Tim retweeted
Almost every US worker has a pathway to a new job, but only 1 in 7 has a direct one. Skills help open the route; wages, credentials, geography, and other barriers determine whether it is practical.
See how MGI maps the pathways: mck.co/workforceinmotion
Tim retweeted
Marc Andreessen, a16z co-founder: "The definition of a moat is the ability to charge more"
a higher price pays for a bigger sales push and more R&D, so a moat keeps widening
charge more, find out
Tim retweeted
AI agent adoption is still very bimodal right now. You have coding and coding adjacent tasks which have taken off, and then everything else.
And even within coding, you have a very wide continuum of adoption patterns, with some likely a small percentage of developers deploying background agents working on projects in parallel, and the rest of everyone else still working with agents 1:1.
Then, there’s the entire rest of knowledge work where real agentic adoption is still very early. The reason for this is that most of the workflows still need to reengineered to work with agents.
This isn’t the same as deploying a chat system for better research efficiency, but instead requires workflows to be rebuilt, data to be wired up in new ways, new practices for accountability and liability of decisions when agents are in the mix, governance and compliance paradigm changes, security upgrades, and more.
We’re still so unbelievably early in what this is going to look like outside of a few categories of work right now. This is why you can basically expect 100X more agent adoption from what we’ve seen so far.
98% of US households aren't paying for AI yet
More charts in State of Markets II: a16z.news/p/state-of-markets…
Introducing our State of Markets pt 2, along with a companion podcast where we unpack the data and discuss what comes next.
Tech is the everything cycle.
Supply: putting the buildout in context, just passed railroads as % of GDP. The wisdom of Elon is real: the factory (or the datacenter!) is the product.
Demand: diffusion is so, so early. Median AI vendor spending in the top 1% of companies is 8x that of the top 10%. Only about 30% of S&P 500 companies report a quantified AI impact, which means there’s a substantial opportunity in connecting models to a company's data and workflows. Diffusion into companies is one of the main themes of the next 5 years.
We’re entering the agent work period. Only a few million users today, but applicable to billions of internet users with massive surplus created. META/GOOG monetize US users at $200+ per year today. Agent opportunity is much higher.
Mega-trends the next 5 years: Consumer agents, Robotics, Autonomy, AI x bio, Personal health, Diffusion into enterprise, New era of American Dynamism.
Much more in our SoM report here - a16z.news/p/state-of-markets…
@a16z @sarahdingwang @aleximm @santiago__rdz
.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take:
"This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top."
"This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives."
"A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'"
"There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web."
@alexatallah @amasad
.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.
In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."
Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.
Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.
Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.
0:45 How the Stripe deal unfolded
5:05 Why mixing models beats one model
7:25 Forcing the labs to compete on price
8:50 Enterprises want open-weight models
10:30 Every board asks about AI every month
12:25 Why companies must own their intelligence
14:15 Replit as the independence layer
15:10 Everyone is building the same agent
16:35 Why Amjad built bring-your-own-cloud
18:10 Amjad's agent that runs his whole company
19:55 Why 10 specialized agents beat one
23:35 Machines, not humans, should specialize
27:30 Guardrails for agents talking to agents
31:10 Models training their own replacements
33:45 Most tasks don't need a frontier model
40:50 Training small models on Qwen 8B
43:25 The Rust cycle is coming for AI
45:10 Fusion models: frontier quality at half the cost
YouTube: youtu.be/ekK8urKHPMQ
@alexatallah @OpenRouter @amasad @eriktorenberg
Tim retweeted
Introducing Decision 2.0: our newest state-of-the-art decision models, open in every size from 0.6B to 27B ⚡
HF collection 🤗: huggingface.co/collections/v…
#OpenSource #Decision #Jev #LLM
Tim retweeted
everyone is sleeping on this new JSON extraction model!
- open-weights
- matches Gemini Flash 3.5
- while being 3x faster
pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON.
lift, from @datalabto, does all of this in a single step. you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema.
the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers.
here's what you get with lift:
- 90.2% field accuracy on 11,000 fields
- 9B model, nearly matches Gemini Flash 3.5 at 3x the speed
- built not to hallucinate; returns null if missing
- self-hostable with vLLM or Hugging Face
GitHub repo: github.com/datalab-to/lift
(don't forget to star 🌟)
Tim retweeted
introducing Jevbox: an open-source document drive that organizes itself, answers questions, and cites everything (all powered by Jev).
TLDR;
- upload anything and Jev categorizes and files it into the folder tree
- search is hierarchical: Jev picks the folder, then the document, then the section. No embeddings, no vector DB
- every answer cites the page and section it came from
- permissions are enforced at retrieval. If someone loses access to a source, they lose access to answers built on it too
- ships with an MCP server, so your agents get the same permission-aware context
@andrewlu0 has been experimenting with Jev internally and wanted to see how far it could go on a real document library. It's built on Extend Parse + Jev, and fully open-source
The GitHub has instructions to self-host, or you can deploy it to Render in one click
Tim retweeted
You don't need a transformation program or a consultant.
You need one workflow, real cases, and 2 to 4 weeks.
I put the whole method in one guide: how to pick the workflow, prove it, and decide with numbers whether it deserves to scale.
Built for operations leaders at mid-market and enterprise companies.
Comment "OPS" and I'll send it to you.
Follow me first so the DM goes through.
Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:
Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.
They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.
00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents
YouTube: youtu.be/OTQ-lFsq7zA
@askvladi @seema_amble @VirtualElena
a16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of record, the startup owns the whole job.
"Why do you need an AI-native startup if you've got Claudeforce... You've got all your data, and your employees are used to the product. So why another product?"
"I absolutely still think there's a case for the AI-native startup, and it centers around the fact that the legacy incumbent is limited to their system of record, and they're not completing the end-to-end job."
"Say a customer calls and says they got charged after they cancelled. Resolving that isn't just the customer going into the chat and saying 'Hey, I got overcharged.'"
"The response there has to hit billing, it has to look at all the chat history, it has to look at the contract. That's not one system of record, that's the knowledge around that customer and everything it touched."
"The opportunity for the AI-native startup is to say: we're going to own that entire end-to-end arc. That could be legal, owning everything from brief all the way through trial... It's really the concept of owning the end-to-end work."
@seema_amble
Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:
Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.
They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.
00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents
YouTube: youtu.be/OTQ-lFsq7zA
@askvladi @seema_amble @VirtualElena
Tim retweeted
Alert: Cloudflare just dropped a Jev alternative on Hugging Face 🚨 (Apache 2.0)
huggingface.co/Cloudflare/cl…
Tim retweeted
"Today, we’re releasing two Cloudflare-trained decision models, Clef and Clef-flash"
blog.cloudflare.com/clef-dec…
Never seen an idea spreading so fast.
Tim retweeted
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:
Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:
Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:
Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:
Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!
In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
Tim retweeted
A big trend with most enterprises that I talk to is deploying internal FDEs into departments in their organization to help bridge the capabilities of AI into their underlying workflows.
This requires a high level of technical expertise, an understanding of AI, and the ability to understand the workflows and processes that the enterprise is trying to automate. There’s no shortcut to getting automation without this combination of skills (either in one person or multiple).
What’s exciting is this is an entirely new function in most enterprises, which is going to create a ton of new roles in the economy.
“Every generation of technology creates jobs that didn't exist before it, jobs that arise from an immediate need in an emergent industry. The Automation Engineer is a key example of those jobs for this generation. Nobody has ten years of experience doing this yet, because ten years ago the tools that made it possible didn't exist (it didn’t even exist two years ago).”
If you have software skills and are diving into AI, this is an area to go deep on.
Tim retweeted
Internal AI Usage Cap Memo:
In response to requests for increased AI budgets, they aren’t coming. And here’s why:
We currently offer 16x token spend per employee compared to the average of tech forward companies (Based on Ramp's spend data from 70,000 businesses). I say this to establish - for those of you who request increases, it is unlikely you will get it. Unlimited budgets create unlimited waste - a luxury we do not have. So, budget and use your tokens only on projects that have a clear revenue producing or cost savings. Otherwise, it's likely to be filed under "shit that doesn't matter and not worth spending money on."
To help you, we will be offering training(s) on:
How to figure out what's worth working on (biggest miss #1)
How to use fewer tokens to get same output (think of this like AI hygiene)
How to use the time AI saves you to be more effective
AI is a tool that creates leverage. It helps you get out more from what you put in. That said, company revenue is flat (relative terms) compared to AI usage increases. This suggests that people are (largely) using it on projects that don't matter, or are using it on things that do matter, then spending the saved time working on things that don't matter.
This discrepancy (difference between money we spend and money we make from AI) hints at a skill deficiency in the team. One which I hope we will train up - just as well as we trained up on AI usage.
So now that everyone is using it - it's time to use it well.
Let's crush Q4.
Alex
PS - The more clearly you can articulate how what you do generates a return, the more likely tokens will be in your future. And the way to maximize a return is to show HOW LITTLE you can use and HOW MUCH you can get from it.
Tim retweeted
There's a huge opportunity right now in being the deployment layer for AI into the economy. The amount of work it takes to change out workflows in enterprises tends to be far greater than anyone realizes or would prefer. Clearly this is what the applied layer of AI is going to look like in the form of software and agents, but also it opens up new services firms opportunities.
Legacy systems need to be moved to the cloud, data organization and access needs to be updated, software needs to be connected to agents in new ways, workflows need to be reengineered for agents, HITL needs to be figured out for the process, evals need to be generated and maintained, and the entire system needs to be continually updated as new models get released and new capabilities emerge. And the full list may even be longer.
AI is not the same as just deploying software. Software you generally did the implementation of an existing, well understood category of technology, then stepped back and the customer kept running. With AI agents, you're delivering actual work augmentation to the organization, which has a completely different set of complexities associated with it. You're no longer deploying tools that the company is merely enabled by, you're deploying work output in a process. Completely different implementation and enablement process.
As a result, this is going to open up lots of new kinds of firms and plays for existing firms to diffuse AI into organizations. We're going to see approaches by industry, by size of company, and by problem inside of companies. Traditional SIs will modernize and adapt (some will clearly not adapt as well), and new entrants will also be founded in this period that take advantage of this window. Great time to be an FDE or FDE firm.
Box CEO Aaron Levie (@levie) calls out the MASSIVE opportunity for AI deployment services:
"every single one of those companies, whether that's a 50-person firm or a multi-100,000 person firm, is gonna need an army of people to go in and help them with that transformation"
"when you go to that law firm and you go to that pharma company and you go to that bank, they need something that bridges the core technology to their workflow in their business process"
"somebody has to go into that organization and get it set up, and somebody has to go and provide domain expertise to this model so it really understands our particular business process"
Vendor FDEs are incentivized to get you hooked on their platform and to spend more money.
Indie FDEs are incentivized to use the best tool for the job and to save you money.
Hire indie FDEs.