Bien plus discret sur cette édition de l’IFA, Samsung n’a pas pour autant disparu. Mais le géant coréen a changé de stratégie. Pour la première fois depuis 1991, il n’occupe plus son emplacement historique dans le hall principal de la Messe Berlin. À la place, Xiaomi est arrivé en force !
From the Flip the Script event in San Francisco, Dmytro Melnyk, Chief Product Officer for MacPaw, discusses his role and the importance of the company’s careful approach to adding AI across products, from both the developer and customer viewpoints.. He explains why some users want frictionless AI while others resist it over privacy, cost, and complexity, and outlines Eney's goal of combining many Mac utility tasks into one assistant with a pay-as-you-go model.
At Flip The Script in San Francisco at WWDC, Vladyslav Hamolia, AI Staff Engineer for MacPaw.discusses the company’s push to become AI-first by rethinking existing products, building internal tools, and supporting third-party vendors. He profiles Eney, the MacPaw AI assistant (available in Setapp) designed to perform tasks, optimize workflows, and improve daily routines. It addresses security while balancing local-first privacy goals with hybrid cloud models.
Google Assistant's days have been numbered for a while, but the company has now settled on a date to retire its pre-AI assistant robot. In an email being sent out to users, Google confirms that Assistant on Android devices will be shut down starting on September 4, forcing everyone over to Gemini. Google told us it was coming, but that doesn't make it hurt any less.
The initial plan was to retire Assistant in late 2025, but the service got a brief reprieve as Google shored up some aspects of Gemini. Now that Gemini is ready to take over, Google will begin ending support for the old system on September 4, but the email notes that it may take several weeks for the process to complete. So those stubbornly clinging to Assistant may still be able to use it a bit longer before the option disappears. For those who made the switch already, nothing will change.
By ending Assistant on mobile devices, Google is also killing it on myriad other gadgets that connect to your phone for smart features. So smartwatches, headphones, and cars with Android Auto are also being forcibly moved to Gemini. Most of Google's smart home devices have already transitioned to Gemini.
OpenAI family of devices plans are no longer limited to an experimental gadget hidden inside a design studio. OpenAI President Greg Brockman has confirmed that the company’s first consumer device will begin a broader series intended to reconsider how people interact with artificial intelligence.
The confirmation gives OpenAI’s hardware strategy a clearer shape without revealing the first product itself. Brockman said the company is not ready to discuss the device in detail, but described it as the first in a series that will rethink what devices should be when AI can understand language, observe context and complete tasks.
That ambition extends well beyond placing the ChatGPT app on another piece of hardware. OpenAI wants to reduce the menus, buttons, model selectors and separate applications that currently stand between a person and an AI assistant.
For Apple, Google and other established platform owners, the implication is significant. ChatGPT currently reaches consumers through devices and operating systems controlled by other companies. An OpenAI-designed product would give the company direct control over the microphones, cameras, sensors, interface and account experience through which its models interact with people. 56000
OpenAI Family of Devices Begins With io
OpenAI’s move into consumer hardware accelerated in 2025 when it acquired io Products in a transaction valued at approximately $6.5 billion.
The startup had been founded by former Apple design chief Jony Ive alongside Scott Cannon, Evans Hankey and Tang Tan. Its team included hardware engineers, designers, manufacturing specialists and product developers, many of whom had spent years working together at Apple.
OpenAI and Ive had already been collaborating before the acquisition. Their early ideas developed into physical designs, convincing the partners that they needed a dedicated company capable of engineering and manufacturing complete products rather than another software prototype.
The io team officially merged with OpenAI in July 2025. Ive and his design collective LoveFrom remained independent while assuming extensive design and creative responsibilities across OpenAI.
The structure combines OpenAI’s models and software research with a group experienced in turning new interface concepts into mass-produced consumer products. Ive’s previous work helped shape the iMac, iPod, iPhone, iPad, Apple Watch and other devices that established Apple’s modern design identity.
OpenAI has continued recruiting hardware expertise from Apple. Paul Meade, who led hardware development for Apple Vision Pro and worked on the company’s smart-glasses efforts, reportedly joined OpenAI’s hardware organization in 2026.
OpenAI also placed product-development veteran Peter Welinder in a leadership role connected to the io division. The resulting organization is not structured like a small accessory project. It is being assembled to develop products, create manufacturing relationships and support more than one form factor.
Image Credit: Google
The First Device Remains Unannounced
OpenAI has not publicly confirmed the name, appearance, price or specifications of its first device. It has also not presented the product as a smartphone, pair of glasses, wearable pin or conventional smart speaker.
Recent reporting describes a portable, screen-free device designed primarily for use inside the home. The reported product resembles a compact speaker but includes a rechargeable battery, allowing it to move between rooms rather than remain attached to one location.
A camera and additional sensors would help the device understand its surroundings. Mechanical elements could allow parts of it to move, creating a more expressive physical presence than a stationary speaker waiting for a wake word.
The reported design would rely heavily on OpenAI’s latest voice technology. Instead of requiring a person to wait for the assistant to finish speaking, the system could listen and respond more naturally during overlapping conversation. Access to personal information, including messages or email when authorized, could give it enough context to handle requests rather than merely answer questions.
These details remain reported plans rather than announced specifications. OpenAI could change the hardware, features or release schedule before introducing the product.
The company is reportedly targeting a presentation during 2026 and a commercial release in 2027. Fortune separately reported that the initial design has been finalized, although development of the larger product family continues.
Why OpenAI Wants Hardware of Its Own
ChatGPT already operates across iPhone, iPad, Mac, Android and Windows devices. That wide distribution gives OpenAI access to an enormous audience without requiring it to manufacture screens, batteries or processors.
It also leaves crucial parts of the experience under the control of Apple, Google and Microsoft.
An operating system determines when ChatGPT can use a microphone, whether it can run continuously, how notifications appear and which applications it can control. Platform owners can limit background access, change default-assistant rules or promote their own AI systems more prominently.
Those restrictions matter more as ChatGPT develops from a chatbot into an agent. Answering a question inside an app requires relatively little control over the device. Completing a task across messages, calendars, files, cameras and connected appliances requires deeper access.
OpenAI hardware could be designed around those capabilities from the beginning. The assistant would not need to behave like one application among dozens because it could become the primary interface.
Brockman has described the current ChatGPT interface as too complicated, particularly as new models and tools add more controls. OpenAI’s objective is to make much of that interface disappear.
A successful device could allow someone to describe an outcome instead of opening several applications and completing each step manually. The user might ask the assistant to reorganize a day after a delayed flight, contact the relevant people and adjust reservations. The interface would be the request and the resulting confirmation rather than a sequence of taps.
A Family Creates More Context Than One Gadget
Calling the project a family of devices changes the strategy. One experimental product would need to prove its value independently. A coordinated group could distribute different sensors and functions across the places where people already live and work.
A home device could provide shared access in kitchens and living spaces. A personal wearable could preserve private conversations while moving outside. A future mobile product could handle visual information, navigation and communication without reproducing the application grid of a smartphone.
OpenAI is reportedly developing approximately five hardware concepts, with the screen-free home companion receiving priority. The company has also explored wearables and home robotics, although none of those later products has been announced.
The devices would become more useful if they shared the same ChatGPT identity, memory controls and task history. A request started at home could continue through another product without requiring the user to repeat the people involved, the desired result or the decisions already made.
That continuity is one of the strongest advantages Apple currently receives from controlling a complete ecosystem. An iPhone, Mac, Apple Watch, AirPods and HomePod can exchange information because Apple designs the devices, operating systems and cloud services together.
OpenAI appears to be pursuing a different center of gravity. Instead of making the operating system coordinate applications, it could make the AI assistant coordinate specialized devices.
Image Credit: Dado Ruvic/Reuters
ChatGPT Will Also Expand Through Other Companies’ Hardware
OpenAI’s hardware work does not mean the company intends to reserve ChatGPT for its own products.
Brockman has said OpenAI is interested in deeper integrations with smart glasses, wearables and other devices through its application programming interfaces. The company wants ChatGPT to become an interface available across hardware categories, including products manufactured by third parties.
That creates a two-track strategy. OpenAI can use its own devices to demonstrate the experience it considers ideal while allowing other manufacturers to connect their cameras, microphones and sensors to ChatGPT.
Third-party distribution would help OpenAI reach consumers who do not want another dedicated device. It would also reduce the risk of depending entirely on the success of one hardware launch.
The difficult part will be keeping those experiences consistent. A wearable may have limited battery capacity, a camera may require visible privacy controls, and a speaker may depend on cloud processing when a connection is available. OpenAI will need to define what its assistant can do reliably across products with very different technical limits.
Early AI Hardware Changed the Expectations
The first generation of dedicated AI gadgets established a warning for every company entering this market.
Humane’s AI Pin and the Rabbit R1 attracted attention by promising an alternative to application-based computing. Their launches also exposed problems involving response delays, battery life, incomplete features and tasks that remained easier on a smartphone.
Those products demonstrated that access to a language model does not automatically justify a new object. Consumers need to understand why they should carry, charge and maintain another device when ChatGPT, Gemini and other assistants already operate on phones.
OpenAI has several advantages those earlier companies lacked. It controls one of the most widely used AI services, operates the models behind the interface and has the resources to improve the software after launch. The io acquisition gives it experienced design and manufacturing leadership rather than requiring it to assemble a hardware organization gradually.
The standard will consequently be higher. A well-designed enclosure and natural voice will not be enough if the device cannot complete dependable tasks more efficiently than an iPhone or Android phone.
Apple’s Lawsuit Could Affect the Schedule
OpenAI’s expansion beyond Apple devices has also produced a direct legal confrontation with Apple.
Apple filed a federal lawsuit in July 2026 accusing OpenAI, io and former Apple employees of misappropriating confidential hardware information. The complaint alleges that trade secrets connected to unreleased products, engineering processes and suppliers were taken for the benefit of OpenAI’s hardware business.
OpenAI has denied having any interest in Apple’s trade secrets and said it was unaware of evidence giving the complaint merit. The allegations have not been decided by a court.
Apple is seeking restrictions on the use of its confidential information, along with the return of materials and financial damages. The case could require OpenAI to separate employees from parts of the project, replace suppliers or repeat engineering work if disputed information is found inside the development process.
It does not automatically establish that OpenAI’s reported product copies an Apple device. The first OpenAI design is described as substantially different from Apple’s current hardware, but the legal dispute could still affect when it can be manufactured or sold.
The First Product Will Define the Rules for the Rest
The most important details at OpenAI’s eventual hardware presentation may have little to do with the object’s shape.
A camera-equipped device that learns from activity inside a home will need unmistakable recording indicators, understandable data-retention controls and a simple method for temporarily disabling its sensors. Owners will need to know which information remains on the device, what reaches OpenAI’s servers and which memories can be reviewed or deleted.
OpenAI will also have to explain whether the product requires a paid ChatGPT subscription, how much it can do without an internet connection and whether multiple members of a household receive separate identities. Those choices will determine whether it behaves like a personal assistant, a shared appliance or a new subscription terminal.
Support for messages, calendars, music and connected appliances will require cooperation from outside companies. A device cannot replace frequent phone interactions if it remains unable to enter the services where those interactions occur.
The first model will therefore establish more than OpenAI’s industrial-design language. It will set the privacy, account, subscription and developer rules that every later member of the OpenAI family of devices inherits.
Siri Wolfram Alpha integration helped create one of the defining moments in the early history of consumer artificial intelligence. When Apple introduced Siri with iPhone 4S in October 2011, users were not merely given another form of voice control. They could ask a spoken question and receive a calculated answer rather than a page of search results.
Questions involving mathematics, measurements, dates, nutrition, astronomy and factual comparisons could be routed to Wolfram Alpha, a computational knowledge engine launched in 2009. Siri handled the conversation, while Wolfram Alpha supplied structured information and calculations behind many of the responses.
The experience could feel remarkably advanced for its time. Asking how many days remained before a holiday, comparing the populations of two cities or converting miles into kilometers no longer required opening Safari, entering a query and selecting a website.
That direct path from natural language to a useful answer established an expectation that continues shaping AI products today.
Its technological roots extend to research conducted at SRI International, including work associated with the DARPA-funded CALO project. The objective was to develop systems capable of learning, organizing information and helping people complete everyday tasks.
SRI later spun the technology into Siri Inc., founded by a team including Dag Kittlaus, Adam Cheyer and Tom Gruber. The company released a standalone Siri app for iPhone in early 2010.
The original product was designed around actions rather than ordinary web search. A user could ask for a restaurant reservation, request a taxi, check entertainment listings or search for local services. Siri attempted to understand the intent and connect the request with an appropriate online provider.
This approach was ambitious because the assistant needed to coordinate several different systems. Speech had to be converted into text, the request had to be interpreted, context had to be considered and an external service had to return something useful.
Apple acquired Siri in April 2010. Steve Jobs had personally recognized the potential of moving beyond a collection of apps toward a conversational layer capable of reaching them on behalf of the user.
When the assistant returned as an integrated feature of iPhone 4S, Apple had narrowed some aspects of the original startup vision while making the technology accessible to a much larger audience.
Siri could send messages, create reminders, place calls, manage calendar events, check weather conditions and answer questions. Apple initially labeled the feature as a beta, an unusual decision for a prominent part of a new iPhone launch.
The beta label acknowledged that natural-language interaction remained unpredictable. Accents, ambiguous phrasing, network conditions and external data providers could all influence whether a request succeeded.
Image Source: Google
Siri Wolfram Alpha Made Answers Feel Immediate
Siri Wolfram Alpha became especially memorable because it showed the difference between retrieving information and computing an answer.
A conventional search engine could locate pages containing a formula. Wolfram Alpha could apply that formula to the values supplied by the user. It could interpret units, compare quantities, calculate probabilities and organize factual data into a structured response.
That distinction allowed Siri to appear more knowledgeable than a voice interface simply reading search results aloud.
A request such as “What is 15% of $86?” could return the calculation directly. A question about the distance between planets could produce a value without requiring the user to inspect several websites. Wolfram Alpha could also answer unusually specific requests, including information about aircraft flying overhead when the necessary location and aviation data were available.
The system was not a large language model. It did not generate open-ended prose in the way current AI assistants do. Wolfram Alpha relied on curated data, symbolic computation, algorithms and formal representations of knowledge.
That structure provided an advantage that remains relevant: many answers could be calculated and traced through defined information rather than improvised statistically.
Early Siri combined several specialized capabilities instead of depending on one system to do everything. One provider might supply restaurant information, another could handle maps and Wolfram Alpha could process computational questions.
The assistant acted as the conversational coordinator.
This architecture made Siri seem like one intelligence even though the response could come from a network of separate services. Modern AI platforms have returned to a similar idea through tools, agents and external data connections. A language model may manage the conversation while calling a calculator, calendar, search engine or business system when a task requires reliable external action.
Siri demonstrated that model on a consumer smartphone more than a decade earlier.
A Small Integration Created a Large Expectation
The Wolfram Alpha partnership influenced how people began judging digital assistants.
Before Siri, voice features on phones were commonly associated with rigid commands such as calling a contact or selecting music. Siri invited users to speak more naturally and attempt questions that would previously have been typed into a browser.
The results were inconsistent, but successful interactions changed perceptions immediately. The iPhone appeared capable of understanding intent, reaching into a knowledge system and returning a concise answer through one conversational interface.
That apparent simplicity concealed substantial infrastructure. Speech recognition, language interpretation, location, personal context and external databases all had to work together quickly enough to preserve the feeling of a conversation.
Wolfram Alpha also gave Apple access to expertise that would have taken years to reproduce internally. Stephen Wolfram had spent decades building Mathematica and computational systems before launching the public knowledge engine.
Following the Siri announcement, Wolfram described the integration as a characteristically direct Steve Jobs decision: users wanted access to knowledge and actions without unnecessary intermediate steps.
That idea became more influential than any individual answer Siri provided.
The assistant did not need to show how many services had participated. It needed to understand the request and return the result.
Early Siri Also Revealed the Limits of the Model
The first version of Siri could feel futuristic one moment and frustrating the next.
Some questions reached Wolfram Alpha correctly, while similar wording could trigger a web search or an unrelated response. Siri occasionally struggled to determine whether the user wanted a calculation, a local result or an action inside the phone.
Wolfram Alpha responses were also better suited to some subjects than others. Mathematical and scientific questions matched its computational structure, while casual conversation, current events and subjective requests required different sources.
Network dependence created another weakness. Early Siri processing relied heavily on remote servers, so delays or outages could make a basic request fail even when the iPhone itself was functioning normally.
Those limitations foreshadowed the central challenge surrounding modern assistants. A convincing interface encourages people to ask almost anything, but no single technical system performs equally well across every type of request.
Current generative AI can produce more natural language and handle a wider range of topics. It can also create confident errors when factual grounding is weak. The computational approach represented by Wolfram Alpha remains valuable precisely because some questions require verified data and exact calculation rather than fluent speculation.
The story behind the first Siri steps is therefore not only about an old iPhone feature. It shows that a powerful assistant needs more than a conversational personality. It requires access to dependable tools, structured knowledge and the ability to recognize when another system should provide the answer.
Siri Wolfram Alpha offered that lesson in 2011, long before AI assistants became capable of writing essays, interpreting screens and acting across apps.
Some Mac users may encounter an unusual situation where a “Keyboard Setup Assistant” application opens at random on their Mac, even if they already connected and setup a keyboard to use with their computer. The Keyboard Setup Assistant might pop up completely randomly, when the computer is woken from sleep, when the computer is freshly ... Read More
Siri Wolfram helped define the original promise of Apple’s voice assistant. When Siri debuted on the iPhone 4S in 2011, it could answer factual and computational questions without sending users through a list of search results. Ask about a mathematical calculation, scientific quantity, historical statistic or unit conversion, and Wolfram Alpha could provide a structured answer.
That partnership gave early Siri something many modern AI assistants still struggle to guarantee: a source designed to compute answers rather than produce plausible sentences. Wolfram Alpha was not a chatbot. It was a computational knowledge engine built around curated data, symbolic mathematics and algorithms.
Siri AI enters a far more ambitious era. Apple now wants its assistant to hold natural conversations, understand personal context, interpret images, find information across apps and complete actions. Yet the old Siri Wolfram relationship remains useful for understanding what Apple gained, what was lost as assistants became more conversational and why verified computation may become even more valuable in the generative AI age.
Siri Wolfram Was Built Around Answers
The original Siri Wolfram integration reflected a straightforward idea: users wanted direct answers without manually searching, opening websites and comparing sources.
Wolfram Alpha was particularly effective for questions with a computable result. It could solve equations, compare financial data, convert measurements, retrieve astronomical information, calculate dates and present structured scientific facts. Siri provided the voice interface, while Wolfram supplied the knowledge engine behind selected responses.
A user could ask how many days remained until a date, calculate a percentage, compare populations or request information about a mathematical concept. Siri translated the spoken request and delivered the result in a format suitable for the iPhone.
The experience was narrow compared with modern generative AI, but it had a valuable discipline. Wolfram Alpha attempted to determine an answer from structured information. It did not need to improvise an explanation every time.
That made Siri Wolfram especially useful for mathematics, science, education and quick factual calculations. The assistant could sound intelligent because it was connected to a system built to compute.
Image Source: Google
What Siri Wolfram Alpha Could Do Well
Siri Wolfram Alpha worked best when the request had a definable structure. Arithmetic, percentages, conversions, equations and statistics were natural categories. The system could also retrieve information about weather history, geography, nutrition, astronomy, transportation and other data-rich subjects.
The integration helped make Siri feel different from ordinary web search. Instead of showing several links, the assistant could present one answer with supporting figures or charts.
That approach had limitations. Wolfram Alpha could only respond effectively when it understood the request and had the relevant data or computational method. It was not designed for open-ended conversation, personal writing, emotional nuance or the countless informal questions users ask assistants.
Siri also relied on several external services, with Wolfram Alpha handling only certain knowledge categories. The experience could therefore feel inconsistent. One request produced a polished computational response, while another returned a web search or a limited canned answer.
Still, the partnership demonstrated a principle Apple has never fully abandoned: the best assistant does not need to create every answer itself. It needs to route each request to the system most qualified to handle it.
Generative AI Changed the Definition of an Assistant
Modern AI assistants are expected to do far more than retrieve facts. Users want them to explain ideas, write messages, summarize documents, analyze images, brainstorm projects and remember the direction of a conversation.
Siri AI is Apple’s response to that expanded expectation. Powered by Apple Intelligence and Apple Foundation Models, the new assistant can handle open-ended requests and continue natural conversations. It can use personal context from supported apps, understand onscreen content and connect requests with app actions.
This is a dramatic expansion from the Siri Wolfram era. The old model was largely transactional: ask a question, receive an answer. Siri AI is intended to interpret intent, understand relationships between information and help complete a larger task.
A user may ask Siri AI to find a restaurant recommendation buried in Messages, locate a hotel confirmation in Mail and add the details to a travel note. That request involves personal context and several system services rather than one public factual answer.
The assistant becomes less like a talking search box and more like an operating-system layer.
Image Credit: Apple Inc.
Conversation Creates a Reliability Problem
Generative AI makes assistants more flexible, but it also introduces uncertainty. A language model can produce an elegant response even when the underlying information is incomplete or wrong. The same fluency that makes the assistant enjoyable can make an inaccurate answer sound convincing.
The old Siri Wolfram model avoided much of that risk within its strongest categories. A mathematical calculation came from a computational engine. A unit conversion followed a defined relationship. A data query could be tied to structured information.
Siri AI needs both capabilities. It needs conversational intelligence for requests that cannot be reduced to a formula, while retaining access to reliable tools for tasks that demand precision.
Apple can use specialized systems for calculations, calendars, maps, weather, app actions and personal information rather than asking one generative model to invent every result. Foundation models can understand the request, then direct the work to an appropriate source.
That structure would preserve the strongest lesson from Siri Wolfram: intelligence also means knowing when not to guess.
Personal Context Is the New Knowledge Engine
Wolfram Alpha gave Siri access to public computational knowledge. Siri AI adds a second knowledge source: the user’s own information.
Apple says Siri AI can understand personal context across messages, emails, photos, notes and other supported content. It can help retrieve a detail without requiring the user to remember which app contains it.
This changes the assistant’s value. Wolfram Alpha could explain the distance between two cities. Siri AI could find the hotel reservation, identify the arrival time and retrieve the message where a friend recommended a nearby restaurant.
That personal layer also requires stricter privacy. Apple uses on-device processing for many requests and Private Cloud Compute when more processing power is required. The company has designed the system so personal data does not become part of an ordinary cloud AI profile.
Siri Wolfram answered questions about the world. Siri AI is being designed to answer questions about the user’s place within it.
Image Credit: Apple Inc.
Apps Complete the Transition
Siri AI also moves beyond knowledge by connecting directly with apps through App Intents. Developers can expose content and actions so the assistant can help users complete tasks instead of only describing how to do them.
That could include editing a photo, retrieving a document, starting a workout, updating a task or sending information through a supported app. Onscreen awareness adds another route, allowing users to ask about content already visible without explaining every detail again.
Wolfram Alpha remains valuable because some questions require specialized computation. App Intents extend the same routing philosophy into actions. Siri does not need to contain every capability internally. It needs to understand the request and connect it with the correct tool.
This may become the defining structure of Siri AI: one conversational interface backed by models, apps, services, personal context and specialized knowledge engines.
The Old Siri Still Has a Lesson for the New One
Siri Wolfram belonged to a more limited assistant era, but its underlying approach was disciplined. The system used a specialized source for questions that demanded structured knowledge and computation.
Siri AI has a wider assignment. It must converse naturally, interpret images, understand personal information and complete actions across Apple devices. That breadth can make Siri far more useful, but only if Apple preserves dependable pathways for calculations and factual tasks.
The future assistant should not choose between Wolfram-style precision and generative flexibility. It should combine them. A conversational model can understand what the user wants. A computational engine can provide the numerical answer. Personal context can supply relevant details. App Intents can complete the task.
The old Siri Wolfram integration showed that a voice assistant could make expert knowledge accessible through one spoken request. Siri AI now aims to place conversation, personal context and action around that foundation.
The assistant has become larger, more personal and more capable. Its credibility will still depend on something the 2011 version understood well: when a question has a correct answer, intelligence should calculate before it speaks
AI coding agent startup Niteshift has raised a $7 million seed round from a who's who of angels. It's betting companies will want power over, not lock-in with model makers.
Après plusieurs années d’attente et de promesses, Apple revoit en profondeur son assistant vocal. Il est plus intelligent, plus conversationnel et désormais capable d’agir directement dans les applications. Mais est-ce que Siri AI est plus qu’un simple clone de Google ?
Segundo a Google, o Gemini Go é uma versão mais leve do assistente inteligente, concebida para os smartphones Android com especificações mais modestas. A substituição do Google Assistant nos modelos equipados com Android Go será disponibilizada de forma faseada.
In November 2023, we reported on dubious claims made by marketing firm Cox Media Group (CMG) Local Solutions. The company advertised a service called Active Listening on a website that said, “It’s true. Your devices are listening to you” and claimed it could use “voice data” to help advertisers target ads to specific people.
Naturally, panic ensued. 404 Media, which initially spotted the website, for instance, wrote that the idea of smartphones listening to people to sell products “may finally be a reality."
A screenshot taken in 2023 from a webpage that CMG has since removed.
The idea of a marketing firm using AI to “detect relevant conversations via smartphones, smart TVs, and other devices” in real time—according to a since-deleted CMG blog post from November 2023 (still viewable via the Internet Archive’s Wayback Machine)—has raised alarms.
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