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Das Drei-Klassen-Netz: Technische Analyse der Mobilfunk-Capabilities im Hutchison-Universum

Ein Beitrag von dieIdee.eu – Die InnovationsAgentur | Wien, März 2026

Zusammenfassung

Hutchison Drei Austria (H3A) betreibt eines der drei österreichischen Mobilfunknetze und vermarktet dieses über eigene Marken sowie externe MVNOs (Mobile Virtual Network Operators). Wer „im Drei‑Netz“ surft und telefoniert, tut dies je nach Marke jedoch unter fundamental unterschiedlichen technischen Bedingungen – hinsichtlich Frequenzzugang, 5G‑Modus, Netzpriorisierung und Zugang zum LTE‑Regional‑Roaming mit Magenta. Diese Unterschiede sind in den öffentlich zugänglichen Vertragsunterlagen für Endkunden nicht transparent ausgewiesen.


Der österreichische MVNO‑Markt: Kontext

Der österreichische Mobilfunkmarkt besteht aus drei Netzbetreibern (A1, Magenta, Drei) und einer Vielzahl virtueller Anbieter. Der MVNO‑Marktanteil liegt laut RTR bei rund 18% der SIM‑Karten (ohne M2M), HoT und spusu dominieren dieses Segment. 2023 vereinten 34 MVNOs und Submarken rund 15% Marktanteil und knapp 250 Mio. Euro Umsatz auf sich – bei einem Gesamtmarktvolumen von knapp 4 Mrd. Euro.

Eine Studie von Arthur D. Little (ADL) zeigt: Auf einen erfolgreichen MVNO kommen fünf, die scheitern – und 50, die es gar nicht erst auf den Markt schaffen. Während MNOs in Österreich EBITDA‑Margen knapp unter 30% erzielen, gilt im MVNO‑Geschäft eine Marge von 15–20% bereits als Erfolg. Rund 60% der Gesamtkosten eines MVNO entfallen auf Access‑Kosten, also die Nutzung der fremden Netzinfrastruktur.

Light‑MVNO vs. Full‑MVNO

Die Branche unterscheidet zwei grundlegende Modelle:

  • Light‑MVNO: Kauft definierte Wholesale‑Pakete beim MNO, betreibt kein eigenes Core‑Netz, hat nur begrenzten Einfluss auf Preisgestaltung, technische Parameter und Gebührenstruktur, fokussiert auf Marke, Vertrieb, Kundenbeziehung.
  • Full‑MVNO: Betreibt ein eigenes Kernnetz (Core), eigene OSS/BSS, kauft nur Funkzugang beim MNO, kann Produkte, Tariflogik und technische Features flexibler gestalten.

In Österreich war Red Bull Mobile einer der ersten Full‑MVNOs; aktuell ist spusu der wichtigste Full‑MVNO im Drei‑Netz.


Hutchison Drei Austria: Marken, MVNOs und Markenkooperationen

Unter dem Dach von Hutchison Drei Austria (FN 140132b) existiert ein vielschichtiges Marken‑ und Partnergefüge.

Eigenmarken und Markenkooperationen von Drei

  • Drei – Hauptmarke mit Premium‑ und Volumentarifen.
  • up3 – SIM‑only‑Submarke, seit Juli 2023; Nutzungsklasse „Mobil: D“ laut Entgeltbestimmungen.
  • Hörbi – Diskontmarke, gestartet am 4. März 2026; betrieben durch Hutchison Drei Austria, Nutzungsklasse Mobil D, siehe Entgeltbestimmungen.
  • educom – Studenten‑ und Bildungstarife.
  • eety – Ethno‑Discounter.
  • Joymobile – Nischenmarke.
  • kabelplus MOBILE – Eigenständiger Anbieter aus der EVN‑Gruppe, technisch als MVNO im Drei‑Netz; nutzt Drei‑Sendemasten (2G, LTE, 5G) und eigene Kabelnetze/FTTH für Festnetz und Internet.

Lidl Connect ist eine Marke in Zusammenarbeit zwischen Lidl Österreich und Drei: Lidl verantwortet Vertrieb und Vermarktung, Drei liefert Netz, Infrastruktur, Service und Mobilfunk‑Know‑how. Rechtlich agiert Lidl Connect damit als Markenkooperation/Eigenmarke auf Basis des Drei‑Netzes und nicht als eigenständiger Full‑MVNO im technischen Sinn.

MVNO‑Partner im Drei‑Netz

  • spusu – Full‑MVNO mit eigenem Core‑Netz und eigenen 5G‑Frequenzen (30 MHz im 3,5‑GHz‑Band in Niederösterreich).
  • Weitere kleinere Reseller.

MVNOs und Markenkooperationen in anderen Netzen

  • A1‑Netz: bob, yesss!, XOXO, Georg, Red Bull Mobile, Krone mobile, Lycamobile u.a.
    • Red Bull Mobile ist dabei eine Marke in Kooperation zwischen A1 und Red Bull („A1, bob, Red Bull MOBILE und yesss! stehen für höchste Qualität …“).
  • Magenta‑Netz: HoT (ca. 1,4 Mio. Kunden, 9,6% Marktanteil), S‑Budget Mobile, LIWEST Mobil, Raiffeisen mobil u.a.

ADL‑Partner Christoph Uferer beschreibt die Rolle eines MVNO prägnant: Er sei „Manager der Marge zwischen dem Marktpreis und den Wholesale‑Kosten, die der Netzbetreiber verrechnet“.


Frequenzausstattung: Low‑Band‑Defizit bei Drei als strukturelle Achilles-Ferse

Drei verfügt über folgende Spektren:

FrequenzbandBandbreite (Brutto)TechnologieNutzung / Relevanz
700 MHz FDD (Band 28 / n28)2×10 MHzLTE, 5G NRFlächenversorgung, 5G SA am Land
900 MHz FDD (Band 8)2×5 MHz (davon effektiv ~3 MHz LTE)LTE, GSM, IoTIndoor‑Fallback, geringe Kapazität
1500 MHz SDL (n75)30 MHznur DownlinkKapazitätserweiterung in Kombination mit Low‑Band
1800 MHz FDD (Band 3)2×20 MHzLTE, GSMurbane Hauptversorgung
2100 MHz FDD (Band 1 / n1)2×20 MHzLTE, 5G NRKapazität in Städten
2600 MHz FDD (Band 7)2×25 MHz (davon derzeit 15 MHz aktiv genutzt)LTE, 5G NRKapazitätsband, Teilnutzung
2600 MHz TDD25 MHzLTE, 5G NRKapazitätsband
3400 MHz TDD (n78)100 MHz5G NRHauptband für 5G+ (SA) und 5G NSA

Strukturell kritisch ist, dass Drei kein Spektrum im 800‑MHz‑Band (Band 20) besitzt. Dieses Band ist in Österreich das Rückgrat der LTE‑Flächenversorgung und Indoor‑Abdeckung in ländlichen Regionen. Drei kann B20 nur über Regional‑Roaming mit Magenta nutzen – sofern der jeweilige Tarif bzw. die Marke für dieses Roaming freigeschaltet ist.

Dazu kommt: Das eigene 900‑MHz‑Band (B8) steht nur mit sehr geringer LTE‑Bandbreite zur Verfügung. In der Praxis sehen Kunden häufig zwar ein B8‑Signal, die Kapazität reicht aber kaum für stabile Datendienste. Nutzerberichte im LTE‑Forum deuten zudem darauf hin, dass Hörbi‑Tarife in einzelnen Regionen selbst auf das Drei‑eigene B8 faktisch keinen Zugriff haben, obwohl andere Drei‑Marken dort noch B8 nutzen.


5G bei Drei: 5G, 5G+ und SA/NSA

Drei unterscheidet marketingseitig nicht zwischen „5G“ und „5G+“ im Sinne von SA/NSA, sondern nutzt „5G“ als Oberbegriff für 5G‑Dienste insgesamt.

  • „5G“ steht bei Drei für 5G NR generell, sowohl im Non‑Standalone‑Modus (NSA, mit LTE‑Anker) als auch im Standalone‑Modus (SA, reines 5G‑Core).
  • „5G+“ bezeichnet die 5G‑Standalone‑Produkte mit Bandbreitengarantie, technisch auf Basis des 3,5‑GHz‑Bandes n78.

Drei hat 5G‑Standalone im September 2022 zunächst für Internet‑Tarife („5G+ Fix“/„Premium Internet Tarife mit 5G+“) eingeführt. Handy‑Vertragstarife wurden erst ab März 2024 sukzessive für 5G SA freigeschaltet; davor stand SA nur für ausgewählte Daten‑Tarife und FWA‑Produkte zur Verfügung.

Im ländlichen Raum setzt Drei für 5G‑Flächenversorgung primär auf n28 (700 MHz), im urbanen Bereich auf n78 (3,5 GHz) und n1 (2100 MHz). Das 1500‑MHz‑SDL‑Band n75 spielt als Downlink‑Ergänzung eine Rolle, aber nur in Aggregation mit einem Ankerband.


Leistungsprofile und Nutzungsklassen

Seit Februar 2024 arbeitet Drei mit einem System statischer Leistungsprofile (A–D) und Nutzungsklassen (mobil/stationär), das in den Entgeltbestimmungen sowie in den EB von Wholesale‑Partnern wie kabelplus MOBILE dokumentiert ist.

LeistungsprofilPriorität im Netz (mobil/stationär)Beispiel‑Tarife
A600 / 300Drei Unlimited L, XL (Premium)
B300 / 150ausgewählte Premium‑Vertragstarife
C100 / 50Drei Unlimited M, Basic S
D60 / 30up3, Hörbi, MyLife SIM Start, Discountprodukte

Bei ausgelastetem Sektor erhält ein Kunde mit Profil A die sechs‑ bis zehnfache Bandbreite eines Kunden mit Profil C bzw. D. Mobile Tarife werden gegenüber stationären Tarifen zusätzlich um den Faktor 2 bevorzugt.

Die Entgeltbestimmungen von up3 weisen explizit „Nutzungsklasse: Mobil, Leistungsprofil: D“ aus. Für Hörbi ist diese Information zunächst nicht in den Vertragszusammenfassungen sichtbar, findet sich aber in den Entgeltbestimmungen: Hörbi‑Tarife sind ebenfalls der Nutzungsklasse Mobil und dem Leistungsprofil D zugeordnet.


TKK‑Bescheid C 1/23: Active Sharing, Regional Roaming und MVNO‑Auflage

Mit Bescheid C 1/23 genehmigte die Telekom‑Control‑Kommission (TKK) eine Kooperation zwischen Drei und Magenta zur gemeinsamen Nutzung aktiver Netzkomponenten („Active Sharing 1“) und zur Einführung eines strukturierten Regional‑Roaming‑Modells im 4G‑ und 5G‑Bereich.

Kernstück ist die MVNO‑Auflage, wonach Drei und Magenta die im Rahmen der Kooperation erzielten Verbesserungen bei Versorgung, Qualität und Performance „allen Endverbrauchern“ und insbesondere auch allen bestehenden MVNO‑Partnern über deren bestehende Vorleistungsverträge zugänglich machen müssen. Dies umfasst insbesondere:

  • Zugang zu Sprach‑, SMS‑ und Datendiensten in den durch Active Sharing/Regional Roaming zusätzlich versorgten 4G‑ und 5G‑Gebieten.
  • Zugang zu VoLTE‑Diensten im Netz des jeweiligen Host‑Betreibers.

Die TKK reagierte damit auf wettbewerbsrechtliche Bedenken der Bundeswettbewerbsbehörde und des Bundeskartellanwalts, die gefordert hatten, MVNOs nicht von den Kooperationsvorteilen auszuschließen.

Wichtig: Die Auflage erwähnt ausdrücklich „MVNO‑Partner“. Hörbi ist keine MVNO‑Partnerin, sondern eine Eigenmarke von Hutchison Drei Austria (identische Firmenbuchnummer, identischer Rechtsträger). Ob drei‑interne Submarken (up3, Hörbi, educom, eety) rechtlich denselben Zugang wie externe MVNOs erhalten müssen, ist damit nicht klar geregelt.


Vergleichsmatrix der technischen Capabilities

Die folgende Tabelle fasst die wesentlichen technischen Parameter ausgewählter Marken im Drei‑Netz zusammen (Stand März 2026, auf Basis öffentlich zugänglicher Unterlagen und Nutzerberichte):

AnbieterTyp5G SA5G NSARegional Roaming Magenta (B3/B20)Nutzungsklasse / ProfilFrequenzen (praktisch)
AnbieterTyp5G SA5G NSARegional Roaming Magenta (B3/B20)Nutzungsklasse / ProfilFrequenzen (praktisch)
Drei Unlimited L/XLEigenmarkeJa (5G+)JaJamobil AB3, B1, B7(15 MHz), n28, n78
Drei Unlimited M / Basic SEigenmarkeJaJaJamobil CB3, B1, B7(15 MHz), n28, n78
up3Drei‑SubmarkeJa (ab 3/2024)JaJamobil DB3, B1, B7(15 MHz), n28, n78
Lidl ConnectMarkenkooperation Drei/LidlNein (kein 5G+)JaJa (MVNO‑Auflage)nicht publiziert, faktisch mobil C/DB3, B1, B7(15 MHz), B20 via Magenta, n78
spusu 5GFull‑MVNONein (über Drei‑Netz)JaJa (MVNO‑Auflage)nicht publiziertB3, B1, B7(15 MHz), B20 via Magenta, n78; teilweise eigene 3,5 GHz‑Zellen
kabelplus MOBILEMVNONein (5G via Drei‑NSA)JaJa (laut EB + MVNO‑Auflage)laut EB: stationär B/CB3, B1, B7(15 MHz), B20 via Magenta, n78
Hörbi 200 5G‑PowerDrei‑DiskontmarkeNein (nur 5G NSA)JaNein (kein Magenta‑Roaming)mobil DB3, B1, B7(15 MHz); B8 teils faktisch nicht nutzbar; kein B20
Hörbi 60 (LTE)Drei‑DiskontmarkeNeinNein (nur LTE)Neinmobil DB3, B1, B7(15 MHz); B8 teils faktisch nicht nutzbar; kein B20

Bei Hörbi ist besonders kritisch: Nutzer im LTE‑Forum dokumentieren Fälle, in denen selbst das Drei‑eigene B8 (900 MHz) für Hörbi‑Karten nicht nutzbar ist, während andere Drei‑Marken noch via B8 versorgt sind. In Kombination mit dem fehlenden Magenta‑Roaming führt dies dazu, dass Hörbi‑Kunden in ländlichen Regionen deutlich häufiger „Kein Netz“ sehen, obwohl andere Drei‑Produkte am selben Standort noch funktionale Versorgung haben.


Das Drei-Klassen-System

Klasse 1 – Vollzugang (Drei-Vertragstarife ab Basic S): 5G SA + NSA, volles Magenta Regional Roaming (Band 3, Band 20), Leistungsprofile A–D (je nach Tarif), alle Drei-eigenen Frequenzen einschließlich n28 und n75 im SA-Modus.

Klasse 2 – Eingeschränkter Zugang (MVNOs: Lidl Connect, spusu, kabelplus): 5G NSA (kein SA), Magenta Regional Roaming aktiv (über TKK-Bescheid C 1/23), nicht publiziertes Leistungsprofil, kein Zugang zu n28/n75 im SA-Modus. Innerhalb dieser Klasse hat spusu als Full-MVNO mit eigenem Core-Netz und eigenen 5G-Frequenzen (30 MHz im 3,5-GHz-Band in NÖ) potenziell mehr Handlungsspielraum als Light-MVNOs.

Klasse 3 – Minimalzugang (Hörbi): 5G NSA (beim 5G-Tarif) oder nur LTE (Hörbi 60), kein Magenta Regional Roaming, nur Drei-eigene Sender (B8/3 MHz, B3/15 MHz, B1/20 MHz, B7/20 MHz), kein Band 20, kein Band 28/n28 im SA-Modus, Nutzungsklasse mobil D.


Die Konsequenz für Endkunden

Das Szenario: Drei Personen mit „5G-Verträgen im Drei-Netz“ am selben Ort mit demselben Endgerät:

  1. Drei-Direktkunde (up3): 5G SA mit n28 + n75, stabile Versorgung
  2. Lidl-Connect-Kunde (5G NSA): 4G über Magenta Regional Roaming (Band 20), brauchbarer Empfang
  3. Hörbi-Kunde (5G NSA, kein Magenta): Kein Empfang, EDGE, oder ein Balken ohne Konnektivität

ADL-Partner Christoph Uferer beschreibt die Rolle eines MVNO als „Manager der Marge zwischen dem Marktpreis und den Wholesale-Kosten, die der Netzbetreiber verrechnet“. Das gilt für echte MVNOs. Bei Hörbi als Eigenmarke des Netzbetreibers stellt sich die Frage anders: Hier entscheidet der Netzbetreiber selbst, welchen Teil seiner Infrastruktur er seiner Diskontmarke zur Verfügung stellt – und wie transparent er das kommuniziert.


Regulatorische Perspektive

Die RTR stellte in ihrem Newsletter 02/2025 fest:

  • Der MVNO-Anteil bei 5G-SIM-Karten liegt „deutlich unter 5%“, während der Gesamtmarktanteil rund 18% beträgt
  • „Bei Technologieübergängen – etwa zu 5G, oder auch zu VoLTE – wurde der Zugang zur Netzinfrastruktur für MVNOs oftmals verzögert oder nur mit höheren Vorleistungsentgelten gewährt“
  • „Bei VoLTE ermöglichte bei einzelnen Anbietern erst eine Auflage in einem regulatorischen Verfahren den Zugang“

Die eSIM-Technologie wird den MVNO-Markt weiter dynamisieren. Laut ADL stehen 30 bis 40% der österreichischen Smartphones bereits mit eSIM-Funktion zur Verfügung. Der digitale Point-of-Sale wird physische Filialen ersetzen – ADL-Partner Uferer spricht von einer „totalen Revolution“. Für 2026 ist zudem der MVNO-Launch der Österreichischen Post im A1-Netz angekündigt.

In diesem dynamischen Marktumfeld wäre eine regulatorische Klarstellung überfällig: Müssen die technischen Capabilities eines Tarifs – 5G SA/NSA, Regional-Roaming-Zugang, Leistungsprofil – für Endkunden transparent ausgewiesen werden? Die aktuelle Informationslage ist unzureichend. Weder in Hörbi-Vertragszusammenfassungen noch in den Entgeltbestimmungen von Lidl Connect finden sich Angaben dazu.


Quellen: RTR Newsletter 02/2025; TKK-Bescheid C 1/23 (Kooperationsgenehmigung Drei/Magenta); RTR-Presseaussendung 12.1.2024; Entgeltbestimmungen Drei (Special Tarife, April 2025; up3, Mai 2025; Unlimited Tarife, Januar 2025); Vertragszusammenfassungen Hörbi (Februar 2026); Entgeltbestimmungen Lidl Connect (Februar 2026); Leistungsbeschreibung spusu 5G (Jänner 2024); OTS-Presseaussendung Hörbi (4.3.2026); Arthur D. Little, „Riding the MVNO Wave: 10 Keys to Success“; report.at, „MVNO – Ein schwieriger Markt“ (2025); tarife.at; teltarif.de; lteforum.at.

Traditional AdTech is Dead. Long Live AdTech For AI

The rebirth of advertisements and AdTech in the age of AI

AdTech used to matter. In the early 2000s, it was one of the hottest areas in tech – hundreds of startups, billions in VC funding, genuine innovation in targeting and formats. Then Google bought DoubleClick in 2007 for $3.1 billion, Facebook launched its ad platform, and the game was over. The duopoly that emerged didn’t just dominate—at their peak, Google and Meta controlled nearly 80% of U.S. digital ad growth. Everyone else was left fighting for scraps. For the past 15 years, „AdTech startup“ became practically an oxymoron as the industry consolidated into irrelevance.

But now, in the age of AI, we are starting to see a resurgence of advertising as a booming revenue source for companies. OpenAI announced last week that they would be testing ads in ChatGPT in a “bid to boost revenue”, and the healthcare AI startup OpenEvidence recently surpassed $100M annualized run-rate revenue (and doubled their valuation to $12B!), largely on an ad-supported revenue model. And around this, the market for AI tools in advertising optimization is growing quickly.

So why are we seeing such a sharp resurgence in a field that just a few years ago was essentially dead?

Three fundamental shifts are driving this renaissance:

First, AI platforms have created the first genuinely new advertising surface since social media. ChatGPT’s 800+ million weekly active users and Claude’s ~20 million monthly active users represent massive, engaged audiences that didn’t exist two years ago. Unlike the incremental improvements of the past decade – slightly better targeting, marginally improved attribution – these platforms represent entirely new real estate where the old duopoly rules don’t apply.

Second, intent signals are dramatically more sharply defined than anything we’ve seen before. When someone types “best CRM for startups” into Google, you get a decent intent signal. But when someone has a 20-message conversation with ChatGPT about their specific sales team structure, pain points, budget constraints, and technical requirements? That’s intent data at a resolution advertisers have only dreamed about. The conversational nature of AI interactions creates a richness of context that search queries simply can’t match.

Third, entirely new infrastructure is required—and being built at breakneck speed. The old AdTech stack was built for display ads, search results, and social feeds. None of it works for conversational AI. How do you measure attribution when there’s no “click”? How do you bid on inventory that’s generated dynamically in response to natural language? What does “viewability” even mean in a text-based conversation? This infrastructure gap is spawning entirely new categories like Generative Engine Optimization (GEO), with dozens of startups raising millions to solve problems that didn’t exist 18 months ago.

So should we be heralding the rebirth of AdTech in the age of AI?

Let’s dig in.


AI is Creating New Real Estate for Ads

The rapid growth of foundation models with “chatbot-style interfaces” has brought forward what we believe is the first new real estate for advertisements since the emergence of social networks. Google and Meta were able to establish dominance in ad models by aggregating eyeballs; Google in search and Meta in social. (And to a lesser extent other social media platforms like Snap, Pinterest, etc.). As an advertiser, why would I place my ads anywhere other than where consumers are aggregating to get most bang for my buck?

Now in the Age of AI, consumers are no longer flocking to the traditional platforms but increasingly to places like ChatGPT (>800M WAUs) and Claude (~20M MAUs). And these consumers are not just making simple search queries but having full-blown conversations on every topic under the sun. This is an advertiser’s dream: a wide-scale canvas with rich, user-generated intent. And advertisers are no longer limited to traditional search displays with sponsored results but can embed more natural advertisements within AI-generated responses. While the consumer may not love this (more on that below), it certainly makes sense for the advertisers.

Beyond consumer-facing platforms, AI development tools are creating a quieter but equally significant advertising opportunity. When developers use tools like Lovable, Replit, or Cursor to build applications, these platforms make dozens of architectural decisions on their behalf—which database to use, where to host, which payment processor to integrate.

Each of these decisions represents potential advertising inventory. Supabase could sponsor recommendations in database selection flows. Vercel could appear as a ‘suggested deployment option’ when a developer’s app is ready to ship. Stripe could surface contextual offers when payment processing code is being written.

The key difference from traditional developer advertising (think Stack Overflow banner ads) is that these aren’t interruptions—they’re recommendations at the exact moment of intent. A developer isn’t being shown a database ad while reading about React hooks; they’re being offered database options precisely when their AI agent is about to scaffold database code. The conversion potential is orders of magnitude higher.

Vertical AI Is Creating Specialized Inventory

It’s not just the large model providers themselves that are benefiting from ads.

The rise of vertical AI providers is creating a new, specialized inventory for high-intent, high-value ads. Verticals like healthcare, legal, finance, real estate, and other professional services are becoming the new adtech frontier, offering advertisers direct access to high-value audiences outside the Google-Meta duopoly for the first time in over a decade.

One great example here is OpenEvidence, which has quickly grown into the leading “AI-powered medical search engine” for clinicians. The company recently said that 40% of physicians across the US across 10K hospitals and medical centers now use OpenEvidence on a daily basis. What else is interesting and unique is its business model: OpenEvidence is free to use for verified medical professionals, and generates revenue largely through advertising.

Per a great business breakdown from Contrary Research:

Given that pharmaceutical companies spent approximately $20 billion annually on marketing to healthcare professionals in the US as of 2019, capturing a portion of this market through digital channels could generate substantial revenue for the company. OpenEvidence’s advertising focus on contextual advertising and sponsored content while maintaining trust. For example, if a doctor submits a query about diabetes treatments, a sponsored summary from a pharmaceutical manufacturer may appear, or a banner for relevant clinical webinars could be displayed.

This advertising model has allowed OpenEvidence to reach >$100M annualized run-rate revenue in just a few short years.

We believe that other vertical AI tools will also embed this type of model, giving away the product to end users for free while generating revenue from charging advertisers. In vertical AI, the intent signals are clearer than ever—and unlike the generic search box, users are getting AI agents that actually solve their specific problems, creating a sustainable value exchange that justifies the ad-supported model.

Measurement Primitives are Changing and New Infrastructure is Emerging

Attribution in AI-native experiences is fundamentally different. The old AdTech stack was built for discrete surfaces where ads could be served, clicked, and tracked, but in conversational and agentic interfaces, there’s often no obvious “ad slot” and no click at all. Instead, influence is embedded inside multi-turn workflows: what the model recommends, what the user accepts, and what gets generated.

In next-gen AI apps, advertising is moving into the flow of work. When a developer scaffolds an app in Cursor, Lovable, or Vercel, the inventory isn’t a banner but it’s the moment an agent suggests a database, auth provider, or cloud service. In vertical AI tools, the same pattern holds: the “ad” looks like a contextual recommendation for a pharmaceutical brand, clinical resource, or specialized service. These touchpoints are integrated into the utility itself.

This shift is spawning an entirely new measurement rail. If clicks disappear, the new primitives become telemetry and adoption: logging multi-turn conversations, mapping model outputs to downstream actions, and tracking “acceptance events” like tab-to-insert, install, purchase, or integration. And because influence in a conversation is cumulative, the real challenge isn’t just attribution but it’s incrementality: did the recommendation actually change what the user would have done otherwise?

As a result of this shift, we are starting to see new ad networks emerge to serve these “in-flow” moments.

  • On the measurement side, companies like Profound and Bluefish are building the GEO observability layer, tracking share-of-response, competitive displacement, and brand presence across models.
  • On the distribution side, a new generation of AI-native ad platforms is forming across multiple surfaces: platforms like ZeroClick, OpenAds, and Nex.ad are beginning to monetize dynamic, contextually relevant recommendations inside or alongside AI conversations, while publisher-centric AI engagement platforms like Linotype.ai help site owners retain users and surface native monetization opportunities.

But unlike the old web, the “auction” can’t just pick the highest bidder. It has to operate inside generation loops, ranking units based on contextual relevance, quality, and bid while navigating trust and policy constraints in sensitive domains like healthcare and legal. Pricing models may shift as well, away from CPM/CPC and toward outcomes like cost-per-accept, cost-per-embed, or cost-per-adoption.

The biggest wildcard is walled gardens. If OpenAI, Anthropic, Google, and vertical copilots control the interface, they may also control the inventory and measurement rails, turning AI advertising into a handful of closed ecosystems rather than an open programmatic market. Time will tell!

Nexad.ai Reinventing Ads for the AI Era

Conclusion / Challenges

There’s one clear challenge in all of this: people generally dislike advertisements. A recent report found that 81% of young people hate ads, and 60% find them intrusive. And who’s to blame them? Most people find ads annoying and not beneficial to them, and now nearly half the internet uses an ad blocker.

Another key challenge is whether people will feel that the answers they are served by the LLMs are influenced by the advertisements that appear. If I ask Claude for the best recommendations for hotels in Switzerland, will I know it showing me what the model says is “best”, or which hotel is spending the most on advertising for this query result?

But here’s the interesting part: in the same study referenced above, only 28% of respondents wanted fewer ads. Which suggests that its not the brands or products being peddled they dislike, but how the ads are actually served.

This could actually be a boon to the new platforms like OpenAI and Anthropic, as well as the emerging AI Adtech tools. By finding creative, non-intrusive, intent-based, transparent, and beneficial ways to reach consumers, a new form of advertising could actually flourish.

So we’re left with the thought…

“Traditional AdTech is Dead…Long Live AdTech For AI“.

Source: https://aspiringforintelligence.substack.com/p/traditional-adtech-is-dead-long-live

Kids in China Are Using Bots and Engagement Hacks to Look More Popular on Their Smartwatches

 
 
In China, parents are buying smartwatches for children as young as 5, connecting them to a digital world that blends socializing with fierce competition.
Image may contain Meng Xiaodong Body Part Finger Hand Person Baby and Shelf
Photo-Illustration: WIRED Staff; Getty Images
 
 

At what age should a kid ideally get a smartwatch? In China, parents are buying them for children as young as five. Adults want to be able to call their kids and track their location down to a specific building floor. But that’s not why children are clamoring for the devices, specifically ones made by a company called Xiaotiancai, which translates to Little Genius in English.

The watches, which launched in 2015 and cost up to $330, are a portal into an elaborate world that blends social engagement with relentless competition. Kids can use the watches to buy snacks at local shops, chat and share videos with friends, play games, and, sure, stay in touch with their families. But the main activity is accumulating as many “likes” as possible on their watch’s profile page. On the extreme end, Chinese media outlets have reported on kids who buy bots to juice their numbers, hack the watches to dox their enemies, and sometimes even find romantic partners. According to tech research firm Counterpoint Research, Little Genius accounts for nearly half of global market share for kids’ smartwatches.

Status Games

Over the past decade, Little Genius has found ways to gamify nearly every measurable activity in the life of a child—playing ping pong, posting updates, the list goes on. Earning more experience points boosts kids to a higher level, which increases the number of likes they can send to friends. It’s a game of reciprocity—you send me likes, and I’ll return the favor. One 18-year-old recently told Chinese media that she had struggled to make friends until four years ago when a classmate invited her into a Little Genius social circle. She racked up more than one million likes and became a mini-celebrity on the platform. She said she met all three of her boyfriends through the watch, two of whom she broke up with because they asked her to send erotic photos.

 

High like counts have become a sort of status symbol. Some enthusiastic Little Genius users have taken to RedNote (or Xiaohongshu), a prominent Chinese social media app, to hunt for new friends so as to collect more likes and badges. As video tutorials on the app explain, low-level users can only give out five likes a day to any one friend; higher-ranking users can give out 20. Because the watch limits its owner to a total of 150 friends, kids are therefore incentivized to maximize their number of high-level friends. Lower-status kids, in turn, are compelled to engage in competitive antics so they don’t get dumped by higher-ranking friends.

“They feel this sense of camaraderie and community,” said Ivy Yang, founder of New York-based consultancy Wavelet Strategy, who has studied Little Genius. “They have a whole world.” But Yang expressed reservations about the way the watch seems to commodify friendship. “It’s just very transactional,” she adds.

Engagement Hacks

On RedNote/Xiaohongshu, people post videos on circumventing Little Genius’s daily like limits, with titles such as “First in the world! Unlimited likes on Little Genius new homepage!” The competitive pressure has also spawned businesses that promise to help kids boost their metrics. Some high-ranking users sell their old accounts. Others sell bots that send likes or offer to help keep accounts active while the owner of a watch is in class.

Get enough likes—say, 800,000—and you become a “big shot” in the Little Genius community. Last month, a Chinese media outlet reported that a 17-year-old with more than 2 million likes used her online clout to sell bots and old accounts, earning her more than $8,000 in a year. Though she enjoyed the fame that the smartwatch brought her, she said she left the platform after getting into fights with other Little Genius “big shots” and facing cyberbullying.

 

In September, a Beijing-based organization called China’s Child Safety Emergency Response warned parents that children with Little Genius watches were at risk of developing dangerous relationships or falling victim to scams. Officials have also raised alarms about these hidden corners of the Little Genius universe. The Chinese government has begun drafting national safety standards for children’s watches, following growing concerns over internet addiction, content unfit for children, and overspending via the watch payment function. The company did not respond to requests for comment.

I talked to one parent who had been reluctant to buy the watch. Lin Hong, a 48-year-old mom in Beijing, worried that her nearsighted daughter, Yuanyuan, would become obsessed with its tiny screen. But once Yuanyuan turned 8, Lin relented and splurged on the device. Lin’s fears quickly materialized.

 

Yuanyuan loved starting her day by customizing her avatar’s appearance. She regularly sent likes to her friends and made an effort to run and jump rope to earn more points. “She would look for her smartwatch first thing every morning,” Lin said. “It was like adults, actually, they’re all a bit addicted.”

 

To curb her daughter’s obsession, Lin limited Yuanyuan’s time on the watch. Now she’s noticing that her daughter, who turns 9 soon, chafes at her mother’s digital supervision. “If I call her three times, she’ll finally pick up to say, ‘I’m still out, stop calling. I’m not done playing yet,’ and hang up,” Lin said. “If it’s like this, she probably won’t want to keep wearing the watch for much longer.”


This is an edition of Zeyi Yang and Louise Matsakis Made in China newsletter. Read previous newsletters here.

 

Why Google Chrome could be a far better product if it wasn’t beholden to Google’s other business interests?

Googles search engine and the Browser Google Chrome could have been far better products if it wasn’t beholden to Google’s other business interests:

They allege that Google blocked the introduction of user-friendly features because they would have harmed the company’s advertising revenue, which depends on people clicking ads in their search results. “Why isn’t autocomplete better? Why isn’t the ‘new tab’ page more effective? Why isn’t browser history better?” says the ex-leader, who also spoke on the condition of anonymity. The answer: “There’s all these incentives to get users to search.”

Read the whole story: https://www.wired.com/story/doj-google-chrome-antitrust/

Google Selling Chrome Won’t Be Enough to End Its Search Monopoly


To dismantle Google’s illegal monopoly over how Americans search the web, the US Department of Justice wants the tech giant to end its lucrative partnership with Apple, share a trove of proprietary data with competitors and advertisers, and “promptly and fully divest Chrome,” Google’s browser that controls more than half of the US market. The government also wants approval regarding who takes over Chrome.

The recommendations are part of a detailed plan that government attorneys submitted Wednesday to US district judge Amit Mehta in Washington, DC, as part of a federal antitrust case against Google that started back in 2020. By next August, Mehta is expected to decide which of the possible remedies Google will be required to carry out to loosen its stranglehold on the search market.

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But the tech giant could still appeal, delaying enforcement of the judge’s order years into the future. On Wednesday, Google president Kent Walker characterized the government’s proposals as “staggering,” “extreme,” “a radical interventionist agenda,” and “wildly overbroad.” He wrote in a blog post that the changes being sought “would break a range of Google products—even beyond Search—that people love and find helpful in their everyday lives.” He also asserted the privacy and security of Google’s users would be put at risk.

Among people who have worked for Google or partnered closely with the company, there’s little agreement on whether any of the proposed remedies would significantly shift user behavior or make the search engine market more competitive. Four former Google executives who oversaw teams working on Chrome, Search, and Ads told WIRED that innovation by rivals, not interventions by the government, remains the surest way to unseat Google as the nation’s dominant internet search provider. “You can’t ram an inferior product down people’s throats,” says one former Chrome business leader, speaking on the condition of anonymity to protect professional relationships.

But a former Chrome engineering leader acknowledged that the search engine could have been a better product if it wasn’t beholden to Google’s other business interests. They allege that Google blocked the introduction of user-friendly features because they would have harmed the company’s advertising revenue, which depends on people clicking ads in their search results. “Why isn’t autocomplete better? Why isn’t the ‘new tab’ page more effective? Why isn’t browser history better?” says the ex-leader, who also spoke on the condition of anonymity. The answer: “There’s all these incentives to get users to search.” Google didn’t respond to a request for comment on the assertion.

Still, competitors that stand to benefit from even a minor reduction in Google’s power are optimistic about the expected remedies. “I can see strong benefits in putting [Chrome] back in the hands of the community,” says Guillermo Rauch, CEO of Vercel, a company that develops tools for websites, many of which depend on search traffic and advertising revenue controlled by Google. “Moderating that relationship to the corporate overlords is always going to be a healthy thing,” Rauch says.

Gabriel Weinberg, CEO of the rival search engine DuckDuckGo, said in a statement that the government’s proposed remedies “would free the search market from Google’s illegal grip and unleash a new era of innovation, investment, and competition.”

Google’s antitrust battle with the Department of Justice began under the first Trump administration in 2020. The federal government, as well as a number of states, accused the tech giant of using anticompetitive tactics to dominate the search market, suppressing Americans’ access to other search providers. The Biden administration moved forward with the case and filed another of its own—accusing Google of illegally monopolizing advertising technologies that millions of websites and apps use to generate revenue. Closing arguments in that case are scheduled for Monday.

Both cases remain unresolved, and it’s unclear to what extent the Justice Department will keep up the pressure on Google after Donald Trump returns to the White House. On the campaign trail, Trump made mixed comments about the tech giant. In October, he expressed concerns about its power, but suggested that imposing onerous conditions on the company could hamper US efforts to achieve tech supremacy over China.

Judge Mehta has set aside nearly two weeks starting in April to hear arguments from the government and Google about the proposed punishments. The new Trump administration’s approach toward Google should become more apparent at that point, and it’s possible that government attorneys will be less willing to defend the proposals released Wednesday.

Walker’s blog on Wednesday highlighted possible ramifications of the proposals that Trump may view as concerning, including the chilling of AI investment and the appointment of a five-expert Technical Committee to monitor Google’s compliance with remedies. “And that’s just a small part of it,” Walker wrote about the proposed panel. “We wish we were making this up.”

The government is seeking to provide users with more choice over what search engines they use. It wants to end Google’s partnership with Apple, which receives tens of billions of dollars in search ad revenue for making Google the default search engine on iPhones. Google has similar deals with other companies, which also would be scuttled.

Google would also have to make changes to how it preferences its own services on Android or else sell, or be forced to sell, Android. The proposals call for Google to give advertisers a stream of data to help them study their purchases.

To give competitors a leg up, the government wants Google to share its search index and the data it collects about users when determining which results to show. The argument is that potential rivals would then be able to match the information advantage Google has amassed over decades studying the behavior patterns of its billions of users. In addition, Colorado’s attorney general proposed in Wednesday’s filing that Google fund “reasonable, short-term incentive payments” to users who opt for non-Google default search engines.

On top of having to divest of Chrome, Google would be banned from launching a new browser or investing in search, ad tech, and AI rivals for five to 10 years. The government says the restrictions would enable “fostering innovation and transforming the general search and search text ads markets over the next decade.”

Rauch, the Vercel CEO, believes that Google is unfairly using Chrome to direct people toward its AI chatbot, Gemini, as well as other services it owns, such as Google Docs, through a mix of nudges and incentives built into its search engine. “Google is stacking every advantage that they can by monopolizing this very important piece of software infrastructure,” Rauch says.

Turning over Chrome to a neutral steward like a nonprofit organization or an academic institution, Rauch says, would burst open the search box on the world’s most popular browser and give people access to a plethora of alternatives. Chrome already allows users to change their default search provider, but Google still nudges users back through alerts as they browse. “I could imagine, in a world where people are more equipped to choose rather than default, a lot of consumers might end up choosing Perplexity or ChatGPT, whereas today it’s a very roundabout thing,” Rauch says.

But financial and legal analysts have expressed doubts about how much the government’s proposals could really achieve. The former Google executives who spoke with WIRED are just as skeptical. Rajen Sheth, who oversaw parts of the Chrome business and now runs a software startup for building online courses, says users are gravitating toward what they are used to in what he believes is already an open marketplace. “Given the technology landscape and the different levers, are there things that will make a difference? It will be tough,” he says.

Getting access to Google’s proprietary data and having the opportunity to court iPhone users may help increase the odds that people turn to alternative search engines. But Google also has unmatched computing infrastructure, unique data from sibling services such as Maps, and more than a quarter-century of brand recognition with consumers. “No matter how much you level the playing field, people are going to go to the best product for the job,” the former Chrome business leader says.

Former Google executives say that what will supplant the company one day isn’t another traditional search engine, but something akin to ChatGPT that presents content to users in a more interactive way. That new technology isn’t fully developed yet, but it might be by the time the government’s lawsuit against Google is finally settled. That means Google’s place in the market could look vastly different before enforcement of the judge’s order even begins.

I Stared Into the AI Void With the SocialAI App

SocialAI is an online universe where everyone you interact with is a bot—for better or worse.

Robot Hands Adults in a Crowd Glitch Effect

The first time I used SocialAI, I was sure the app was performance art. That was the only logical explanation for why I would willingly sign up to have AI bots named Blaze Fury and Trollington Nefarious, well, troll me.

Even the app’s creator, Michael Sayman, admits that the premise of SocialAI may confuse people. His announcement this week of the app read a little like a generative AI joke: “A private social network where you receive millions of AI-generated comments offering feedback, advice, and reflections.”

But, no, SocialAI is real, if “real” applies to an online universe in which every single person you interact with is a bot.

There’s only one real human in the SocialAI equation. That person is you. The new iOS app is designed to let you post text like you would on Twitter or Threads. An ellipsis appears almost as soon as you do so, indicating that another person is loading up with ammunition, getting ready to fire back. Then, instantaneously, several comments appear, cascading below your post, each and every one of them written by an AI character. In the new new version of the app, just rolled out today, these AIs also talk to each other.

When you first sign up, you’re prompted to choose these AI character archetypes: Do you want to hear from Fans? Trolls? Skeptics? Odd-balls? Doomers? Visionaries? Nerds? Drama Queens? Liberals? Conservatives? Welcome to SocialAI, where Trollita Kafka, Vera D. Nothing, Sunshine Sparkle, Progressive Parker, Derek Dissent, and Professor Debaterson are here to prop you up or tell you why you’re wrong.

Screenshot of the instructions for setting up the Social AI app.

Is SocialAI appalling, an echo chamber taken to the extreme? Only if you ignore the truth of modern social media: Our feeds are already filled with bots, tuned by algorithms, and monetized with AI-driven ad systems. As real humans we do the feeding: freely supplying social apps fresh content, baiting trolls, buying stuff. In exchange, we’re amused, and occasionally feel a connection with friends and fans.As notorious crank Neil Postman wrote in 1985, “Anyone who is even slightly familiar with the history of communications knows that every new technology for thinking involves a trade-off.” The trade-off for social media in the age of AI is a slice of our humanity. SocialAI just strips the experience down to pure artifice.

“With a lot of social media, you don’t know who the bot is and who the real person is. It’s hard to tell the difference,” Sayman says. “I just felt like creating a space where you’re able to know that they’re 100 percent AIs. It’s more freeing.”

You might say Sayman has a knack for apps. As a teenage coder in Miami, Florida, during the financial crisis, Sayman gained fame for building a suite of apps to support his family, who had been considering moving back to Peru. Sayman later ended up working in product jobs at Facebook, Google, and Roblox. SocialAI was launched from Sayman’s own venture-backed app studio, Friendly Apps.

In many ways his app is emblematic of design thinking rather than pure AI innovation. SocialAI isn’t really a social app, but ChatGPT in the container of a social broadcast app. It’s an attempt to redefine how we interact with generative AI. Instead of limiting your ChatGPT conversation to a one-to-one chat window, Sayman posits, why not get your answers from many bots, all at the same time?

Over Zoom earlier this week, he explained to me how he thinks of generative AI like a smoothie if cups hadn’t yet been invented. You can still enjoy it from a bowl or plate, but those aren’t the right vessel. SocialAI, Sayman says, could be the cup.

Almost immediately Sayman laughed. “This is a terrible analogy,” he said.

Sayman is charming and clearly thinks a lot about how apps fit into our world. He’s a team of one right now, relying mostly on OpenAI’s technology to power SocialAI, blended with some other custom AI models. (Sayman rate-limits the app so that he doesn’t go broke in “three minutes” from the fees he’s paying to OpenAI. He also hasn’t quite yet figured out how he’ll make money off of SocialAI.) He knows he’s not the first to launch an AI-character app; Meta has burdened its apps with AI characters, and the Character AI app, which was just quasi-acquired by Google, lets you interact with a huge number of AI personas.But Sayman is hand-wavy about this competition. “I don’t see my app as, you’re going to be interacting with characters who you think might be real,” he says. “This is really for seeking answers to conflict resolution, or figuring out if what you’re trying to say is hurtful and get feedback before you post it somewhere else.”

“Someone joked to me that they thought Elon Musk should use this, so he could test all of his posts before he posts them on X,” Sayman said.

I’d actually tried that, tossing some of the most trafficked tweets from Elon Musk and the Twitter icon Dril into my SocialAI feed. I shared a news story from WIRED; the link was unclickable, because SocialAI doesn’t support link-sharing. (There’s no one to share it with, anyway.) I repurposed the viral “Bean Dad” tweet and purported to be a Bean Mom on SocialAI, urging my 9-year-old daughter to open a can of beans herself as a life lesson. I posted political content. I asked my synthetic SocialAI followers who else I should follow.

The bots obliged and flooded my feed with comments, like Reply Guys on steroids. But their responses lacked nutrients or human messiness. Mostly, I told Sayman, it all felt too uncanny, that I had a hard time crossing that chasm and placing value or meaning on what the bots had to say.

Sayman encouraged me to craft more posts along the lines of Reddit’s “Am I the Asshole” posts: Am I wrong in this situation? Should I apologize to a friend? Should I stay mad at my family forever? This, Sayman says, is the real purpose of SocialAI. I tried it. For a second the SocialAI bot comments lit up my lizard brain, my id and superego, the “I’m so right” instinct. Then Trollita Kafka told me, essentially, that I was in fact the asshole.One aspect of SocialAI that clearly does not represent the dawn of a new era: Sayman has put out a minimum viable product without communicating important guidelines around privacy, content policies, or how SocialAI or OpenAI might use the data people provide along the way. (Move fast, break things, etc.) He says he’s not using anyone’s posts to train his own AI models, but notes that users are still subject to OpenAI’s data-training terms, since he uses OpenAI’s API. You also can’t mute or block a bot that has gone off the rails.

At least, though, your feed is always private by default. You don’t have any “real” followers. My editor at WIRED, for example, could join SocialAI himself but will never be able to follow me or see that I copied and pasted an Elon Musk tweet about wanting to buy Coca-Cola and put the cocaine back in it, just as he could not follow my ChatGPT account and see what I’m enquiring about there.

As a human on SocialAI, you will never interact with another human. That’s the whole point. It’s your own little world with your own army of AI characters ready to bolster you or tear you down. You may not like it, but it might be where you’re headed anyway. You might already be there.

Source: https://www.wired.com/story/socialai-app-ai-chatbots-chatgpt/

OpenAI Announces a New AI Model, Code-Named Strawberry Step – ChatGPT o1

The ChatGPT maker reveals details of what’s officially known as OpenAI o1, which shows that AI needs more

OpenAI made the last big breakthrough in artificial intelligence by increasing the size of its models to dizzying proportions, when it introduced GPT-4 last year. The company today announced a new advance that signals a shift in approach—a model that can “reason” logically through many difficult problems and is significantly smarter than existing AI without a major scale-up.

The new model, dubbed OpenAI o1, can solve problems that stump existing AI models, including OpenAI’s most powerful existing model, GPT-4o. Rather than summon up an answer in one step, as a large language model normally does, it reasons through the problem, effectively thinking out loud as a person might, before arriving at the right result.

“This is what we consider the new paradigm in these models,” Mira Murati, OpenAI’s chief technology officer, tells WIRED. “It is much better at tackling very complex reasoning tasks.”

The new model was code-named Strawberry within OpenAI, and it is not a successor to GPT-4o but rather a complement to it, the company says.

Murati says that OpenAI is currently building its next master model, GPT-5, which will be considerably larger than its predecessor. But while the company still believes that scale will help wring new abilities out of AI, GPT-5 is likely to also include the reasoning technology introduced today. “There are two paradigms,” Murati says. “The scaling paradigm and this new paradigm. We expect that we will bring them together.”

LLMs typically conjure their answers from huge neural networks fed vast quantities of training data. They can exhibit remarkable linguistic and logical abilities, but traditionally struggle with surprisingly simple problems such as rudimentary math questions that involve reasoning.

Murati says OpenAI o1 uses reinforcement learning, which involves giving a model positive feedback when it gets answers right and negative feedback when it does not, in order to improve its reasoning process. “The model sharpens its thinking and fine tunes the strategies that it uses to get to the answer,” she says. Reinforcement learning has enabled computers to play games with superhuman skill and do useful tasks like designing computer chips. The technique is also a key ingredient for turning an LLM into a useful and well-behaved chatbot.

Mark Chen, vice president of research at OpenAI, demonstrated the new model to WIRED, using it to solve several problems that its prior model, GPT-4o, cannot. These included an advanced chemistry question and the following mind-bending mathematical puzzle: “A princess is as old as the prince will be when the princess is twice as old as the prince was when the princess’s age was half the sum of their present age. What is the age of the prince and princess?” (The correct answer is that the prince is 30, and the princess is 40).

“The [new] model is learning to think for itself, rather than kind of trying to imitate the way humans would think,” as a conventional LLM does, Chen says.

OpenAI says its new model performs markedly better on a number of problem sets, including ones focused on coding, math, physics, biology, and chemistry. On the American Invitational Mathematics Examination (AIME), a test for math students, GPT-4o solved on average 12 percent of the problems while o1 got 83 percent right, according to the company.

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The new model is slower than GPT-4o, and OpenAI says it does not always perform better—in part because, unlike GPT-4o, it cannot search the web and it is not multimodal, meaning it cannot parse images or audio.

Improving the reasoning capabilities of LLMs has been a hot topic in research circles for some time. Indeed, rivals are pursuing similar research lines. In July, Google announced AlphaProof, a project that combines language models with reinforcement learning for solving difficult math problems.

AlphaProof was able to learn how to reason over math problems by looking at correct answers. A key challenge with broadening this kind of learning is that there are not correct answers for everything a model might encounter. Chen says OpenAI has succeeded in building a reasoning system that is much more general. “I do think we have made some breakthroughs there; I think it is part of our edge,” Chen says. “It’s actually fairly good at reasoning across all domains.”

Noah Goodman, a professor at Stanford who has published work on improving the reasoning abilities of LLMs, says the key to more generalized training may involve using a “carefully prompted language model and handcrafted data” for training. He adds that being able to consistently trade the speed of results for greater accuracy would be a “nice advance.”

Yoon Kim, an assistant professor at MIT, says how LLMs solve problems currently remains somewhat mysterious, and even if they perform step-by-step reasoning there may be key differences from human intelligence. This could be crucial as the technology becomes more widely used. “These are systems that would be potentially making decisions that affect many, many people,” he says. “The larger question is, do we need to be confident about how a computational model is arriving at the decisions?”

The technique introduced by OpenAI today also may help ensure that AI models behave well. Murati says the new model has shown itself to be better at avoiding producing unpleasant or potentially harmful output by reasoning about the outcome of its actions. “If you think about teaching children, they learn much better to align to certain norms, behaviors, and values once they can reason about why they’re doing a certain thing,” she says.

Oren Etzioni, a professor emeritus at the University of Washington and a prominent AI expert, says it’s “essential to enable LLMs to engage in multi-step problem solving, use tools, and solve complex problems.” He adds, “Pure scale up will not deliver this.” Etzioni says, however, that there are further challenges ahead. “Even if reasoning were solved, we would still have the challenge of hallucination and factuality.”

OpenAI’s Chen says that the new reasoning approach developed by the company shows that advancing AI need not cost ungodly amounts of compute power. “One of the exciting things about the paradigm is we believe that it’ll allow us to ship intelligence cheaper,” he says, “and I think that really is the core mission of our company.”

Source: https://www.wired.com/story/openai-o1-strawberry-problem-reasoning/

The Catch of Temu in Europe – July 2024

The Catch of Temu in Europe

Temu, the Chinese e-commerce platform, offers products at remarkably low prices, which raises concerns about its business practices. One significant issue is the undervaluation of parcels entering the EU. Estimates suggest that around 65% of parcels are deliberately undervalued in customs declarations to avoid tariffs, which undermines local businesses and creates an uneven playing field [1]. Additionally, Temu employs a direct-to-consumer model, sourcing products directly from manufacturers in China, allowing them to benefit from bulk discounts and reduced shipping costs [2].

Benefits for the Chinese State

The low pricing strategy of Temu serves multiple purposes for the Chinese state. Firstly, it helps expand China’s influence in global e-commerce by increasing the market share of Chinese companies abroad. This can lead to greater economic ties and dependency on Chinese goods. Secondly, by facilitating the export of low-cost products, Temu contributes to the Chinese economy by boosting manufacturing and logistics sectors. Lastly, the data collected from users can be leveraged for insights into consumer behavior, which may benefit Chinese businesses and potentially the state itself in terms of economic planning and strategy [1].

Overall, while Temu’s low prices attract consumers, they also raise significant regulatory and ethical concerns in Europe, prompting scrutiny from authorities regarding compliance with local laws and standards.

Deeper Analysis of Future Benefits for the Chinese State

Temu’s aggressive pricing strategy in Europe not only serves immediate commercial interests but also aligns with broader strategic goals of the Chinese state. Here are several potential future benefits for China:

  1. Economic Expansion and Market Penetration:
    By establishing a strong foothold in European markets through low prices, Temu can facilitate the expansion of Chinese goods into new territories. This not only increases sales volume but also enhances brand recognition and loyalty among European consumers. As more consumers become accustomed to purchasing Chinese products, it could lead to a long-term shift in buying habits, favoring Chinese brands over local alternatives.
  2. Strengthening Supply Chains:
    Temu’s model emphasizes direct sourcing from manufacturers, which can help streamline supply chains. This efficiency can be replicated across various sectors, allowing China to become a dominant player in global supply chains. By controlling more aspects of production and distribution, China can mitigate risks associated with international trade tensions and disruptions, ensuring a more resilient economic structure.
  3. Data Collection and Consumer Insights:
    The platform’s operations will generate vast amounts of consumer data, which can be analyzed to gain insights into European consumer behavior. This data can inform not only marketing strategies but also product development, allowing Chinese manufacturers to tailor their offerings to meet the specific preferences of European consumers. Such insights can enhance competitiveness and drive innovation within Chinese industries.
  4. Geopolitical Influence:
    By increasing its economic presence in Europe, China can leverage its commercial relationships to enhance its geopolitical influence. Economic ties often translate into political goodwill, which can be beneficial in negotiations on various fronts, including trade agreements and international policies. This strategy aligns with China’s broader goal of expanding its influence globally, as outlined in its recent political resolutions emphasizing the importance of state power and common prosperity.
  5. Promotion of Technological Advancements:
    As Temu grows, it may invest in technology to improve logistics, customer service, and user experience. This could lead to advancements in e-commerce technologies that can be exported back to China, enhancing domestic capabilities. Moreover, the emphasis on technology aligns with China’s ambitions to become a leader in areas such as artificial intelligence and data analytics, as highlighted in its national strategies.
  6. Cultural Exchange and Soft Power:
    By making Chinese products more accessible and appealing to European consumers, Temu can facilitate a form of cultural exchange. As consumers engage with Chinese brands, they may also become more receptive to Chinese culture and values, enhancing China’s soft power. This cultural integration can help counter negative perceptions and foster a more favorable view of China in the long term.

In conclusion, Temu’s low pricing strategy is not merely a tactic for market entry; it is a multifaceted approach that can yield significant long-term benefits for the Chinese state. By enhancing economic ties, gathering valuable consumer data, and promoting technological advancements, China positions itself to strengthen its global influence and economic resilience in an increasingly competitive landscape.

Why Elon Musk should consider integrating OpenAI’s ChatGPT „GPT-4o“ as the operating system for a brand new Tesla SUV – Here are the five biggest advantages to highlight

  1. Revolutionary User Interface and Experience:
    • Natural Language Interaction: GPT-4o’s advanced natural language processing capabilities allow for seamless, conversational interaction between the driver and the vehicle. This makes controlling the vehicle and accessing information more intuitive and user-friendly.
    • Personalized Experience: The AI can learn from individual driver behaviors and preferences, offering tailored suggestions for routes, entertainment, climate settings, and more, enhancing overall user satisfaction and engagement. 
  2. Enhanced Autonomous Driving and Safety:
    • Superior Decision-Making: GPT-4o can significantly enhance Tesla’s autonomous driving capabilities by processing and analyzing vast amounts of real-time data to make better driving decisions. This improves the safety, reliability, and efficiency of the vehicle’s self-driving features.
    • Proactive Safety Features: The AI can provide real-time monitoring of the vehicle’s surroundings and driver behavior, offering proactive alerts and interventions to prevent accidents and ensure passenger safety.
  3. Next-Level Infotainment and Connectivity:
    • Smart Infotainment System: With GPT-4o, the SUV’s infotainment system can offer highly intelligent and personalized content recommendations, including music, podcasts, audiobooks, and more, making long journeys more enjoyable.
    • Seamless Connectivity: The AI can integrate with a wide range of apps and services, enabling drivers to manage their schedules, communicate, and access information without distraction, thus enhancing productivity and convenience.
  4. Continuous Improvement and Future-Proofing:
    • Self-Learning Capabilities: GPT-4o continuously learns and adapts from user interactions and external data, ensuring that the vehicle’s performance and features improve over time. This results in an ever-evolving user experience that keeps getting better.
    • Over-the-Air Updates: Regular over-the-air updates from OpenAI ensure that the SUV remains at the forefront of technology, with the latest features, security enhancements, and improvements being seamlessly integrated.
  5. Market Differentiation and Brand Leadership:
    • Innovative Edge: Integrating GPT-4o positions Tesla’s new SUV as a cutting-edge vehicle, showcasing the latest in AI and automotive technology. This differentiates Tesla from competitors and strengthens its reputation as a leader in innovation.
    • Enhanced Customer Engagement: The unique AI-driven features and personalized experiences can drive stronger customer engagement and loyalty, attracting tech-savvy consumers and enhancing the overall brand image.

By leveraging these advantages, Tesla can create a groundbreaking SUV that not only meets but exceeds consumer expectations, setting new standards for the automotive industry and reinforcing Tesla’s position as a pioneer in automotive and AI technology.

Real World Use Cases for Apples Vision Pro + Version 2 – with the new operating system ChatGPT „GPT-4o“

The integration of advanced AI like OpenAI’s GPT-4o into Apple’s Vision Pro + Version 2 can significantly enhance its vision understanding capabilities.
Here are ten possible use cases:

1. Augmented Reality (AR) Applications:
– Interactive AR Experiences: Enhance AR applications by providing real-time object recognition and interaction. For example, users can point the device at a historical landmark and receive detailed information and interactive visuals about it.
– AR Navigation: Offer real-time navigation assistance in complex environments like malls or airports, overlaying directions onto the user’s view.

2. Enhanced Photography and Videography:
– Intelligent Scene Recognition: Automatically adjust camera settings based on the scene being captured, such as landscapes, portraits, or low-light environments, ensuring optimal photo and video quality.
– Content Creation Assistance: Provide suggestions and enhancements for capturing creative content, such as framing tips, real-time filters, and effects.

3. Healthcare and Medical Diagnosis:
– Medical Imaging Analysis: Assist in analyzing medical images (e.g., X-rays, MRIs) to identify potential issues, providing preliminary diagnostic support to healthcare professionals.
– Remote Health Monitoring: Enable remote health monitoring by analyzing visual data from wearable devices to track health metrics and detect anomalies.

4. Retail and Shopping:
– Virtual Try-Ons: Allow users to virtually try on clothing, accessories, or cosmetics using the device’s camera, enhancing the online shopping experience.
– Product Recognition: Identify products in stores and provide information, reviews, and price comparisons, helping users make informed purchasing decisions.

5. Security and Surveillance:
– Facial Recognition: Enhance security systems with facial recognition capabilities for authorized access and threat detection.
– Anomaly Detection: Monitor and analyze security footage to detect unusual activities or potential security threats in real-time.

6. Education and Training:
– Interactive Learning: Use vision understanding to create interactive educational experiences, such as identifying objects or animals in educational content and providing detailed explanations.
– Skill Training: Offer real-time feedback and guidance for skills training, such as in sports or technical tasks, by analyzing movements and techniques.

7. Accessibility and Assistive Technology:
– Object Recognition for the Visually Impaired: Help visually impaired users navigate their surroundings by identifying objects and providing auditory descriptions.
– Sign Language Recognition: Recognize and translate sign language in real-time, facilitating communication for hearing-impaired individuals.

8. Home Automation and Smart Living:
– Smart Home Integration: Recognize household items and provide control over smart home devices. For instance, identifying a lamp and allowing users to turn it on or off via voice commands.
– Activity Monitoring: Monitor and analyze daily activities to provide insights and recommendations for improving household efficiency and safety.

9. Automotive and Driver Assistance:
– Driver Monitoring: Monitor driver attentiveness and detect signs of drowsiness or distraction, providing alerts to enhance safety.
– Object Detection: Enhance autonomous driving systems with better object detection and classification, improving vehicle navigation and safety.

10. Environmental Monitoring:
– Wildlife Tracking: Use vision understanding to monitor and track wildlife in natural habitats for research and conservation efforts.
– Pollution Detection: Identify and analyze environmental pollutants or changes in landscapes, aiding in environmental protection and management.

These use cases demonstrate the broad potential of integrating advanced vision understanding capabilities into Apple’s Vision Pro + Version 2, enhancing its functionality across various domains and providing significant value to users.

After ruining Android messaging, Google says iMessage is too powerful

Google failed to compete with iMessage for years. Now it wants Apple to play nice.

Source: https://arstechnica.com/gadgets/2022/01/after-ruining-android-messaging-google-says-imessage-is-too-powerful/

Google took to Twitter this weekend to complain that iMessage is just too darn influential with today’s kids. The company was responding to a Wall Street Journal report detailing the lock-in and social pressure Apple’s walled garden is creating among US teens. iMessage brands texts from iPhone users with a blue background and gives them additional features, while texts from Android phones are shown in green and only have the base SMS feature set. According to the article, „Teens and college students said they dread the ostracism that comes with a green text. The social pressure is palpable, with some reporting being ostracized or singled out after switching away from iPhones.“ Google feels this is a problem.

„iMessage should not benefit from bullying,“ the official Android Twitter account wrote. „Texting should bring us together, and the solution exists. Let’s fix this as one industry.“ Google SVP Hiroshi Lockheimer chimed in, too, saying, „Apple’s iMessage lock-in is a documented strategy. Using peer pressure and bullying as a way to sell products is disingenuous for a company that has humanity and equity as a core part of its marketing. The standards exist today to fix this.“

The „solution“ Google is pushing here is RCS, or Rich Communication Services, a GSMA standard from 2008 that has slowly gained traction as an upgrade to SMS. RCS adds typing indicators, user presence, and better image sharing to carrier messaging. It is a 14-year-old carrier standard, though, so it lacks many of the features you would want from a modern messaging service, like end-to-end encryption and support for non-phone devices. Google tries to band-aid over the aging standard with its „Google Messaging“ client, but the result is a lot of clunky solutions that don’t add up to a good modern messaging service.

Since RCS replaces SMS, Google has been on a campaign to get the industry to make the upgrade. After years of protesting, the US carriers are all onboard, and there is some uptake among the international carriers, too. The biggest holdout is Apple, which only supports SMS through iMessage.

Apple's green-versus-blue bubble explainer from its website.
Enlarge / Apple’s green-versus-blue bubble explainer from its website.
Apple

Apple hasn’t ever publicly shot down the idea of adding RCS to iMessage, but thanks to documents revealed in the Epic v. Apple case, we know the company views iMessage lock-in as a valuable weapon. Bringing RCS to iMessage and making communication easier with Android users would only help to weaken Apple’s walled garden, and the company has said it doesn’t want that.

In the US, iPhones are more popular with young adults than ever. As The Wall Street Journal notes, „Among US consumers, 40% use iPhones, but among those aged 18 to 24, more than 70% are iPhone users.“ It credits Apple’s lock-in with apps like iMessage for this success.

Reaping what you sow

Google clearly views iMessage’s popularity as a problem, and the company is hoping this public-shaming campaign will get Apple to change its mind on RCS. But Google giving other companies advice on a messaging strategy is a laughable idea since Google probably has the least credibility of any tech company when it comes to messaging services. If the company really wants to do something about iMessage, it should try competing with it.

As we recently detailed in a 25,000-word article, Google’s messaging history is one of constant product startups and shutdowns. Thanks to a lack of product focus or any kind of top-down mandate from Google’s CEO, no division is really „in charge“ of messaging. As a consequence, the company has released 13 half-hearted messaging products since iMessage launched in 2011. If Google wants to look to someone to blame for iMessage’s dominance, it should start with itself, since it has continually sabotaged and abandoned its own plans to make an iMessage competitor.

 

Messaging is important, and even if it isn’t directly monetizable, a dominant messaging app has real, tangible benefits for an ecosystem. The rest of the industry understood this years ago. Facebook paid $22 billion to buy WhatsApp in 2014 and took the app from 450 million users to 2 billion users. Along with Facebook Messenger, Facebook has two dominant messaging platforms today, especially internationally. Salesforce paid $27 billion for Slack in 2020, and Tencent’s WeChat, a Chinese messaging app, is pulling in 1.2 billion users and yearly revenues of $5.5 billion. Snapchat is up to a $67 billion market cap, and Telegram is getting $40 billion valuations from investors. Google keeps trying ideas in this market, but it never makes an investment that is anywhere close to the competition.
 
 

Google once had a functional competitor to iMessage called Google Hangouts. Circa 2015, Hangouts was a messaging powerhouse; in addition to the native Hangouts messaging, it also supported SMS and Google Voice messages. Hangouts did group video calls five years before Zoom blew up, and it had clients on Android, iOS, the web, Gmail, and every desktop OS via a Chrome extension.

As usual, though, Google lacked any kind of long-term plan or ability to commit to a single messaging strategy, and Hangouts only survived as the „everything“ messenger for a single year. By 2016, Google moved on to the next shiny messaging app and left Hangouts to rot.

Even if Google could magically roll out RCS everywhere, it’s a poor standard to build a messaging platform on because it is dependent on a carrier phone bill. It’s anti-Internet and can’t natively work on webpages, PCs, smartwatches, and tablets, because those things don’t have SIM cards. The carriers designed RCS, so RCS puts your carrier bill at the center of your online identity, even when free identification methods like email exist and work on more devices. Google is just promoting carrier lock-in as a solution to Apple lock-in.

Despite Google’s complaining about iMessage, the company seems to have learned nothing from its years of messaging failure. Today, Google messaging is the worst and most fragmented it has ever been. As of press time, the company runs eight separate messaging platforms, none of which talk to each other: there is Google Messages/RCS, which is being promoted today, but there’s also Google Chat/Hangouts, Google Voice, Google Photos Messages, Google Pay Messages, Google Maps Business Messages, Google Stadia Messages, and Google Assistant Messaging. Those last couple of apps aren’t primarily messaging apps but have all ended up rolling their own siloed messaging platform because no dominant Google system exists for them to plug into.

The situation is an incredible mess, and no single Google product is as good as Hangouts was in 2015. So while Google goes backward, it has resorted to asking other tech companies to please play nice with it while it continues to fumble through an incoherent messaging strategy.