fast way to do browser use
Breaking: Browser Use + Jev = Ultrafast ⚡
Findings flights took 7s and cost only $0.0039 🤯
> new action space every step
> DOM state space
> small LLM fallback to type
(this video is at 1x speed btw)
Built a tiny open source browser agent. try it below ↓
Kuberan Marimuthu (Kube) retweeted
this is what you're shown on day 1 of YC.
don't be scared of launching early.
Kuberan Marimuthu (Kube) retweeted
A CS undergrad asked me where he could have most effect in the AI age. I said probably at either extreme: either close to the technology, actually making LLMs, or close to the customer, using AI to give them exactly what they want. Or maybe both if you can stretch that far.
Kuberan Marimuthu (Kube) retweeted
A software engineer is now a 100x engineer
A non-engineer is now a 1x engineer
Kuberan Marimuthu (Kube) retweeted
I've intellectually known that software engineering was going to go away but it's just started to feel real.
The little dopamine rushes — learning a language, reconfiguring keybindings, upgrading packages or pushing around code on a page to make it easier to read — fade in the rear view mirror, vestiges of a profession no longer needed.
The job is more director than architect or builder, and quickly rising to higher levels of abstraction. Agents may do the work of spewing code into files and servers, but building ambitious real world software is hard in a new way. We are responsible for houses we did not create and whose floors we've never truly walked.
The longer you've been an engineer in the previous world, the harder the transition is now. But it's time to rewire your brain to appreciate the new ways of how things are going to be built.
The ceiling on what we can do is so much higher now.
Kuberan Marimuthu (Kube) retweeted
EXCITED TO LAUNCH: Akai (akai.run)
Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai.
Akai was an internal tool to automate our painfully repetitive operations in Finance, HR, Accounts Payable, and Compliance, etc. We never intended to make this a product.
But we watched revenue per employee grow from $130K to $215K
We built >8k agents that do the work of ~600 employees
It had such a dramatic impact on our business that today we are launching it for everyone.
How it works: Say you're automating payment reconciliation:
1. Record your screen while manually matching a messy transaction and Akai will capture your screen, voice, server requests
2. Akai will see that you pulled unformatted wire transfer info from an archaic bank portal, put it in some excel sheet, checked NetSuite invoices, payment history, and put a ticket on Zendesk
3. Akai reads between the lines and build a workflow + steps + conditional guardrails. It learns tacit edge cases, like resolving malformed invoice references without you writing a single regex
4. Simply connect NetSuite, your ledger, Zendesk, PSPs, and even legacy bank portals with zero API access
5. Run the workflow and tell it what to adjust in plain English: "strip slashes on wire memos and auto-apply partial payments." It adapts instantly
6. Once it works for you, add 100s of colleagues. Your entire payment ops team forks and extends the workflow for new PSPs, secondary ledgers, or regional settlement rules
7. We automated 85% of our payment reconciliation end to end, eliminating 500+ hours of soul-crushing manual grunt work every single week.
Claude Code/Codex can't do this in multiplayer mode. Every person rebuilds the same skill from scratch in their own way.
Deel built Akai to:
1. Understand backend operations edge cases (it had to work for our 7000 person team first)
2. Collaborative across 1000s of employees
3. Self-Learning from millions of runs
4. Optimises cost and gets cheaper every run
We're so confident that we're announcing an Automation Guarantee:
If our engineers can't automate a thousand of hours of work in your first 30 days, you get a full refund.
Book a demo: akai.run if you're an exec at a company with hundreds of employees
Great one. Adapting to new information matters the most in times when market assumptions change at an exponential speed.
