Lotan Levkowitz

Healthy infrastructure takes a thin slice of the value above it. In AI the slice is bigger than the pie: the model layer collects more than the applications above it earn. The bubble ends the day the software on top can pay for the model underneath and keep a margin.

Past industrial revolutions made products accessible at the price of standardization. AI delivers personalized service at industrial scale. Three company types are forming; the third builds demand that never existed, and if history repeats, that is where most value gets created.

Investors diligence you deeply. You pick a decade-long partner on a few meetings and gut feel. And investors are the hardest thing to replace. We built a tool that flips it: founders running DD on funds, ours included. Information symmetry is healthy for both sides.

Now that almost everyone arrives with traction, it no longer marks who is on a venture-scale path. Customers are living in the past. Founders who steer by their feedback are driving by the rearview mirror.

Before a data moat can compound, you have to build the dataset. Five patterns that actually worked: earn it intimately (Navina), go where no one else will (Alice), digitize the physical world (Limitless), structure the public signal (OnFire), create data that never existed (Protai).

The hard part of AI moved from capability to embedding it in real organizations. That work is local, cannot be imported, and gets paid for by productivity, not growth capital. The people leaving tech are exactly the hands for it.

Proprietary data is a head start, not a moat. A head start depreciates the day a better model ships. A moat is proprietary data that compounds. One question to ask an AI company: does it compound.

The markets most at risk are the ones SaaS already conquered, because the bridge to software is built. The opportunity is the huge economy that never went through the wave. Frontier labs are buying the bridge; FDE startups build it themselves. Not competition. Division of labor.

Data center bandwidth was about to hit a wall and silicon photonics was the unlock, before co-packaged optics was a buzzword. The pattern: identify the constraint before it becomes consensus, back the team that owns the solution.

When the cost of execution approaches zero, effort-based pricing collapses. Differentiation moves to who creates value and who is willing to be measured on it. It will not stop at lawyers.

AI will not eliminate most roles. It will quietly redefine what good looks like inside them. You will not be measured by hours. You will be measured by the quality of your decisions.

The market reacts to the acquisition and misses the scientific breakthrough in the same week. Some layers that feel strategic today will commoditize faster than we think. The challenge is not speed. It is the allocation of attention.

AI is not another domain. It is a new operating layer, and you cannot truly adopt one while deep in the 24/7. A founder who takes real time between companies for AI bootcamp may return with a different kind of edge.

It takes time to learn how to save time. The people meant to shape the future are captive to maintaining the present. If something is critical to the future, it needs a recurring slot in the calendar.

Three sources of SaaS advantage, and AI stresses each differently. Panic is justified where advantage rested on momentum. It is overdone for systems of record, network effects, and compounding data. Ask where the advantage comes from, not whether the product is replaceable.

In every ideation process there is a small moment we all wait for, when the picture snaps into focus and the direction forward becomes clear. Accompanying founders to that moment is the heart of the work.

Independent, skeptical, allergic to fluff. The future of GTM is a data layer that replaces opinions with real signals. That thesis is how OnFire was born.

To turn a service into a product, companies need Palantir's path: product innovation plus organizational and GTM innovation. Every startup here must build a brand an enterprise can trust.

In the seventies customers wanted better pocket calculators while founders were building Oracle, Microsoft and Apple. Customer feedback matters, and it does not necessarily point forward.

The line runs between companies that are relevant to the AI generation and those that are not. Every founder needs sharp answers to three questions, with proof.

Companies spend trillions on outsourced services that never went through deep digital transformation. Customers do not want an interface. They want the problem solved.

The industry is being forced from electrical to optical interconnect. Not an improvement, a step change. Teramount is one of the only companies in the world enabling it.

The pendulum settled on unique data. A data strategy can and should be articulated before the first line of code. Navina's founders refused to write code for a year until theirs was precise.

Keep the familiar interface, put the sophistication under the hood, give users control over how much help they get. And build for a new kind of user: your next customer may not be human.

AI is the enabler, not the product. Integration beats technical perfection. Adoption is driven by trust. Proprietary data outlasts the model. The best products create a data flywheel.

Windsurf and IO in one week. The race is no longer for the smartest model but for the layer through which models get used. Is AI opening technology up, or are we watching fast reconsolidation?

The model has too many degrees of freedom. Product management flips from defining what to build to deciding what the product must never say, offer, or attempt, and where it stops and says: I do not know.

Every breakthrough started because someone did not want to make the effort. Instead of fighting laziness, listen to it. It points precisely at what needs automation or a paradigm shift.

AI can now learn from observing organizational routine, not just from text. Tacit knowledge becomes working infrastructure. The prize is the next giant platform, for every department still run like a tribe instead of a system.

From the Knesset AI subcommittee: juniors are the fastest adopters. Once companies build the right AI workflows, GenAI will not replace juniors. It will turn them into seniors faster.

A framework categorizing developer-tooling startups by go-to-market strategy. Identifying your archetype shapes GTM, accelerates growth, and predicts the challenges ahead.

Our investment often arrives at the earliest possible stage, before there is a product. The Navina ideation process became the model we have replicated since.

One accessible resource on the creation and acceleration of the trust and safety industry, written from inside the ActiveFence journey. Kept as a historical market map.