morphic

the sovereign ai stack
own your context and the data you generate

// starting with the proactive revenue agent unifying any gtm stack you bring

[morphic @morphic_io]

// to build a Stargate for Data

the internet was a one-time subsidy, and proprietary data locked away in private will be the next gold mines for data

🧵 a thread on how this plays out, and why the "how" matters when we embark on this civilization-wide quest for data

[will depue @willdepue]

A Stargate for Data

Labs are on a trajectory towards >$100B/year of data spend by 2030. As we begin the trillion-dollar compute project, we need to think about the equivalent civilizational-scale effort for the other core ingredient: data.

At the foundation of the scaling revolution is a simple empirical law: deep neural networks improve smoothly, near magically, as you scale two things in proportion — (1) the size of the model and (2) the amount of data you train on. And despite the scaling laws being brutally diminishing, we’ve successfully bitten the bullet of logarithmic scaling with exponentially larger clusters and datasets, and received incredible new capabilities in return.

But this exponential scaling is bound to hit some limits. Oddly enough, compute has compounded fairly smoothly without limit, with trillions flowing into hypercluster buildout. Instead, we’re starting to hit the limits of an exponential demand for data. Gone are the days of being purely in the compute-limited regime, where we had effectively infinite internet data but never enough GPUs, we’re now entering a data-limited regime.

Luckily, this limitation is coinciding with staggering improvements in AI capabilities. Incredibly, we seem to have a real line of sight towards automating a majority of knowledge work with the methods we have today. RL + pretraining, and the data for each, will be generally sufficient to achieve most economically valuable tasks, given some minimal algorithmic progress and continued compute scaling.

In a data-limited world, economic progress & scientific acceleration will be directly bottlenecked by our coverage in each domain. We need to see data collection as imperative, deserving the same civilizational ambition we’ve given compute.

The internet as a one-time subsidy

It’s underrated how much all progress in AI owes everything to the blessing of the internet, this one-time civilizational subsidy to deep learning, decades of unintentional accumulation of a perfect dataset: every book, blog post, image, video, paper, discussion, etc. all digitized and freely available. Without the internet, we’d likely see comparably minimal progress in AI today, and in fact, if you notice where systems currently underperform, it’s almost always a domain where web coverage is limited and data is private, expensive, non-digitized, or non-existent.

But we’re running out of it. There are only about 300 trillion tokens of useful public human text, and the internet doesn’t produce nearly enough new high-quality data to match what scaling demands — we’re soon to hit the limits of public data for pretraining. And though the advent of RL bought us reprieve — chain-of-thought RL needed a new form of untapped data, gradable math & coding tasks, also available online — we’re quickly running dry of hard tasks for RL as well.

Why do we need so much data anyways? Humans learn comparably in far less time, needing just one textbook where language models might need the equivalent of hundreds to learn a new topic. It’s possible we discover methods that are massively more data efficient — synthetic data, data efficient architectures, other exotic algorithms — but fundamental progress is slow and highly unpredictable, and the recipe we have just works today.

And, while I’m wary of getting too deep here, even arbitrary data efficiency can’t replace data that just doesn’t exist in the first place. There’s a massive amount of missing information on the web: the dark matter of the internet — tacit knowledge, undocumented processes, etc. — most of which was never published and lives only inside organizations, the physical world, or just in people’s heads. I’ll leave it here and say, for reasons far longer than I can fit in this post [1], it’s best to operate on the assumption that our insatiable desire for data will continue as it has for the last decade.

There will be >$100B/year in data spend by 2030

We’re not screwed yet, of course. Only a fraction of useful data in the world is on the public internet, the rest is stored inside private datasets, corporations, personal archives, universities, governments, and otherwise. Labs can and will continue to license these private datasets, or create them from scratch, like Anthropic’s book scanning project. And we’ll increasingly task human experts to manufacture new high-quality data, with a large fraction of hard RL training tasks already being sourced this way.

But collecting this data, unlike before, will be expensive. As the free internet dries up and demand for data rises, we should see labs investing equally in data as compute, likely spending a significant fraction of their compute budgets on data. As we see trillions spent on compute, we should also expect hundreds of billions spent on data (human data & collection budgets), given their equivalent importance. And, notably, data spend is already tracking this way: total data spend across vendors, not counting internal lab efforts, is already roughly $7 billion per year. It’s quite reasonable we’ll see >10x by 2030.

Data is the moat

Data becoming increasingly private will also majorly shift the competitive landscape. While compute is a commodity — everyone buys the same chips and builds the same clusters — data really isn’t. The big reason why frontier models have felt eerily similar to one another, until now, is they were trained on substantially the same internet (pretraining data variability across labs seems pretty low). As labs diverge onto more exclusive, manually collected corpora, I think models will begin to increasingly diverge.

OpenAI pulling ahead in mathematics and Anthropic in cybersecurity isn’t an accident. I really think laser-focused collection of high-quality midtraining tokens, custom RL tasks, environments, with dedicated research effort, has driven much of the visible progress in the last year. James Betker has an excellent blog about “the ‘it’ in a model is the dataset”: model architecture and compute buy you efficiency and order-of-magnitude performance, but ultimately, models, of any architecture, are such incredible approximators of their dataset that the core meat of a model boils down to just that, nothing else. Data is a major moat.

AGI long, ASI short

As I’ve tweeted before, I’m confident that, despite the narrative, the data labeling industry will continue to fuel great businesses and be an excellent AGI long, ASI short. The argument is just: By the time the AGI labs no longer need data, it’s probably over for everything else too [2]. In this frame, the last companies left should be the data companies, as the last speck of economically relevant data is sucked in. And these companies are already among some of the fastest-growing companies in history: Mercor, founded three years ago, is rumored to be doing $2 billion in revenue with something like a few million expert labelers under contract.

While these businesses are very non-stationary, what type of data is needed shifts constantly, I don’t think that diminishes their value. The long-tail of the economy is long, and the value isn’t diminishing as you extend farther into more obscure information: as models get more capable, the value of the marginal dataset goes up, not down. Automating a full job means covering its full distribution of tasks, tools, edge-cases, and long-horizon loops. There’s some O-ring logic to it: a dataset that buys a 1% bump can justify a previously unjustifiable collection cost when it’s the difference between a system that does 99% of a job and one that does all of it [3].

The competitive dynamics of the data industry are still evolving but as demand for data is increasingly niche, ultra high-quality, expert-generated, I think we’ll see real consolidation. Again, contra-narrative, we’ll probably see true competitive differentiation built on brand, quality control of data (which, from personal experience, can vary massively), as well as in network effects from the talent networks themselves over time. We’ve already seen rapidly shifting data type demand work in favor of incumbents, benefiting those with early knowledge of where the market is headed.

The binding constraint

It’s truly remarkable that we seem to have the recipe — pretraining + RL — to absorb most economically valuable work, despite being far from a lot of what we expected from “AGI”. The same way chess engines revealed we never needed general intelligence to solve chess, as we originally thought, we’ll soon realize that software, mathematics, and the vast majority of the economy (including physical, just running ~3 years behind!) are the same. If recursive self-improvement or some other algorithmic breakthrough arrives, that’s wonderful, but we really don’t have to wait for it. The binding constraint between here and an automated economy isn’t that, it’s data coverage: every app, workflow, edge case, process, etc. sitting in private stores or someone’s head.

Ultimately, while we make tremendous strides in more efficient model architectures, and clusters like Stargate equip us with zettaflop-scale compute, we really aren’t making rapid progress collecting the data we lack.

We’ll soon live in a world where we have the methods & compute to accelerate scientific progress or economic growth, but not the data. And we’re already there today: frontier models would surely be as good at accounting/many medical tasks/legal advice as they are at software engineering if we only had the same pretraining & RL coverage as we did for code.

I really want to drill this in: The speed at which we automate the economy is going to be directly rate-limited by our ability to collect data about it.

Worth noting that under this assumption, with data as defensible and directly proportional to economic & scientific progress, data should also be considered a national strategic asset like compute. Imagine what we’d do in a world where we had a Manhattan Project-effort for AI and needed to mobilize data collection as a limiting factor. We should be concerned about China, with greater state capacity and authoritarian economic control, being capable of mobilizing data collection at national scale, potentially compounding their economy and scientific output faster than us down the line.

A Stargate for data

I’m leaving my complete ideas for a future post, as this one is already far too long, so I’d really like to pose the question here. Stargate exists because we organized trillions of dollars, international strategy, gigawatts around compute as a fundamental ingredient. What would equivalent ambition look like for data?

Obviously, scaling data collection, a heterogeneous mass of information across the economy, isn’t going to be as clear as scaling compute, as a homogenous infrastructural effort. A core division will be first, coverage — all uncaptured knowledge sitting across the economy/science/physical world and all that simply isn’t recorded — and, secondly, sheer volume in the domains we already train on: more hard math tasks, more high-quality web text, way more coding data, more legal drafts, etc.

I have a post coming soon which breaks down my proposals. There’s a lot of room for creativity. Quickly, we’ll probably want to start with a deep census of what we have and what we’re missing, predict what the 2030 model will still be bad at and work backward to what we should be collecting today. You can probably license a large amount, leveraging high lab valuations to buy datasets or companies altogether. There’s an adversarial nature to a lot of this collection with firms, so there’s lots of engineering to do this correctly. We should go convince important companies to turn off deletion policies, even if we’re not buying from them yet. Data flywheels in consumer products will be massive. Confidential training, government legislation for grant-funded research, running companies at a loss for their data, etc.

We’re headed towards hundreds of billions in expenditure, national prioritization, and major data limitation on the horizon. We have a great opportunity to think creatively about what a megaproject for data would look like: How do we, deliberately this time, construct the next internet’s worth of data?

Footnotes:

[1]: I’ll probably soon publish my much longer post explaining my position on data efficiency and why the value of this data is still pretty high in most worlds regardless of new algorithms.

[2]: The “AGI freeroll” bet: heads you win, tails ASI flips the world upside down anyways.

[3]: We already see a glint of validation of this point, given the data market is strongly tilting towards ultra-high-quality agentic data, rather than unskilled labeling — niche expert workflows, live environments, and evaluations requiring increasingly obscure talent & knowledge — yet shows increasing, not decreasing, revenues.

[morphic @morphic_io]

// today we introduce the Morphic Core

> 57% of businesses are piloting or running AI agents, but only 8% say their data is ready for it

Morphic Core embeds a model-agnostic enterprise platform that forms the foundation for your agent/AI deployments

more on the release 👇

[morphic @morphic_io]

// the Morphic Workspace

SFDC's 2026 research found 51% of sales leaders say disconnected systems slow AI initiatives

Morphic is a home base where anyone (technical or not) can command AI with unified context across
> tasks
> tables/records
> knowledge base
> meetings

more👇

[Josh Sum @joshsum_]

@socrateslabs_io is the AI transformation company for @morphic_io

-> we embed the best forward deployed engineers into global enterprises

our customers own every deployment, all context and workflow data

and subsequent implementations always build on what was previously built

[morphic @morphic_io]

Introducing Socrates Labs, Morphic’s AI transformation company.

Businesses need work done and outcomes, not just more tools.

Socrates focuses on the last mile of AI adoption: building and deploying systems with your team that change how work gets done.

We’re already making it happen.

[socrates labs @socrateslabs_io]

AI transformation is a tricky process.
People aren't looking for more tools and platforms.

What they need is work done, and a partner to tailor AI systems that reshape how their business works, not just automating old processes faster.

Here's how we do it 👇

[morphic @morphic_io]

// Introducing the Morphic Revenue Agent

Your bottleneck for scaling revenue is not lead generation

It's the deals that die in the pipeline
> 40-60% of deals end in no decision
> 44% of contacts never make it into the CRM. 26% of those are decision makers

We're fixing that 👇

[morphic @morphic_io]

Introducing Morphic: the sovereign AI platform

Tribal knowledge is an unsolved problem with an unseen cost - and companies are trying to fill a leaky bucket adopting AI without the systems to capture it

We're launching the Morphic platform today to make it easy for anyone, technical or non-technical, to start compounding tribal knowledge

// The first agent we're launching is the Morphic Revenue Agent

Most deals die in the pipeline due to negligence. Bring your traditional sales/GTM stack and our proactive revenue agent connects to any software in it.

The agent helps you close deals faster and more systematically.

Because most companies don't need more tools - they need a process and a way to execute it with human judgement enhanced with intelligence that agents are great at gathering

// The Morphic Workspace

Building a company operating system used to take a mix of multiple software platforms with siloed data.

The Morphic workspace unifies context and allows anyone to start automating their work with embedded agents without the need for an AI implementation expert or engineer
> no-code knowledge base (.md files)
> project management
> meeting note-taker
> records and custom tables
> integrations marketplace

// Sovereign from day one
Every decision trace and data generated from human judgement,

If your company is looking for a partner to scale revenue and optimize operations while retaining your most important asset in the age of AI
-> get started with a no-obligation call on our website. we'll run a workshop to identify the first workflow we can automate today so you see value immediately.

The future is sovereign.
Morphic.

[morphic @morphic_io]

Check these updates live at https://t.co/6bf0aaFjfW or book a demo today

[morphic @morphic_io]

// @morphic_io platform update week 3

new products/features:
> launched Morphic Core
> relationship strength scores and promise tracking
> Workspace “needs you” and “inbox”

general improvements:
> meeting bot reliability
> smarter records

[morphic @morphic_io]

5/ Workspace improvements

> delete one or many records directly from the pipeline view
> inline edits for fields in records and collections
> consistent search across the Morphic workspace, revenue agent and Core
> cleaner Inbox layout

[morphic @morphic_io]

@michael_chomsky yes

[morphic @morphic_io]

@satyanadella both systems of record and workflow/process data on how your organization works will be core to each org

building for that future with
> "head" with vertical agents
> "headless" with our ontology layer

so companies can own their AI and self-improvement in a model-agnostic way

[morphic @morphic_io]

@hydra_db "powered by the fastest and most scalable enterprise graph database on the market @hydra_db"

[morphic @morphic_io]

>> start with a FREE workflow audit today

we'll study how work moves through your company and identify the top 3 workflows with the biggest ROI potential

Morphic is already live in 7+ countries across insurance, finance, manufacturing etc

booking link: https://t.co/7lqFaGEYGY

[morphic @morphic_io]

// today we introduce the Morphic Core

> 57% of businesses are piloting or running AI agents, but only 8% say their data is ready for it

Morphic Core embeds a model-agnostic enterprise platform that forms the foundation for your agent/AI deployments

more on the release 👇

[morphic @morphic_io]

// Extension: Autonomous process mining

process mining is a data-driven method to understand what happens when work takes different paths

> who gets pulled in, and which department
> how are exceptions resolved
> dependencies and how different teams' priorities slow down the bigger picture

> where and when is human judgment is needed
> could the right person be involved earlier?
> can we run different work in parallel?