All data in this report is drawn directly from Lusha’s live signal database, tracking 10 distinct signal categories — funding events, IT spend change, hiring surge, headcount change, website traffic change, commercial activity, product activity, corporate strategy news, market intelligence, and executive moves — across 25 named AI-native companies between January 1 and June 22, 2026. This is a focused sample of named companies, not a full population census. Source: Lusha live signal database, pulled June 22, 2026.
A single signal tells you something changed. Multiple signals firing on the same account at the same time tell you something bigger is happening and this sample shows that pattern is far from evenly distributed. Five of the 25 companies tracked fired all 10 signal categories in the same window. Two companies fired only one or two. The rest sit somewhere in between.
This report maps what “stacking” actually looks like in practice, what it does and doesn’t predict, and one operational finding about company naming that matters for anyone building a target account list from signal data.
The numbers at a glance
Part 1: What “full-stack” signal firing looks like
Five companies in this sample — Anthropic, ElevenLabs, Sierra, Harvey, and Cursor — fired a signal in every one of the 10 categories tracked during the same six-month window. That means each of these companies simultaneously showed: a funding or investment event, rising IT spend, a hiring surge, headcount growth, rising website traffic, commercial activity news, product activity news, corporate strategy news, market intelligence coverage, and at least one executive move.
Read this as a map of commercial visibility, not a ranking of company quality. A company with a low stack depth in this sample isn’t necessarily struggling — Adept, for example, is a small, focused team that may simply generate fewer public signals by design. Stack depth measures how much detectable activity Lusha’s signal layer picked up across categories, not how good the company is.
Part 2: The honest nuance — stacking measures breadth, not growth rate
It would be a clean story if the five full-stack companies were also the five fastest-growing companies in the sample. They aren’t, and it’s worth being direct about that rather than forcing the numbers to fit a tidier narrative.
The full-stack group and the 8-9 category group land at essentially the same average growth rate — 13.8% versus 14.4%, a gap too small to call meaningful. The real drop-off is in the 5-7 category group, at 9.8%. The highest individual growth rates in the sample both sit in the 8-9 tier: Replit at 26% and Mistral AI at 22%. The lowest, Jasper at 1%, sits in the 5-7 tier alongside Decagon, which grew 24% in the same window — a reminder that even within one stack-depth tier, individual companies can move at very different speeds. The takeaway isn’t that stacking is meaningless. It’s that stack depth measures how many different kinds of activity Lusha detected at a company, not how fast that company is growing. A company can be broadly active across ten categories while growing at a measured pace, and a company can post explosive headcount growth while only tripping five or six signal types. Treat stacking as a breadth indicator and headcount or hiring-surge percentage as a speed indicator — they answer different questions, and the most complete account view uses both.
Part 3: A full-stack account up close
Sierra is a useful example because every category fired within the same window: the company closed a $950M Series E, showed rising IT spend, ran an active sales hiring surge, grew headcount, saw rising website traffic, and generated commercial activity, product activity, corporate strategy, market intelligence, and executive-move signals — all inside H1 2026. No single one of those signals would necessarily justify prioritizing the account on its own. Together, they describe a company where every function is expanding at the same time, which is a meaningfully different account to walk into than one where only hiring is up and everything else is flat.
Part 4: What this data revealed about company naming — and why it matters for your own prospecting
While building this report, one company in the sample returned under a name we didn’t search for. We searched for Writer, the AI writing platform at writer.com, and Lusha’s signal database returned results under the name Qordoba — Writer’s original registered company name before it rebranded. The data wasn’t wrong; it was filed under the company’s legal entity name rather than its current public brand.
The practical takeaway: if you’re building a target account list from a brand name alone, you can miss or misattribute signal data for any company that has rebranded, spun out, or been acquired since incorporation. Search by domain, not just brand name, and double-check unfamiliar company names in your results before assuming a data error — it may be the legal entity behind a brand you already know.
Part 5: What this means for account prioritization
1. Use stack depth to find accounts worth a closer look, not to rank them outright.
A full-stack account is broadly active and worth investigating. It isn’t automatically a hotter opportunity than an account with fewer signal categories firing but a sharper hiring surge.
2. Pair breadth (stack depth) with speed (growth rate) before prioritizing.
The two measure different things. An account that’s both broadly active and growing fast — Sierra, Cursor — is a stronger signal than either measure alone.
3. Verify company identity by domain before building a list.
The Writer/Qordoba case shows how a name-only search can miss signal data filed under a legal entity name. Domain-based matching avoids this.
Methodology and data notes
Data source: Lusha live signals API, pulled June 22, 2026.
Sample: 25 named AI-native companies spanning foundation model labs, AI coding tools, AI customer service platforms, and AI-native infrastructure vendors. This is a sample study, not a full population census of the AI-native market.
Signal window: January 1 to June 22, 2026.
Signal categories tracked: financial events (funding, asset investment, strategic investment), IT spend increase, sales/hiring surge, headcount increase, website traffic increase, commercial activity news, product activity news, corporate strategy news, market intelligence news, and executive/people moves. Stack depth counts the number of these 10 categories with at least one detected signal for a company in the window — it does not weight by signal magnitude or frequency.
Data quality note: one company in the original 25-name sample (Writer) returned results under a different registered name (Qordoba) due to legal-entity naming in the underlying data. We identified and disclosed this rather than silently relabeling or excluding it.
Privacy and compliance: All figures in this report are drawn from public, company-level signal data. No individual contact details are included or derivable from the figures presented. Full privacy documentation is available at lusha.com/trust-center.
