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Our own network, compute and data fabrics lead to fixed-cost compute. Owning the root of the stack keeps the economics predictable as agentic usage grows.
General Agentic is bringing enterprise software to the next billion users, enabled by our vertically integrated software supply chain.
Not humans clicking buttons on screens.
Most acquirers see AI as a cost story. They run what they buy with less, often rebuilding working systems from the ground up, a gamble their customers cannot afford.
We see a growth story. We unlock software already in production for its next generation of users, repackaged for agents, without disrupting the pathways today’s users rely on. We believe this opens a far larger market at a minimal added cost.
A reference implementation of the complete software supply chain, built and operated by General Agentic. Multi-tenant, cloud-agnostic, and model-independent from the ground up.
Our own network, compute and data fabrics lead to fixed-cost compute. Owning the root of the stack keeps the economics predictable as agentic usage grows.
The machinery that moves software to its new users: continuous integration with real-time delivery, identity, connection APIs, permissioning and governance, and an SDK for seamless handoff. One integration at the core, not one per platform.
The validated logic of the software we acquire, served to agentic systems at full fidelity. We do not rebuild it, we deliver it.
Our founders run the firm on this system, including the application that built this very page. We were its first users and the companies we partner with are its next.
We invest in established software companies and often take a controlling interest. The trusted name stays on the door, customers keep the product they rely on, and when their agents arrive, it is still the same core program powering their work.
Customers bring their agents, companies bring the trusted logic, and General Agentic brings the technology that connects them together.
In this era, software splits into a rewrite or a repackage. General Agentic buys where the natural path is a repackage, where the buyer runs an enterprise procurement process. We keep the system of record, we do not rebuild the core, and we connect the existing validated product to every agentic workspace and system their customers use.
Nothing changes for the company's current customers. We connect the company's identity system and APIs to a governed distribution layer, and together we configure permissioning so an agent can work at the same fidelity a human user can. The company controls what is exposed, and to whom. People keep the sign-in they already have.
The company's product becomes reachable from ChatGPT, Claude, and the other surfaces where work is starting. To users, it's the same product, under the same entitlements, just delivered through a new channel. Usage starts to climb, and we can test pricing models that are a win-win with the customers rather than only seats.
Development continues as the company planned. Every new feature can reach those workspaces without a second architecture. The company's team inherits the tooling to do that at product velocity. Its customers bring their own agents and pay their own inference. The roadmap starts growing where the new users are.
The results show up where it counts: new users on the same product, more revenue per customer, and a channel you can price, because the work now starts where the customers already work.
Growth is our focus, efficiency is our engine. We study the frontier of Silicon Valley and Wall Street, prove it out on ourselves, and install what works in our companies. We relentlessly source and test tactics and technologies that help our companies grow faster, more efficiently.
We help companies build for agents, faster. This requires new surfaces, new permissions, and new features designed for agentic users. Companies’ teams start from our working system, not a blank page.
New users deserve new pricing. Agents don’t buy seats, so we help companies price what the product actually delivers: usage, tiers, and outcomes without breaking contracts. Buyers increasingly choose software their agents can reliably use, and every agent they deploy expands the account while tying companies’ products deeper into their work.
Success in the agentic channel multiplies usage, and metered infrastructure bills companies for it. Over time the supply chain centralizes onto the one system we operate, the way a well-run fleet is renewed: opportunistically, piece by piece, as each part reaches the end of its useful life.
Every company carries work that has nothing to do with its product. Finance, accounting, payroll, and reporting benefits as our portfolio scales. Companies’ teams’ time goes back to making the best products for their customers.
Our portfolio companies spent years building their brands and earning their customers’ trust. That is what General Agentic sees is the core asset. We keep the core product, the name on the door, and the management that built it. Although we often take majority control, we do not hire a new general manager and our structure allows additional partners to participate as the platform scales. Our technology and our capital exist to compound what they built through the next platform shift.
General Agentic was founded on a straightforward principle:The best acquirers of technology companies are builders of technology themselves. The firm pairs Silicon Valley technical discipline with Wall Street investment judgment, and operates from New York.
The firm’s founders bring experience from world-leading institutions.
AI is missing its “miles-per-gallon” equivalent. Token counts and ambiguous model SKUs are not the answer. Until there is an obvious proxy for outcomes, value-based pricing for AI products will be unclear.
By and ·September 2026 · New York, NY
You either own, you loan, or you pay-as-you-go. That’s a perpetual license, a subscription, or a consumption-based pricing model.
In any market, supply is priced at a premium to cost, and demand is priced at a discount to value. Cost is straightforward, but value remains unknown in the AI era.
Let’s think about the combustion engines that powered the planes, trains, and automobiles. All of them from a value perspective could be boiled down to one metric: miles-per-gallon (MPG).
Now think about today in AI. Try to value the intelligence engines that power workflows. Try to think of an equivalent to miles-per-gallon and you’ll run out of human and digital tokens before you reach an answer.
Let’s break down what miles-per-gallon is: the numerator is a unit of distance, and the denominator is a unit of measure for fuel that got you that unit of distance. Given the distance you need to travel and the MPG rating, you know how much gas to put in the tank given your risk level and budget constraints to reach your destination.
Applying this same logic to the cloud era yields another straightforward result: hard drives are measured in bits-per-drive. The numerator is a unit of how many photos can be stored; the denominator is a discrete physical box.
For AI, the market view is “tokens” of all types: input, output, cached. Tokens are not a metric that business users can rely on because they have no tangible meaning and they are also non-fungible. People inherently know that a photo stored at 200kbs is a worse resolution than the same photo stored at 2mbs. People also inherently know that Chevron 91 has better miles-per-gallon than Valero 87. Customers have no way of knowing whether “2 billion input tokens used by 20 headless agents” is better than “200 million output tokens used by 5 sessions of an AI app.”
This brings us to what we call “the missing metric.” The market today has not decided what the “miles-per-gallon” should be for the AI era. That is why pricing remains so opaque and difficult to understand.
Zach and I demystified the blockers to the market finding this metric by going back to a process we learned at Goldman Sachs. When a client would come in wanting to fund a merger but unsure of the direction they should go in, we would show them what we called “the menu page,” which consisted of equity on one side and debt on the other, with every shade of security in between.
We replicated this for the spectrum of pricing options that a business could have here:
| Perpetual license + service | Subscription by seats | Subscription by seats + usage | Consumption / usage | Workflow | Outcome | Take rate | |
|---|---|---|---|---|---|---|---|
Revenue behavior | One-time sale, then maintenance No expansion with use or agents | Recurring per distinct user Grows with user headcount, not with work or spend | Base per distinct user plus overage when usage exceeds limits Grows with headcount, and with work | Meters usage, no ceiling other than getting cut off Grows with thinking, not with dollars made | Paid when a workflow is run Regardless of whether the workflow generates end value or just costs money | Paid when a defined result happens Reduced or no cost if defined result does not happen | Tax on a metric associated with a deliverable or a sale The more ingrained the workflow, the higher the cost |
Impact of AI Costs | More support tickets given new tech End customer pays upgrade costs which puts added friction on agentic rollouts | Cost of rollout is minimal given SaaS Inference and talent scale with use, making the seller absorb the cost | Seller absorbs only up to a pre-agreed cost Overage is passed through to buyer | Inference billed through to buyer on the same meter as COGS Costs felt in real-time, not absorbed | More capable AI means more complex workflows run in AI Lower setup and maintenance costs | Borne mostly on the seller as the buyer only pays if the result is delivered Incentivizes better product quality | Passed through directly to the buyer as a % of a metric The more spend, the more revenue the seller makes |
Customer pros / cons | Pro: customer cannot lose an integration or a workflow overnight Con: updates slow and systems lag the market in capability | Pro: labor-like costs (AI inference) are being absorbed by the seller Con: seat prices a human not work, so agent usage unbilled | Pro: buyer able to test until value is seen at no new cost Con: buyer absorbs costs if usage skyrockets | Pro: transparent pricing, cancel anytime to floor cost Con: unpredictable commodity pricing | Pro: price the job already bought today Con: likely not a perfect fit to needs if non-custom pricing | Pro: only pay for successful outcome Con: each payment bundles in the cost of each loss which can make price very high | Pro: pay scales with increased GMV Con: tax is direct drag on bottom line profits as one grows |
Vendor pros / cons | Pro: customer pays more for bespoke service and support Con: hard to get to economies of scale with many versions | Pro: customer sticky and hard to rip out Con: vendor absorbs all variable costs, and customer games the seat count | Pro: customer sticky given value accrued by included usage Con: still eating some of the usage although capped | Pro: profit margins are fixed to vendor Con: customers are less sticky and have incentive to churn | Pro: setup costs amortized across multiple customers Con: constant re-working | Pro: quality is proven with each deliverable each time Con: cost of each failure is variable on the vendor | Pro: fixed take, no COGS surprise Con: harmed by underlying downturn in client’s business |
Requirements | Systems that can be run in isolation Teams that can manage and upgrade multiple versions | Software that is user-based; in the cloud Software that can be multi-user and multi-tenant in cloud | Separation of the distinct users from their distinct usage Seamless detachment of fixed & variable | Discretization of offering into chunks with specific value Real-time pricing and costs of supply chain | Separate proprietary / unique elements Workflow creation and run-rate costs well-defined | Judgement of the final product’s quality Understanding seller’s position in value chain | Clearly defined metrics that can be taxed Seller who can cap costs into fixed rates |
Examples | Oracle: on-prem database engine; capex + support SAP: on-prem ERP + support | Salesforce: named CRM users Slack: active users Workday: priced on people, not payroll | DocuSign: seats + envelope quota HubSpot: seats + marketing-contact allowance | Anthropic API: meter tokens; pass through inference AWS: meter data center resource usage + marketplace | Zapier: per successful task Checkr: per background screen Avalara: per tax calculation | Zendesk AI: $ per successful resolution, not per attempt Salesforce Help AI: $0 paid if customer asks for human | Stripe: take rate % + fixed $ per charge Shopify: card take rate + fine for off-platform transactions |
The internal situation that led to this was the July 2026 release of three new model classes by SpaceX (Small, Grok 4.5), OpenAI (Medium, ChatGPT 5.6 Sol), and Anthropic (Large, Fable 5). The pricing was what is known as a Fermi difference, meaning that Fable was 10x more expensive than Sol, which in turn was 10x more expensive than Grok. The industry says “measure cost in tokens.” That’s analogous to measuring gas in octanes. Chevron 91 is not the same as Shell 91. What does octane 91 even mean? Octane 89? How about 87?
To get to that point we need to align on where we are in this technological cycle:
| Phase 1 Thinking | NowPhase 2 Action | Phase 3 Headless | Phase 4 Autonomy | Phase 5 Value | |
|---|---|---|---|---|---|
Breakthrough | Chatting | Tool Calling | Headless Agents | Agentic Commerce | Opportunity Cost |
Meaning | A computer program can “think”, and its responses are not constrained by pre-programmed logic. | “Thinking” becomes action, with the models reading from, writing to, and integrating with the systems of record. | Agent runs in the background with pre-configured tools not tied to an AI Workspace (e.g., Cursor or Claude). | Agent makes buying decisions over the tools it can access to execute with a budget given by a principal user. | Agent can set a price for its services and charge counterparts for the value accrued from performing work. |
Interaction Mode | Human users install app and message with AI. | Human users select and configure the tools. | Human assigns work, agent comes back. | Human sets budget for task, agent executes. | Services assigned at market clearing price. |
Consumer Friction | Need computer access. | Find tasks requiring tools. | Finding many tasks. | Setting up a wallet. | Value proposition itself. |
Enterprise Friction | Identifying why chatting with an app is valuable Getting permission to enter data into system. | Identifying workloads which can show value Procuring access to an enterprise system itself. | Accountability of who is responsible for issues Budget cycles of how much can be spent. | Rewiring of the financial rails for agentic spend Organizational structure of agents as co-workers. | Switching costs are daunting and distracting New system is not 1:1 to legacy trusted solution. |
B2C | 2022–2025 | 2025–2026 | 2026–present | Late 2026 | — |
B2B | 2024–2026 | 2026–present | — | — | — |
By the end of Phase 3, agents will be headless and we will start to see a metric analogous to miles-per-gallon for agents, and by Phase 4 we will be able to estimate this right after filling up the tank. By Phase 5, we will have enough information to estimate this value given the price ahead of even getting to the pump, and that is where the agentic economy starts to have an opportunity cost.
Jake Chasan & Zach Evans
Co-Founders of General Agentic