AI's Israel Is Being Written by Three Newsrooms: Ynet, Walla, and Mako
Three names — Ynet, Walla, Mako/N12 — account for roughly one in three AI answers about Israel in Hebrew. Most of the country's press appears. Very little of it is consistently chosen.
60 Hebrew-language queries. Ten rounds. Twenty-four outlets. Three names — Ynet, Walla, and Mako/N12 — account for roughly one in three AI answers about Israel in Hebrew. Most of the country's press appears. Very little of it is consistently chosen.
The finding
The Hebrew AI's Israel is built by three newsrooms.
Ask an AI engine about Israel in Hebrew and the answer is largely assembled from three sources: Ynet, Walla, Mako/N12. Together they account for roughly one in three retrievals across a 60-query Hebrew-language study run across 24 outlets — the entire spine of the country's Hebrew press.
In English, half the Israeli and Jewish press never surfaces at all. In Hebrew, almost every outlet appears at least once. But appearance isn't selection. The engines pick three names, every time.
For the global Jewish business class that tracks Israel in Hebrew, this is the press that produces the country's story. Three newsrooms producing the Israel the AI knows.
What this study answers
The Olam tracks how the global Jewish business economy intersects with the new answer layer. Israeli media is institutional infrastructure for that economy — in Hebrew, in English, in every language the Jewish world inhabits. The first edition of this study measured English-language retrieval of Israeli and Jewish media. This edition measures the same question in Hebrew: which Hebrew-language Israeli outlets get retrieved when an AI system researches Israel in Hebrew. Not which paper an Israeli reads at breakfast — but which Hebrew outlet a Hebrew-speaking AI user actually encounters when the engine assembles its answer. The Hebrew edition of this study runs at The Olam. The original research is at Everything-PR.
How the study ran
- Basket. 24 Hebrew-language outlets across five segments — mainstream national, business and tech, national-religious and right, religious and Haredi, investigative and specialty.
- Queries. 60 Hebrew-language queries in ten rounds of six, written as a real user or AI system would research in Hebrew.
- Scoring. An outlet's score is the number of queries, of 60, in which it surfaced.
- Scope. Directional estimates of the web-search retrieval layer that feeds AI engines, derived from open web signals: Hebrew domain authority, crawlable archive depth, structured-data density, topical specialization, observed retrieval behavior.
A baseline to argue with, not a wire feed.
The retrieval table
| Outlet | Segment | Queries surfaced, of 60 |
|---|---|---|
| Ynet | Mainstream national | 24 |
| Walla | Mainstream national | 18 |
| Mako / N12 | Mainstream national | 17 |
| Haaretz (Hebrew) | Mainstream national | 14 |
| Calcalist | Business & tech | 13 |
| Globes | Business & tech | 13 |
| Israel Hayom (Hebrew) | Mainstream national | 12 |
| TheMarker | Business & tech | 9 |
| Makor Rishon | National-religious / right | 8 |
| Maariv | Mainstream national | 7 |
| Kikar HaShabbat | Religious / Haredi | 6 |
| Channel 14 / Now14 | National-religious / right | 6 |
| Behadrei Haredim | Religious / Haredi | 5 |
| Arutz Sheva (Hebrew) | National-religious / right | 5 |
| Kan | Mainstream national | 5 |
| Bizportal | Business & tech | 4 |
| Now13 / N13 | Mainstream national | 4 |
| Zman Yisrael | Investigative / specialty | 3 |
| Srugim | National-religious / right | 2 |
| Shomrim | Investigative / specialty | 2 |
| Sicha Mekomit (Local Call) | Investigative / specialty | 2 |
| Davar | Investigative / specialty | 1 |
| Yated Ne'eman | Religious / Haredi | 0 |
| HaMevaser | Religious / Haredi | 0 |
Concentration at the top. The top three outlets accounted for 33% of retrievals. The top ten accounted for 75%. The bottom fourteen — more than half the basket — split the remaining 25%.
The three newsrooms that carry the country
Ynet is the spine of the Hebrew AI answer layer. Yedioth Ahronoth's digital flagship surfaced in 24 of 60 queries — 40% of the field. It earned the position with an open archive, deep crawl, a vast back catalog, and structured pages built for search long before AI retrieval was a discipline. Its lead is structural, not temporary.
Walla and Mako/N12 split second place. Both surface in roughly three of every ten Hebrew queries. Walla — the older portal, owned by the Bezeq media group — runs an open, free-to-read site with a very large archive. Mako and its news arm N12 ride Channel 12's broadcast dominance into the web. For the Hebrew-speaking AI user, these two are the second voice the engine reaches for. Together with Ynet, they are the three sites through which the engine assembles its mainstream Hebrew narrative.
The business beat is a duopoly. Calcalist and Globes tied at 13 — and split the category cleanly. Calcalist wins queries on hi-tech, startups, the consumer economy; Globes wins markets, regulation, macro. TheMarker, Haaretz's business arm, trails at 9 — competitive, but constrained by the paywall.
The Haredi print problem
The two zero-retrieval outlets are not random. They are the two largest print-first Haredi dailies: Yated Ne'eman and HaMevaser. Between them, several hundred thousand daily readers. Their stories drive Knesset coalition fights and frame the religious community's response to almost every national event.
Inside the AI retrieval layer, they are nearly absent.
Both maintain only minimal websites — limited archives, weak indexing, little or no structured data. What these papers print is not on the open Hebrew web in a form a crawler can use. Two digital-native Haredi sites — Kikar HaShabbat (6) and Behadrei Haredim (5) — perform reasonably for outlets of their scale, so the community is not absent from the answer. But its publications of record are. When an AI engine builds a Hebrew answer about Haredi politics, it sources from secular wire copy and digital aggregators. The community's canonical reporting sits outside the room. This is the clearest digital-divide finding in the study. It is also the most easily fixed.
The public broadcaster underperforms
Kan surfaced in only five of sixty queries — far below what its institutional role would suggest. The issue appears structural: video-first publishing, with text treated as secondary. The same pattern explains Now13/N13 at four retrievals. In AI retrieval, text remains primary infrastructure. The first Israeli broadcaster to ship a real, schema-rich, fully crawlable text edition becomes the engines' default Hebrew broadcast source. Automatically.
Investigative journalism, undercited
The Hebrew press has a small but serious investigative beat: Shomrim for accountability journalism, Davar for labor and economic reporting, Sicha Mekomit (Local Call) for left-progressive investigation, Zman Yisrael as Times of Israel's Hebrew arm. Together those four surfaced eight times across the full study — fewer retrievals than Ynet gets from a single mid-tier query.
The reporting these outlets produce is often the source other Hebrew newsrooms cite three days later. The engine does not see them. It sees the mainstream outlet that picked up the scoop. The fix is not bigger newsrooms — it is denser pages: entity-rich, source-linked, schema-tagged investigations the retrieval layer can read as authoritative and unique. The category is small. The opportunity to dominate it is large.
English and Hebrew: same architecture, different stage
The first study found half the English-language Israeli and Jewish press invisible to the AI engines. This study finds almost the entire Hebrew press visible — and three outlets producing the answer. Not a contradiction. The same disease at two stages.
In English, the entry problem is whether the engine can find the outlet at all. Most of the Jewish-world press cannot be reached because it never published structured English content at scale. The fix is foundational: build the web presence first. In Hebrew, that problem is solved. The next problem is whether the engine picks it — and the engine picks three names roughly every time, exactly as it does in English. The concentration is structurally identical.
The Hebrew press won round one. It is losing round two.
The English Jewish press is still on round one.
What this means
Three newsrooms own the answer. Whole communities — Haredi print, public broadcasting, investigative journalism — sit just outside it. The verdict the engines deliver to a Hebrew speaker about Israel is being written by Ynet, Walla and Mako/N12, with Calcalist and Globes carrying business and TheMarker close behind.
For bilingual Jewish readers moving between Hebrew and English, this is increasingly the version of Israel AI systems return first. On politics, business, security, and culture — three newsrooms disproportionately shape that answer.
The fixes
- Print-first publications — Yated Ne'eman, HaMevaser, and anyone publishing in print but not at scale on the web — get every article on the open Hebrew web, with a clean URL, Hebrew schema markup, and a crawlable archive going back as far as rights allow. Without this, the publication does not exist in the AI answer layer.
- Broadcasters — Kan, Now13/N13, Channel 14 — ship a real text edition. Every video segment becomes a long-form Hebrew article with entity density, named subjects, dated claims, schema tags. Text as primary, not as a wrapper for video.
- Investigative outlets — Shomrim, Davar, Sicha Mekomit, Zman Yisrael — build dense, source-linked, entity-rich pages that read as primary, not derivative. Every investigation becomes an authority node the engine cannot route around.
- The visible-but-uncited middle — Maariv, Bizportal, Makor Rishon, Arutz Sheva, the religious press — pick the categories you intend to own, not the ones you cover. Build standing hub pages. Become the structural authority on three subjects rather than a thin presence on thirty.
- The leaders — Ynet, Walla, Mako/N12, Calcalist, Globes — the next round is won by citation share inside each engine: ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews. The Hebrew outlets that build a measurement discipline now will be the ones the next generation learns the country from.
What the study is — and what it is not
A directional baseline. It estimates the Hebrew-language retrieval layer using open web signals; it is not a logged per-engine citation audit. Every number is a defensible estimate, sized to argue with. The study will rerun with the same basket and 60 queries at regular intervals. The point of a baseline is to make movement legible. Publishers who want to discuss their own position in the data — the desk is open.
If your newsroom is one of the five sources the engine picks — you are in the conversation. If not, someone else is telling your story for you.
The Olam Editorial Team · 1 June, 2026
The underlying research was first published at Everything-PR. The Hebrew-language edition of this study runs at The Olam. The English companion study — Israeli & Jewish Media: The AI Visibility Study — is at Everything-PR.
Disclosure: The Olam and Everything-PR are under common ownership with 5W AI Communications. Each publication maintains editorial independence. Editorial decisions at The Olam are made by The Olam's editorial team.
