Public posts from the accounts Zeitwire monitors, in the selected window. Findings need three supporting authors and a published source. Announcements can cite one known-affiliated account. This is a sample of the conversation, not a survey or a measure of adoption.
The posts behind the picture
Public source posts
@alexatallah
Known affiliation
The biggest lie in AI is that the future belongs to one winner.
Every technology wave starts with one company promising that one walled garden is all you'll ever need. And every time, market forces and creativity break the walls down.
I cofounded OpenSea and now OpenRouter around the opposite bet: humanity will refuse to be locked in.
Intelligence was never meant to belong to one lab, one model, or one gatekeeper.
The future of AI is open, plural, and yours to choose.
let me tell you something my friend
this is not the time to cancel subscriptions
this is not the time to play it safe
this is not the time to "wait and see"
right now is the time to go fucking ALL IN
>max out your Claude sub
>burn through your ChatGPT plan
>spend all your tokens on OpenRouter and top up AGAIN
it's time to experiment, to build and PRINT a shit ton of money
We've announced OpenRouter's first major APAC partnership: KT Telecom, Korea's largest telecom company.
Around 80 media outlets took part. KT will become a provider of sovereign Korean models on OpenRouter. Launch will be anchored by KT Mi:dm K 3.0 Pro, Upstage Solar and Motif, with plans to expand 10-15 models in 2027.
OpenRouter will power KT's Token Factory, which will be available to KT's enterprise clients.
There is an absolute barrage of inference providers popping up that get to $1M/mo extremely quickly.
This is the rough economics of inference providers:
Even if you rent on-demand B300s at $8/GPU-hr, many open source models (GLM 5.3, Kimi K3, DeepSeek 4.1 Flash / Pro) can get you >$10-20/GPU-hr at reasonable throughputs (100tok/s). DeepInfer from DeepSeek directly shows they can get $38/GPU-hr on off-peak hours. Every chip / model has a different throughput / interactivity curve, but there is a lot of arbitrage between even high rental spot prices and token prices. New chips (Vera Rubins) also change this meaningfully. Your ability to be making gross margin relies on your utilization and how good your stack is.
Gateways like OpenRouter, Vercel and more help solve that problem by aggregating demand to you. May inference providers discount their tokens to get longer term commits too. I've even heard stories of selling back Chinese model tokens to Chinese labs!
One thorn in the economics of these players is that many Chinese open source model labs are now asking for rev share if you're at a certain scale, espp for pre-release access to their models for day 1 support. That further pushes the threshold to be in the money.
The barrier to entry seems low and falling. There are even stories of smaller ones who can "vibe code" a way to stand up OSS models on a cluster they rent. And as a result, we're seeing 10s of such companies pop up almost every week and immediately skyrocketing revenue at low-ish gross margin and raising rounds on the back of that.
Unsurprisingly, because the actual service provided seems commodity, this eventually drives up compute prices and drives down token prices. In theory, it would seem like neoclouds who have the lowest cost of compute and themselves have started inference businesses would win the race to the bottom (Crusoe Coreweave Nebius and GMI all have inference businesses), with larger players (Modal Fireworks Baseten) being second because they are well capitalized to make bigger compute commits cheaply and have demand (high utilization), but the reality is the pie is growing so quickly that even upstarts are absolutely crushing it and growing aggressively (inference dot net, DeepInfra, Wafer, Relace, Morph).
Strategies vary across the board: neoclouds want to serve inference, inference players want to be neoclouds and do higher margin reserved instances / post training, and some want to go heterogeneous hardware or creatively flexible compute constructs.
The risks are: do open source models continue to be competitive? Will their creators rent seek more? Will their shape change compute needs? How do different chips change economics? Who gets compute in a supply crunch?
Below is a aggressive sketch of how gross margin scales with utilization for various models / chip sets.
For now, there are many inference providers and they’re all ripping!
When I joined venture 2 years ago, everyone told me I was foolish to pick Menlo Ventures.
Today, research from the best Stanford professor of venture capital ranks Menlo as #3 in 2026, and #1 for <$25B in AUM, with 50 unicorns and only ~20 investors. These rankings rewards being early, leading, valuation, exits and recency. Rankings are just rankings, but it’s extremely rare for VC firms to change reputations so quickly. It’s a small milestone to celebrate.
Menlo has spent the last 5 years rebuilding the team with people who have built startups and long time investors. We built a culture around sticking to first principles while staying true to our roots of low volume high conviction bets. In the last few years, we have been fortunate to partner with Anthropic, OpenRouter, Lovable, Suno, Fireworks, Gimlet, Higgsfield, Wispr, Modal, Legora, OpenEvidence and they are really the stars of the show. And we’ve missed innumerably more.
However, in venture, you’re only as good as your next deal.
