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.
vs.
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.
Systems
One system, from the networklayer through the businesslogic.
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.
1 / 3
Network
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.
Supply chain
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.
Business logic
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.
Partnership
We partner with companies through acquisition.
Our process
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.
1 / 3
Phase 1 (First and Second Quarters)
Connect identity, leave the product alone
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.
Phase 2 (Third and Fourth Quarters)
Open a governedroute into the AI systems they already use
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.
Phase 3 (Second Year Onward)
Ship the roadmap through that route
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.
How we help
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.
Growth
Build
Product Development & Velocity
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.
Sell
Sales, GTM & Customer Success
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.
Efficiency
The stack
Software Supply Chain
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.
The business
Finance, Accounting & Back Office
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.
What we don’t change
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.
One flight on a Boeing 707 in 1959. One conversation with ChatGPT in 2022. The breakthrough is the peek into the future; the supply chain is where eras are won.
By and ·October 2026 · New York, NY
Technology cycles have a natural rhythm. As focus shifts from exploration to commercialization, the supply chain is built out end to end. Most of the tooling doesn’t yet exist, so the inventor must build each step themselves to turn raw inputs into a finished product. As adoption widens, the individual stages of the supply chain grow large enough to sustain standalone vendors serving each niche, which begins the cascading effect of subcontracting.
Sequoia Capital’s Co-Managing Partner Pat Grady recently explained that the age of AI is different than prior invention eras in its pace of change. This mentality leads one to question whether lessons of the past still apply. If there is one thing that is certain: “history may not repeat itself, but it sure does rhyme.”
Ask any analyst at Goldman Sachs which coverage group shipbuilders or airplane manufacturers sit under, and you’ll hear a consistent answer: “industrials.” New inventions start their journey as “technology” and become reclassified as “core infrastructure” once diffused throughout the economy.
Enough time has not yet passed for computer hardware, and their derivative market of software, to cross into the industrial classification. Despite the transistor being invented in 1947, the raw components of personal computing remained experimental and expensive until the early 2000s, at which point broad market adoption started to occur. This shows how nascent the AI era is in the context of time.
The commercial aviation industry during the jet age bears striking resemblance to the characteristics seen in the AI era to-date. Its technology unlocked new routes in the economy, its speed was unparalleled, and a handful of core players controlled the entirety of a rapidly consolidating market.
Boeing is the clear illustration of this point. During the height of innovation in the jet age, Boeing competed domestically with Douglas and Lockheed, and abroad with an assortment of subscale European players. For the first few years, every new generation of airplane made palpable advancements in reliability and experience. Then came the tipping point, where the product was good enough and the market turned to efficiency, which saw the half-dozen oligopoly merge into a worldwide duopoly between Boeing and Airbus.
At the dawn of the jet age, the speed of change must have seemed as fast as the jets themselves were moving.
In December 1958, a Douglas DC-7 could fly from Los Angeles to New York in 8-10 hours. By January 1959, a Boeing 707 made the trip in 4 hours and 3 minutes. Destinations that a month prior took weeks by ship and days by propeller plane now took merely hours by jet. All it took was one flight in a jetliner and you knew the world was in a new era. That was the “ChatGPT moment” of the jet age.
Jetting across the skies on a Boeing 707 must have inspired endless ideas for entrepreneurs: faster travel meant new types of freight could be shipped and longer range meant new routes were possible. Those in the ocean and rail industry likely felt frightened by the prospect that everything would soon travel by jet airplane and leave them in a bygone era.
Two factors delayed that outcome:
Cost.The cost of a Los Angeles to New York coach ticket in 1959 on the Boeing 707 was $124.40 (~$1,440 in 2026 dollars). Today a passenger can buy an economy fare for $170, meaning the cost of jet travel came down by an order of magnitude, making this form of travel accessible to almost the entire American economy nearly 70 years later. The cost of agents is following a similar trajectory.
Reliability.The reliability of early Boeing 707s was poor. The initial jet engines were prone to blowouts and rapid turbine blade degradation across each of the airliner’s 4 engines. Replacement parts and qualified technicians were scarce. The 707 offered a peek into the future, but its prototype-like reliability kept dragging passengers back into the past, rebooked onto propeller planes that could always be counted on to get them there. The reliability of agents is following suit: the AI system breaks and the user is thrown back into the present.
The Boeing 707 kicked off an innovation race at a pace the world had not previously seen. The prototype first flew in 1954 designed with four engines and five seats across. By 1957 the first production 707 had been widened for six seats across. Within two years the 720 rolled off the factory floor with a shorter fuselage. In 1963 jet engine reliability had improved to the point where the airplane needed only three engines, now mounted on the tail. This model was called the 727 and its tri-jet t-tail meant the airplane could sit lower to the ground, allowing it to land at airports without pre-existing ground infrastructure for boarding and baggage handling, and increasing the addressable market of the jetliner. Four years later, jet engine technology improved to the point where only two engines were needed, and this model was known as the 737. Each model was offered at various lengths and had quirks based on the fast-evolving technology platforms which saw significant changes on the factory line without headline marketing changes.
The parallels to the AI era are clear: OpenAI had shared early GPT models in the late 2010s with GPT-2 in 2019, but it wasn’t until the November 2022 “ChatGPT Moment” when the future crystallized. One conversation with the first public ChatGPT model and the future of work became clear to almost anyone who used it. Two conversations in, and the user saw how inconsistent reliability hampered adoption. Three conversations in, and the user hit usage limits, a sign that the cost had not yet normalized to the point where the general public could use this technology regularly. In the following years, OpenAI released models under a haphazard naming convention with names like “4o” and “o4” being offered at the same time without any natural understanding as to the rationale or use cases of either.
Further, each model had a complex “effort” flag of low / medium / high which paralleled the length offerings of Boeing’s airplanes. More length meant more passengers and thus more revenue, but also more fuel burn yielding lower range and higher costs. Higher effort meant stronger output, but also token burn yielding slower outputs and higher costs.
By the mid-1970s the airlines were asking to simplify their sprawling fleets which now consisted of several families and generations of airplanes which became difficult to maintain and staff. Boeing’s solution was elegant: simplify its product offering to one family of two-engine airplanes that varied by length and width, and Boeing would convince the government regulators to allow this one family of planes over long oceans, which until that point required more than two engines.
At first, the pitch to airline customers resonated: The 757 had one aisle, the 767 had two. The -100 was designed to be the shortest (~150 passengers on the single aisle) and the -300 was the longest (~300 passengers on the twin aisle). The plane only needed a crew of two at the controls vs. three in the older models.
Boeing paid the immense cost of research and development and the product was spectacular. One supply chain for parts. One type rating for crews. One experience for passengers.
The market didn’t bite quite as Boeing had anticipated.
The smallest variant in the family, the 757-100, was cancelled before production began given a lack of firm orders. Airlines demanded the 737-family be re-engined and stretched to fill the void and the backlog built for an unbuilt plane.
The largest variant in the family, the 767-300, was deemed too small. Boeing stretched it to its limits with the 767-400ER and incorporated technology, systems, and interior aesthetics from the 777 in an attempt to save the program and appease the market; however, only 38 were ever built. Airlines kept building a backlog for the wider triple seven.
Boeing’s new “clean sheet” airliner family of the 757 (1,050 deliveries) and 767 (1,370 deliveries) never had a chance to amortize its supply chains like the Boeing 737 did (12,000+ deliveries), which was an evolved Boeing 707 (1,010 deliveries) and Boeing 727 (1,831 deliveries), and thus inherited the economics of a half-century supply chain.
Boeing tried another clean sheet design with the Sonic Cruiser, a near-supersonic airliner promising trips 15-20% faster. Airlines chose efficiency over speed, and by December 2002 Boeing had shelved it for a slower, carbon-fiber airplane that became the 787. The jet age had hit its second tipping point, where the cost, quality, and capability were good enough for worldwide use and speed was no longer the bottleneck.
In the early 2000s, Boeing’s executives realized this and made a strategic pivot away from mechanical engineering and towards financial engineering. Gone were the slide rules and in were the spreadsheets. Boeing shut down the 757 line entirely in 2004, and by 2005 had started selling off pieces of its supply chain. By 2007, profits were up nearly 2.2x from 2004.
The cracks in Boeing’s business started showing a year later:
787 Dreamliner.Designed to fill the void between the small 737 and the supersized 777 and usher in a completely new era of composite material sciences, this plane was the first to be built across a fully distributed supply chain. Airlines expected deliveries in 2008. First deliveries slipped to 2011, with the quality of the first two dozen aircraft so bad that each was 6 tons overweight from specifications. Fully aligned manufacturing didn’t start for the entire aircraft family until 2018.
737 MAX.Taking the lessons from the Dreamliner, Boeing built the 737 MAX on their same outsourced supply chain. The outcome was a disaster: two fatal crashes halted flights on the type for twenty months, and after the airplane was cleared again a door plug fell out mid-flight causing further groundings. Although the buck stops with Boeing, the outsourced chain made every fix harder: Spirit-built fuselages arrived with defects such as mis-drilled holes, and when Boeing's own line opened a door plug to rework Spirit's rivets, it went back on without its bolts and blew out mid-flight in January 2024. By the time the last stored MAX was delivered in 2025, some airplanes were 6 years old, eating nearly a third of the airframes’ service life sitting on the tarmac assuming a service career of roughly two decades.
777X.The idea in concept was to do to the triple-seven what Boeing did to the 737. This became the latest of Boeing’s series of delays and financial disasters. Airlines expected deliveries in 2020 and have received no airplanes as of this writing. Dozens of airframes remain stored on the ground awaiting reworking as major defects are fixed, and Emirates and Lufthansa are refusing delivery of the first line numbers given they are non-standard and will be years old before they carry a single passenger. The customers have learned their lesson.
Contrast that with the first Boeing 757 and Boeing 767 that entered commercial service, both of which went on to fly for more than 20 years.
All three cases stem from the same core issue: as the technology matured, the market entered a renaissance period in which each vendor could make enough profit owning a narrow portion of the supply chain and subcontracting to the maximum extent possible. The tide turned when the needs of the end customer changed and the supply chain snapped because outsourcing created a rigid structure that lacked the flexibility to change under market conditions.
Boeing ultimately was forced by market pressure and regulators to re-acquire major parts of their supply chain, including Spirit AeroSystems, which manufactured up to 70% of the Boeing 737 MAX, at disadvantageous prices to regain their ability to ship airplanes.
Airlines were right to keep stretching designs they had already validated. What broke was not the repackaging. It was that Boeing sold the supply chain the repackaging depended on, then asked that fragmented chain to absorb a new material in every part at once.
Enterprise software is living the same sequence. The cloud split every product across identity, files, mail, and a dozen vendors. AI is the composite: it touches each of those parts at once, and no single vendor owns the integration.
The lesson is not to rebuild the airplane. Keep the validated core, own the supply chain around it, and assemble it while the pieces trade at advantageous multiples, not after a crisis forces the purchase at a premium.
