Youtube тестирует поиск с AI

YouTube начал тестировать Ask YouTube — поиск с ИИ, где можно задавать вопросы обычным языком и получать не список ссылок, а готовую подборку видео и фрагментов.

Фича уже доступна в США и работает на сложные запросы: если нужно, ИИ уточняет вопрос и подсказывает следующий шаг.

Что это значит для поиска на YouTube и когда новинка дойдёт до других…

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The data-sharing wall: why partner-channel measurement is structurally harder than direct

Direct-sales attribution is hard but tractable — one company owns the full funnel data. Partner-channel attribution adds a structural obstacle that no model fixes: the data lives in two organizations that have rational reasons not to share it fully.

The competing incentives:

— The vendor wants account-level detail to attribute and forecast.
— The partner treats its account relationships and pipeline as a strategic asset and resists exposing accounts the vendor's direct team might poach.

The result is systematic blind spots: vendors see deals only when partners choose to register them, and partners see downstream product usage only when vendors choose to report it. Both sides forecast on partial data and then distrust each other's numbers.

What actually moves this:

— Clean rooms / privacy-preserving matching let both sides reconcile overlapping accounts without raw data exchange — increasingly used for co-sell reconciliation.
— Reciprocal reporting as a contractual term, not a favor: the vendor shares usage/health signals in exchange for the partner sharing pipeline stage.
— Deal registration as the trust primitive: the cleaner the registration system, the less both sides need to over-share to feel protected.

Implications: partner measurement quality is capped by the data-sharing arrangement, not by the analytics stack. Negotiate the data flow before buying another attribution tool.

Open questions: can clean-room reconciliation scale to a long tail of small partners, or is it economical only for top co-sell relationships?

Z.ai анонсировала новую GLM-5.5

Z.ai готовит релиз флагманской GLM-5.5: модель обещают показать в августе 2026 года.

Главная интрига — рост до 1 трлн параметров при том же контекстном окне в 1 млн токенов. Новинка снова будет заточена под код и агентные задачи.

Почему версия сразу 5.5, без 5.3 и 5.4, и что это может означать для рынка — в блоге.

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Telegram запустил собственный сервер для ботов

Telegram запустил собственный сервер для ботов и мани-приложений: теперь backend можно размещать прямо внутри инфраструктуры мессенджера.

Сервер работает на JavaScript/TypeScript, через вебхуки, и позволяет подключать SQL-базу для сбора контактов без посредников.

Пока неясны цена и ограничения — что именно уже можно тестировать, а где скрыт подв…

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When does a SaaS partner ecosystem actually produce network effects?

'Ecosystem' implies network effects — value that compounds as more partners join. Most partner programs are not ecosystems by this test; they are linear referral networks where each partner adds roughly fixed, independent value. The distinction has real strategic consequences.

The test for a genuine ecosystem effect: does partner A's presence increase the value or productivity of partner B, and of the core platform's customers? If not, you have a reseller list, not an ecosystem.

Where true ecosystem effects appear:

— Integration/technology partners. Each new integration raises the platform's switching cost and stickiness for all customers, and makes the platform more attractive to the next integration partner. This is a same-side and cross-side network effect.
— Marketplace partners. A larger app marketplace makes the platform more valuable to buyers, which attracts more developers — the classic two-sided dynamic.

Where they usually don't: pure referral/affiliate partners rarely make each other more productive; adding the 200th affiliate doesn't help the 1st. Value is additive, not multiplicative.

Implications: the investment case differs sharply. Linear referral networks should be optimized for partner activation and per-partner ROI. Ecosystem plays (integrations, marketplaces) justify subsidizing early partners because the externality they create compounds. Conflating the two leads to over-investing in affiliates and under-investing in integrations, or vice versa.

Open questions: at what marketplace density does the cross-side effect become self-sustaining, and how do you measure proximity to that threshold before you reach it?

Google картинки станут конкурентом Pinterest

Google Картинки начали превращать в полноценную платформу с персональной лентой по прошлым запросам — по сути, в аналог Pinterest.

Во вкладке For you уже тестируют подборки, а ещё обещают коллекции и генерацию изображений во встроенной Nano Banana.

Как это будет работать и когда новинка дойдёт до других стран — в блоге.

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Clawbacks and the time-value problem in partner commissions

Commission clawbacks — reclaiming paid commissions when a referred customer churns or refunds early — are framed as fraud control. Their deeper function is aligning partner incentives with customer lifetime value rather than with the moment of sale. The design is a genuine trade-off, not a formality.

Why naive payout-at-close misaligns: if a partner is paid in full at signature, the partner is indifferent to whether the customer succeeds or churns in month two. For subscription businesses where unit economics depend on retention, this rewards partners for selling poor-fit customers who book revenue and then leave.

The clawback parameter space:

— Clawback window — typically tied to the point where the customer's LTV exceeds the commission cost. Too short and partners stay indifferent to churn; too long and partners face unpredictable liabilities and disengage.
— Vesting vs. clawback — paying commissions over time (vesting) is psychologically gentler than paying then reclaiming (clawback), though economically similar. Partners reliably prefer not getting money to giving money back.
— Carve-outs — churn the partner couldn't influence (a product outage, a vendor pricing change) shouldn't be clawed back, or partners stop trusting the program.

Implications: tie the payout schedule to the retention curve, prefer vesting over reclaim where cash flow allows, and exclude vendor-caused churn. The goal is partners selling for fit, not for signature.

Open questions: does retention-linked vesting suppress partner participation enough to offset the quality gains, and for which margin profiles?

Россияне не смогут покупать стейблкоины

Россиянам могут закрыть доступ к покупке стейблкоинов: в новой версии закона их приравняли к иностранным активам.

Купить такие токены смогут только квалифицированные инвесторы — например, с активами от 24 млн рублей или доходом от 12 млн в год.

Что это значит для обычных пользователей и когда правило заработает — в блоге.

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MQL definition drift: the silent killer of partner-program trust

The Marketing Qualified Lead is a negotiated fiction — a threshold marketing and sales agree on so leads can be handed off. In partner programs paid at MQL, the definition becomes a contract term, and definitions drift. That drift, not partner behavior, often explains why programs sour.

How drift happens:

— Quiet redefinition. Sales, flooded with low-quality MQLs, unilaterally raises the internal bar (more fit criteria, higher intent score) without renegotiating the partner payout trigger. Partners are now paid on a definition that no longer produces accepted leads.
— Score inflation. Lead-scoring model changes shift what crosses the line. A model retrain can silently make the same partner lead 'unqualified' overnight.
— Stage proliferation. Adding MQL → SAL → SQL stages moves the goalposts deeper, and partners discover their payout milestone now precedes two more rejection gates.

The cost is trust: partners can't model their return when the qualifying bar moves invisibly, and unpredictable payout terms suppress partner investment more than low payout terms do, per channel-engagement research.

A measured fix: version the qualification definition like an API. Any change to fit criteria, scoring model, or stage gates that affects the payout trigger should be announced with a lead time, and in-flight leads grandfathered under the prior definition.

Implications: the MQL definition is a partner-facing SLA. Treat changes to it as breaking changes, with versioning and deprecation notice.

Open questions: can fit-based qualification be made stable enough to pay on, or is paying at a later, harder-to-game stage the only durable answer?

Quick rec — @NativeHeresy keeps a tight feed on Native ads. If today's post landed, that one's for you.

Attribution measures credit; incrementality measures causation. Partner budgets need the second.

The most consequential confusion in partner analytics is treating attributed revenue as caused revenue. Attribution divides credit for deals that happened. Incrementality estimates which deals would not have happened without the partner. For commission and budget decisions, only the second is defensible — and they often diverge sharply.

Why the gap is large in B2B:

— Self-selection. Partners work accounts they judge winnable. The attributed deals include accounts that would have closed anyway, inflating apparent partner contribution.
— Last-touch capture. A partner's referral link captures a buyer who had already decided via other channels — fully attributed, zero incremental.

How to measure incrementality despite long cycles:

— Geo or segment holdouts. Withhold partner activity from a comparable market and compare closed-won, accepting the long lag.
— Staggered rollout / difference-in-differences. Launch a partner motion in waves and read the delta, controlling for secular trends.
— Account-level randomization where ethics and contracts permit — the cleanest, rarely feasible.

The practical obstacle is patience: a clean B2B incrementality test must run at least one full sales cycle, often two, before it yields signal. Programs that won't wait fall back on attribution and overstate causal impact.

Implications: use attribution for operational reporting; require an incrementality read before scaling partner spend or restructuring commissions. The two answer different questions and frequently disagree.

Open questions: when partners self-select accounts, can any observational method recover causation, or is a holdout the only honest option?