Arjun Karanam

cofounder at Trajectory (@trajectorylabs) | prev @stanford, @apple

[Arjun Karanam @QuantumArjun]

I first met @rronak_ and @MichaelElabd when we were all freshman at Stanford. Today, 8 years later, we’re announcing that we’ve started Trajectory, a research lab and product company building the platform for continual learning.

I believe that Continual Learning demands a fundamentally new interface for how we build products. That's a research challenge and a product challenge in equal measure, so we've assembled a team to meet both: researchers from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, and Scale AI, and product talent from Stripe and Figma.

We’re also partnering with the best AI native companies @Clay, @Harvey, @Decagon, @Mercor, and @RogoAI to power their agentic experiences, and push the boundaries of what agents look like in the real world. Please reach out if you’re excited to build with us!

[Ronak Malde @rronak_]

Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory.

We are a research lab and product company building the platform for Continual Learning.

Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs

We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more.

We’re partnering with some of the best AI-native companies: @ClayRunHQ @Harvey, @DecagonAI, @mercor, @RogoAI to power their agentic systems, some of which we are already in production with.

We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma.

AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

[Gabe Pereyra @gabepereyra]

Awesome seeing @JensenHuang talking about @harvey and @trajectorylabs work with Nemotron at GTC

[Arjun Karanam @QuantumArjun]

@martin_casado hardware comfort and content were always the biggest problems imo, and it feels like both are pretty much solved now

[Arjun Karanam @QuantumArjun]

@willdepue did you get it

[Trajectory @trajectorylabs]

We trained NVIDIA Nemotron 3.5 Super VL on Harvey’s Legal Agent Bench.

It now scores level with Opus 4.7 on rubric criteria, at $1.34 per million tokens vs $15. 🧵

[Arjun Karanam @QuantumArjun]

@kickingkeys @GoogleAIStudio 😍

[Arjun Karanam @QuantumArjun]

@majamediaco reporting in as a huge diner fan

[Arjun Karanam @QuantumArjun]

@raizamrtn congrats!! so exciting

[Arjun Karanam @QuantumArjun]

Welcome to the Era of Experience?

[Pope Leo XIV @Pontifex]

Intelligence must draw on memory. The mind must not simply compile data — as an algorithm now does more quickly than we can — but recall lived experiences, which contain depths of meaning that only the human soul can recognize, and connect more deeply with the goodness of these experiences. Everything that is good begins “with the Spirit” and thus possesses a breath, a dynamism and a sense of communion that, once experienced, are engraved upon the heart. https://t.co/Ij21tkb3qT

[Arjun Karanam @QuantumArjun]

@lu__jasper Somebody should work on that

[Arjun Karanam @QuantumArjun]

Huge reason why “a/b test to success” is a beige (leaning red) flag to me during product interviews

[Ryo Lu @ryolu_]

convergence to the mean

when everyone looks at everyone else to decide what to make, we eventually stop making anything of our own.

we copy what works. borrow the language, the aesthetic, the features. we call it learning from the market, following best practices, staying competitive. together, everything starts to feel familiar. polished, interchangeable. nothing wrong. nothing alive.

the mean isn’t necessarily bad. it’s where everything lands when we remove the reasons for it to be different.

numbers are easier to compare than feelings, so we make things that produce numbers. we study what gets attention, then shape ourselves around it. eventually the feedback stops informing the work and starts authoring it. we’re no longer asking what we want to say. we’re asking what will be rewarded for saying.

ai makes this loop frictionless. it can draw from more references than we could see in a lifetime. but access to every perspective isn’t the same as having one. it hasn’t lived a particular life that makes one detail unbearable and another beautiful. when we ask it to choose for us without bringing any intention of our own, we get something plausible, easy to approve.

then we ship it, measure it, copy it, and feed it back into the next round. a tidal wave of mid.

this changes not just the work, but the people making it.

we describe ourselves in categories platforms can distribute and markets understand. designer. founder. creator. pick a niche. build a brand. be consistent. the parts that don’t fit become inconvenient, even when those contradictions might produce our most interesting work. we become easier to classify and harder to tell apart.

i think we need to approach making things more like artists.

an artist develops a perspective through the work. making, noticing, choosing, failing, returning. learning what matters even when nobody else cares. craft gives that perspective a form precise enough for someone else to experience.

the life outside the work matters too. the places you’ve lived, the people you’ve loved, the things you’re embarrassed to care about. these aren’t distractions from having something to say. they’re how you come to have something to say.

we understand this with music, paintings, photographs, movies. we expect a person to be present in the choices. we want to experience something we wouldn’t have encountered without them.

software can do this too.

a piece of software expresses beliefs about how life should feel. what deserves your attention. what you should be free to change. whether a task should feel like paperwork or play. even a small interaction can carry someone’s care, humor, or curiosity.

but we rarely talk about software as a medium. we talk about it as an industry. markets to capture, workflows to optimize, competitors to catch up with. we add a feature because someone else has it, without asking whether it belongs. products accumulate the same parts and lose what made them worth choosing.

underneath the branding, it’s all the same thing.

artists learn from others too. the question is whether you’re absorbing those influences into a point of view, or using them to avoid developing one.

what are you trying to let someone feel, understand, or do? why this particular shape? what have you noticed that the existing tools don’t seem to notice?

those questions don’t have answers you can borrow from a competitor’s roadmap.

if we can make almost anything, deciding what deserves to exist becomes more important, not less. speed gives us more chances to exercise judgment. it doesn’t remove the responsibility.

i don’t want a world where every person becomes a more efficient distributor of the same ideas. i want people to use these tools to go further into what they actually care about. to make things specific enough that we can feel someone behind them.

what is the point of everyone being able to make something, if we all end up making the same thing?

[Arjun Karanam @QuantumArjun]

@pranavmitl Coooooooking

[Arjun Karanam @QuantumArjun]

@yannietanmusic So cool!!

[Arjun Karanam @QuantumArjun]

@SpaceX 😍

[Arjun Karanam @QuantumArjun]

Honestly a fun trip down memory lane - for your consideration

[Arjun Karanam @QuantumArjun]

@joshelman Huge +1 on For All Mankind

[Arjun Karanam @QuantumArjun]

@floguo @emguoz @NotionHQ Woahhh so fun

[Arjun Karanam @QuantumArjun]

@jeffreykWNDR So excited for what this world looks like

[Arjun Karanam @QuantumArjun]

@brianryhuang yessirr

[Arjun Karanam @QuantumArjun]

I got nerd sniped by all the Opus 5.5 videos yesterday and this is the result 😅

[Trajectory @trajectorylabs]

There’s a good chance your open source model is costing more than the frontier.

Cheap tokens ≠ cheap tasks.

Here, we introduce Intelligence Density, and Density Aware Training, our post-training technique to achieve less wasted compute, better learning, all with no knobs to tune.

Enabled by default in every Trajectory model.

[Arjun Karanam @QuantumArjun]

@kylejeong I was probably going back and forth with it for about a day, roughly 2-3 times an hour?

[Arjun Karanam @QuantumArjun]

@kylejeong oh man I wish it was only one prompt