Andreas Lordos

@llmh

[Andreas Lordos @aglordos]

@ivanburazin Intercom is a great case study as well

[Andreas Lordos @aglordos]

@ivanburazin amazing work

[Andreas Lordos @aglordos]

@willdepue

[gabriel @gabriel1]

every enterprise will soon want ai for everything all at once because it will very soon work, and there wont be enough FDEs and consultants to add ai fast enough

[will depue @willdepue]

remember just three years ago seb asked gpt-4 to draw a unicorn

[Mark Yi @ SpaceXAi @himarkyi]

i think about this every single day

[David Senra @davidsenra]

The best founders in the world like @elonmusk or @tobi “go ultra-hardcore on deletion and simplification.”

Luca Ferrari says Bending Spoons employs this idea of “Relentless Simplification” across their businesses:

“Humans have a tendency to add complexity and do things that destroy value.”

“If you leave an organization, almost any environment unattended, and you don't provide guidance in this regard, it will tend to become more complicated. People will add parts. It could be expanding a team or adding a step to a process, or adding an entire new process. If it's a product, adding a feature to the product. New rules. It applies to almost any human endeavor.”

“People tend to add pieces, they very rarely remove pieces.”

“If you don't make a conscious effort to achieve simplicity, and avoid this increasing value-destroying complexity, that's how we got to our modern society with all the bureaucracy and complicated regulation.”

“We have this principle whereby we ask everyone who works here, every time someone is suggesting that we should be adding complexity, the burden of proof is on those making that suggestion. The people who support the thesis that we shouldn't be adding complexity don't need to prove it. They just have to raise a flag and say, ‘I don't think we should.’ The burden of proof is on those who want to add complexity.”

“The other part of relentless simplification is that we want people to be on the lookout for existing complexity and suggest that we should remove it.”

[Varun Shenoy @varunshenoy_]

at long lake, we've bought 40+ services businesses to build AI where work actually happens

we're working on:
- deploying agents to complete knowledge work
- frontier eval generation from real workflows
- post-training open models on data no lab has

come build with us @llmh

[Emm | scenario.com @emmanuel_2m]

@LEGO_Group Here's another example. The Palace of Fine Arts in San Francisco, from one image..

I know Kung Fu 😂

[Anthropic @AnthropicAI]

Today, we're kicking off the first phase of the research preview for Model Hardware Standard (MHS): a new standard for AI agents to safely operate physical equipment in scientific research and advanced manufacturing.

Read more: https://t.co/XQ2y9EW7Af

[Andreas Lordos @aglordos]

@actavisprime01 @clemissima "Besides the obvious"

[Andreas Lordos @aglordos]

@clemissima Yeah of course! Try Sugar & Spice if you have time -- it's one of two restaurants in Massachusetts certified by the Thai government as genuine

[Andrew Ho @andrewho03]

Even though it’s fun to tease him for nearly blowing up, I think it’s important to remember that Leopold really is an impressive man who had the courage to execute on his convictions. He laid out his playbook in public, even, and we could all have copied him and become tremendously wealthy; I imagine few of us did so. Certainly not me. And unlike SBF, Bill Hwang, etc., there was no deception at any point. I respect the execution.

[prinz @deredleritt3r]

Generally speaking, I believe that software-only RSI is possible. Some of the recent math results have made me somewhat more convinced of this.

What happens after RSI is very hard to say. There will be bottlenecks of all kinds: availability of compute and human verification are just two that one should keep in mind. I do wonder how many of these bottlenecks can be solved by pointing increasingly powerful AI models at them.

[Andreas Lordos @aglordos]

@SanjithUdupa Jarvis, make this man dripped out

[Andreas Lordos @aglordos]

@SanjithUdupa Ramp card maxxing

[Andreas Lordos @aglordos]

@SanjithUdupa this and reducing cold starts by 50% >>>

[Elad Gil @eladgil]

Long Lake did the world's first (to my knowledge) AI-driven take private of a public company

-$6.3B acquisition
-100 year old Amex Global Business travel
-Plan to transform it with AI

Spoke w CEO Alex Taubman on @NoPriorsPod about it in his first public podcast ever

[Russell Kaplan @russelljkaplan]

# The Path Forward for AI Startups

A lot of founders are messaging each other after the SpaceXAI <> Cursor “IPO-deferred acquisition”. Common discussion topic: what is the future for independent startups? Must ~everyone ultimately be acquired by a frontier lab or go extinct?

The data from our direct experience @cognition suggests the opposite. The more startups in a category that defect from independent competition by selling to a lab, the stronger the remaining ones become. We experienced this firsthand last year with Windsurf. When the founders went to Google and we acquired the remaining company, it dramatically accelerated our product roadmap and GTM. Now, cloud agents are ready for prime time, and our usage has exploded. (We’re in the fastest rate of usage growth in Cognition’s history - almost 50% month-over-month growth in Devin enterprise.) We already see the next round of acceleration with yesterday’s news, from prospects and customers to candidate inbound.

In just about every category, there’s a clear market for a winning independent offering that’s not tied to models from any one lab. Especially in a space as dynamic as software engineering, where customers value model flexibility as the rankings from different providers are constantly changing.

For startups to seize that independence opportunity, here are the lessons we’ve learned so far:

1. DIFFERENTIATION
You need to have extremely clear differentiation vs. what’s already offered by the labs. Cursor had stiff competition from Claude Code in self-serve, in part because one tool was substitutable for the other, which presented a challenge.

Our approach has been to differentiate heavily for enterprises, which is the largest market for software engineering. Specifically:
1. We invest as much in forward deployed engineering and AI enablement as we do in core R&D. Our customers treat us as a change management partner, not just an AI software engineering platform. We run 1000-person workshops all around the world to help train developers inside companies on frontier AI adoption. We target specific use cases and outcomes in addition to providing developer tooling.
2. We focus on accelerating the *entire software development lifecycle* at large company scale, not just the writing of code. Devins now spin up automatically for everything from ticket scoping to DeepWiki codebase indexing to security vulnerability remediation and application monitoring alert response.
3. We eat the pain of deployment complexity to work well in the largest and most complex environments imaginable. Cognition can run inside a customer’s virtual private cloud, has a permissioning and team collaboration model that can scale to 100,000+ developers inside one company, runs as well for COBOL mainframes as it does for modern Python. From day 1 each Devin ran in a microVM on its own machine, vs running locally as a CLI tool, which allows arbitrary horizontal scaling and is a better fit for event-driven automation.

Of course, one element of startup differentiation will always be model independence. This is particularly powerful in large enterprises, who value supplier continuity and the ability to centralize tooling without taking on the business risk that they committed to the wrong foundation model. And useful for individual developers, who always want to try the latest models. (If you haven’t yet tried the Windsurf 2.0 release which came out last week, it’s a good day to give it a shot!)

I expect the labs will catch up on some of these fronts at some point. But at that point, we’ll have already made the next leap in differentiation, because…

2. FOCUS
You won’t outcompete the labs in everything, but you can outcompete the labs in *your* thing. Every application domain has fractal complexity at the edges. Lean in to what makes your domain special and offer things no one else can. Does it make sense for a lab to devote training resources to a specialized code review model? Probably not - they’re working on AGI. But for the 3-6 month window where the latest frontier models don’t solve that use case at acceptable performance, cost, or latency, do it yourself and build a better product experience than would otherwise be possible. Rinse and repeat as the frontier of what’s possible via specialization continues to evolve.

3. VELOCITY
One of our values at Cognition is: “Every second counts.” Maniacal urgency helps in every startup, but it counts extra in today’s accelerated AI times where advantages compound faster than before. With sufficient focus, you can out-accelerate the AI labs on any one specific feature or workflow. Do this consistently to stretch the overhang of what’s enabled by each new generations of models, and you can maintain your edge on a differentiated product experience.

-

In many ways the SpaceXAI <> Cursor news is a win for everyone. SpaceX gets a new research team and the chance to become competitive in coding. Cursor gets a meaningful exit and the opportunity to accelerate their research roadmap with much more compute. And the whole ecosystem benefits from increased competitiveness among the foundation model labs. Congrats to the teams on the outcome.