99% of Ramp uses ai daily. but we noticed most people were stuck — not because the models weren't good enough, but because the setup was too painful and unintuitive for most. terminal configs, mcp servers, everyone figuring it out alone.
so we built Glass. every employee gets a fully configured ai workspace on day one — integrations connected via sso, a marketplace of 350+ reusable skills built by colleagues, persistent memory, scheduled automations. when one person on a team figures out a better workflow, everyone on that team gets it and gets more productive.
the companies that make every employee effective with ai will compound advantages their competitors can't match. most are waiting for vendors to solve this. we decided to own it.
Since 2023, the top quartile of AI spenders on @tryramp have more than doubled their revenue. Bottom quartile? Flat
A roofing company in Texas. A window installer in Utah. A construction firm in Florida that grew 65%
The gap is accelerating and most companies don't feel it yet
AI has become the justification for every layoff. It's the perfect excuse card, but there is a lot of spin involved. Every layoff is some combo of the following five very different AI stories.
1. Nothing changed, we just realized we have too many people. We are going to blame AI, but we are bullshitting. This is the AI as an excuse; it was really sloppy hiring, and we are just blaming AI. (See Block)
2. Growth has gone away so now we have too many people. This may be because of AI if you are a SaaS company. All the customer love is now going to AI. But it's less AI as a productivity lift, and more about you just building a less ambitious growth company. (See Salesforce and most every SaaS company)
3. We spent our money on capex to build AI so now we can’t afford as many people. Management may say it’s about AI making us productive (4 below) but my gut is a lot of it is about Nvidia getting our money so now there is none for you. (See Meta and Oracle)
4 We are really using AI the way god intended us to. We don't need as many people. This is the ONLY version of the story that is actually about a productivity increase. It's real, it's happening, but I wonder if it is even the majority of the layoffs. (See some software engineering departments right now)
@jasonlk raised a fifth reason that doesn't get talked about enough: we just have the wrong people. Maybe we don't need 20 engineers who all know C++, but rather eight who have strong AI skills. This I think should be happening everywhere.
Every time a layoff announcement comes out, I try and mentally categorize per the above.
Claude will be the biggest software procurement platform in tech. And they aren't even trying to be (i don't think).
Every time you use Claude Code, your infrastructure is now implicitly auditing your vendor stack.
And unlike your engineering team, it has no vendor loyalty and there are very little switching costs.
Everything just looks like code. And code is now extremely inexpensive.
Claude is about to drain the moat.
I'm here for it.
Incredible wake up call masqueraded in an X article from the indomitable @clairevo..
If you’re a CEO, please read this and start now.
Don’t hire a Chief AI officer and call it done - you’re a dead company walking just buying more time. This needs to start with you!
If you’re responsible for AI in the enterprise, read this. The book for anyone currently navigating an AI transformation program while being responsible for supporting all of the stakeholders. @jeffrschneider is a practitioner
Offloading: The Agentic Enterprise
by Jeff Schneider
How to transform your organization by delegating work to AI agents, and why the companies that move fastest will define the next era of business.
Now available: https://t.co/QZ6iH2GpzJ
Offloading: The Agentic Enterprise
by Jeff Schneider
How to transform your organization by delegating work to AI agents, and why the companies that move fastest will define the next era of business.
Now available: https://t.co/QZ6iH2GpzJ
Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work:
“If you just buy LLMs off the shelf and try to do any of this, it won’t work.”
“It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.”
“People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.”
“What you’re going to see, especially in America, is people trying to do something like Ontology by hand.”
“Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.”
“There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.”
“Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’”
“Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”
This is BEYOND insane:
AI compute demand is now growing at over 2 TIMES the rate of Moore’s Law, creating a massive shortage.
Just to meet current demand, $500 billion must be invested in data centers PER YEAR until 2030.
What does this mean? Let us explain.
(a thread)
APIs are the bridge between enterprise data & AI Agents.
But agents don’t guess. They skip bad docs.
They need structured, discoverable, semantically rich APIs.
New blog from @godigitalML recaps our @nordicapis panel:
https://t.co/XeUeFCauc6
@johnbogard from @godigitalML joins a panel of API experts on June 25 to unpack what holds companies back from building long-term API value - and how to move past it.
Together, they'll share strategies that work across industries.
Register: https://t.co/o8pKd60VOS