Google unveils an “anything-to-anything” AI model

Google pushes multimodality to a new level

Google used its I/O conference to put back at center stage an idea that has been running through the entire industry for nearly two years: AI should no longer be conceived as a series of specialized models, each confined to one format, but as a single system capable of understanding, transforming, and generating almost any type of content. The phrase chosen by Google, relayed in particular by The Verge in its article Google’s new anything-to-anything AI model is wild, sums up that ambition in three words: “anything-to-anything”.

Behind the announcement effect, the strategic signal is clear. Google is no longer talking only about text to image, text to audio, or video to text. The company is highlighting a multimodal architecture in which an audio input can produce a video, an image can serve as the starting point for a spoken response, and a sketch, a text, a camera feed, or a sound clip can be combined within the same system. This shift is not cosmetic. It marks a break with the logic of separate building blocks that has dominated generative AI since the explosion of ChatGPT at the end of 2022.

The subject goes far beyond the demo effect. For Google, this direction responds to intense competitive pressure. OpenAI set the media pace with GPT-4o, its natively multimodal model presented in May 2024, capable of processing voice, image, and text in real time. Anthropic, for its part, consolidated its position in professional use cases with Claude 3 and then Claude 3.5, emphasizing reasoning quality, visual understanding, and safety. Meta, finally, chose another path: widely distributing its Llama models and investing in open multimodal systems in order to weigh on the ecosystem more than on the product interface alone.

In this context, Google had to do more than an incremental update. The company has considerable historical strengths: DeepMind, one of the most advanced research teams in the world; YouTube, a giant reservoir of video and audio data; Android, Chrome, and Search, which provide entry points to billions of users; and long experience with foundation models, from Transformer to PaLM, then Gemini. But for the past eighteen months, the question has no longer been only scientific. It has become industrial: who will control the universal interface between humans, content, and software agents?

The concept of “anything-to-anything” therefore takes its place in a broader battle around the universal model. The idea is not only to make several media types interact, but to have a system that handles different formats as expressions of the same internal representation. In other words, AI no longer learns laboriously to move from one silo to another; it treats text, speech, image, and video as compatible, convertible, combinable modalities. It is this promise that fascinates the industry, because it opens the way to assistants capable of seeing, listening, speaking, summarizing, editing, illustrating, coding, and acting.

The term also has symbolic significance. For a long time, multimodality was presented as an addition of skills: a language model to which vision is added, then voice, then video. With the approach now championed by Google, multimodality becomes the core of the system, not an extra. This brings generative AI closer to an ideal of a general-purpose machine, still far from general intelligence in the strong sense, but clearly closer to a unified computing environment. For creators, developers, businesses, and media organizations, this evolution changes the nature of possible uses.

This change is particularly important for the French-speaking market. In France and Europe, the adoption of generative AI was initially driven by text-based uses: writing, customer support, document research, code generation. The next wave could be multimedia and agentic: automated dubbing, adaptation of marketing campaigns, localized video production, visual search in document databases, voice interfaces for public services, business copilots capable of simultaneously analyzing PDFs, photos, diagrams, and conversations. A credible “anything-to-anything” model is therefore not just a technological demonstration; it potentially redefines the value chain of many sectors.

What Google actually showed, and why it matters

According to the elements highlighted by Google and picked up by The Verge, the novelty lies less in an isolated feature than in the demonstration of a continuum between modalities. Google showed systems capable of taking varied inputs and producing equally varied outputs, with a degree of integration higher than what was still being seen recently in consumer products. The issue is no longer whether a model can “see” an image or “hear” a voice, but whether it can reason across several forms of signals and decide for itself on the best output.

This logic is embodied in particular in the Gemini family, which has become the backbone of Google’s AI strategy. Since Gemini 1.0, launched in December 2023, Google has defended the idea of a model designed from the outset to be multimodal. The company then emphasized Gemini’s ability to understand and combine text, code, image, audio, and video. With the more recent announcements around Gemini 1.5, followed by demonstrations presented at I/O, Google has above all sought to show scale and fluidity: massive context, long-video processing, real-time interactions, enriched generation, and integration into its products.

An important technical point is the context window. Google claimed up to 1 million tokens for Gemini 1.5 Pro, then mentioned experiments at 2 million tokens. Applied to concrete use cases, this means that a model can ingest very long corpora, entire codebases, long videos, or vast document sets without excessive splitting. In an “anything-to-anything” logic, this depth of context is essential: the value comes not only from conversion between formats, but from the ability to connect a large number of heterogeneous clues in a single response.

Google also highlighted near-real-time capabilities, with more natural interaction between the user and the system. Here again, the interest goes beyond ergonomics. A universal multimodal AI must be able to alternate between listening, visual perception, text generation, speech synthesis, and possibly software action without visible interruption. This is what Google is seeking to illustrate with its enhanced assistants, its live camera demonstrations, and its creative tools integrated into Workspace, Android, or Search.

The term “anything-to-anything” also serves to unify several sometimes disparate building blocks. Google already has major assets in image and video generation, with Imagen and Veo, as well as in audio and music with work such as AudioLM, MusicLM, then Lyria. Historically, these models existed as distinct families, each optimized for one type of output. The current challenge is to bring them closer together, or at least make them cooperate under a common interface driven by Gemini. It is this unification that gives meaning to the announcement: one same system can become the conductor of modalities that were once separate.

At the product level, this changes the nature of the user experience. Instead of opening one tool to write, another to illustrate, another to summarize a meeting, and another to generate a video, the user could simply express an intention. A concrete example: provide a spoken brief in French, attach a few reference images and a data table, then ask for a presentation, a video script, a voice-over, visuals, and a short version for social networks. The AI then chooses the right intermediate formats and the right final outputs. This is precisely the kind of integrated flow that Google wants to make credible.

It is nevertheless necessary to distinguish ambition from actual availability. As is often the case at Google, the technological demonstration moves faster than uniform deployment. Some capabilities are reserved for tests, developers, English-speaking markets, or specific products. Others rely on combinations of models rather than on a single monolithic system. The expression “anything-to-anything” therefore simplifies a more composite reality. But even with that caveat, the message sent to the market is clear: Google wants to be perceived not as a follower in multimodality, but as the player capable of industrializing it at large scale.

This ambition rests on a long-standing scientific foundation. The paper Attention Is All You Need, published in 2017 by Google researchers, laid the foundations of the Transformer architecture that powers all modern generative AI. DeepMind, acquired by Google in 2014, then accumulated major advances, from AlphaGo to AlphaFold, including work on multimodal learning. The company therefore has particular legitimacy when it says that the next step is no longer the enhanced text chatbot, but a model natively capable of moving across formats.

Still, the credibility of such a promise is measured against three very concrete criteria: output quality, latency, and cost. Turning an image into text is relatively commonplace in 2025. Turning a video into a marketing plan, a conversation into a software interface, or a spoken draft into a usable multimedia campaign, with coherence, low delay, and acceptable cost, is on another scale. That is where Google’s announcement carries its full weight: it does not just say “we can do more things,” it asserts “we can bring versatility and real-world use closer together.”

Why this step is strategic against OpenAI, Anthropic, and Meta

To understand the scope of Google’s offensive, it must be placed back in the competitive sequence of the last eighteen months. OpenAI captured global attention with ChatGPT, then gradually shifted the market’s center of gravity toward multimodal assistants. GPT-4, launched in March 2023, already promised image-text capabilities, but it was above all GPT-4o, presented in May 2024, that made concrete the idea of a system capable of listening, speaking, and seeing with unprecedented fluidity. OpenAI then highlighted voice response times on the order of a few hundred milliseconds, much closer to a natural conversation than previous generations.

Google’s response could not be limited to alignment. It needed to shift the debate. Where OpenAI strongly emphasized the real-time conversational interface, Google is seeking to broaden the field toward complete orchestration of modalities and depth of context. In other words, OpenAI popularized the multimodal assistant; Google wants to embody the universal multimodal platform. The nuance is decisive. In the first case, value is concentrated in the human-machine exchange. In the second, it extends to content production, professional workflows, search, creation, and agents.

Anthropic, for its part, adopted a more restrained but very effective trajectory. Claude 3 and then Claude 3.5 demonstrated high performance in document understanding, visual analysis, and programming, while cultivating an image of reliability for businesses. The company, backed in particular by Amazon and Google, did not seek spectacle at the same level as OpenAI or Google in voice and video. It instead consolidated a reputation as a “serious” model, strong on complex tasks and appreciated in professional environments. Faced with this approach, Google’s “anything-to-anything” is a way of reminding the market that it is aiming at a broader spectrum than premium text assistance.

Meta, finally, is playing a different game. With Llama 3, Mark Zuckerberg’s company strengthened its position in open models and in the developer ecosystem. Its multimodal work, its advances in audio and video generation, as well as its advertising and social infrastructure, give it considerable leverage. But Meta still suffers from a perceived fragmentation between research, open source, conversational products, and creative tools. Google is trying precisely to make that fragmentation an angle of attack: offering a more integrated vision, connected to Search, Workspace, Android, YouTube, and Cloud.

At the industrial level, the battle is also about inference costs and control of infrastructure. Google benefits from a structural advantage with its TPUs, its data centers, and its experience in optimization at very large scale. This dimension is fundamental. An “anything-to-anything” model is not only more ambitious; it is also potentially much more expensive to run, because it must handle rich, sometimes synchronous streams, and generate several outputs. If Google manages to make this economically sustainable in consumer services, it can regain the initiative on a field where OpenAI remains dependent on Microsoft Azure and where Meta still favors other trade-offs.

There is also a narrative issue. Since the arrival of ChatGPT, Google has often been described as the giant that had the technology but struggled to package it in a simple, conquering product story. The expression “anything-to-anything” partly corrects that deficit. It is immediately understandable, spectacular enough to circulate in the media, and broad enough to encompass both Gemini and Veo, Imagen, or the tools integrated into Google services. In an industry where the perception of leadership matters almost as much as benchmarks, this ability to impose a vocabulary is not trivial.

The comparison with competitors can be read along four axes. First axis: multimodal nativeness. OpenAI and Google both claim architectures designed for several modalities, but Google pushes further the idea of generalized conversion between formats. Second axis: product ecosystem. Google has the advantage of potential distribution in everyday services, from Gmail to Android, whereas Anthropic remains more centered on partner integrations and OpenAI is still building its conversational OS layer. Third axis: openness. Meta retains a symbolic lead in open models, an area where Google remains more cautious. Fourth axis: video and media creation. Google is clearly seeking to make this field a major differentiator, relying on YouTube and on its work in video generation.

This battle is also being fought at the level of developers and businesses. An “anything-to-anything” model becomes much more interesting if it can be called through a simple API, interact with tools, manage permissions, retain long context, and produce outputs that can be used in business pipelines. In this segment, Google Cloud is seeking to capitalize on Vertex AI and on its enterprise portfolio. The promise is not only to provide a more skillful chatbot, but a universal transformation layer for applications. That is a very different message from that of a simple consumer assistant, and it speaks directly to CIOs, SaaS vendors, and integrators.

In short, Google’s announcement should be read as an attempt to reconfigure the balance of power. OpenAI imposed the idea of the multimodal conversational companion. Anthropic established itself as a benchmark for quality and safety for many professionals. Meta wants to make its building blocks ubiquitous in the ecosystem. Google, for its part, is seeking to position itself as the provider of the universal cognitive infrastructure, the one that connects all forms of content and all digital touchpoints. If this promise materializes, it could reshuffle the cards far beyond the chatbot market alone.

Concrete use cases that change the equation for creators, businesses, and agents

The interest of “anything-to-anything” appears above all when leaving the terrain of demonstrations to look at real production chains. In media, advertising, e-commerce, education, or software, a large share of the work consists of transforming information from one format to another. A report becomes a presentation. A video becomes an article. A product catalog becomes a multichannel campaign. A meeting becomes a list of actions, then a ticket in a project management tool, then a customer message. Until now, these transitions required a mosaic of tools, often manual. A universal multimodal model promises to compress them.

Take the case of video creation, a sector where Google clearly wants to score points. With a system capable of ingesting a text brief, reference images, a soundtrack, a few rushes, and brand constraints, it becomes possible to generate several output formats: storyboard, script, voice-over, vertical mobile versions, localized subtitles, promotional thumbnails, and SEO summaries. The value lies not only in raw generation, but in intermodal coherence. A good system must preserve the characters, tone, visual style, chronology, and commercial intent from one format to another.

For businesses, the impact is just as strong. In customer support, an AI agent can receive a photo of a defective product, read the text history of the case, listen to a voice message from the customer, consult a PDF manual, and produce a structured response, possibly in the form of text, audio, or an illustrated procedure. In industry, a technician can film a machine, comment orally on a problem, and obtain a diagnosis enriched by documentation. In healthcare, subject to strict compliance, use cases are emerging around the synthesis of reports, imaging, and voice exchanges. “Anything-to-anything” then becomes an engine of documentary cross-functionality.

Software development is another key field. Google as well as OpenAI now emphasize agents capable of using tools and handling several formats. A universal multimodal model can read a mock-up, analyze a screenshot, listen to a voice instruction, generate code, comment on the changes, produce a video demonstration, and document the whole thing. For product teams, this brings design, documentation, and implementation closer together. For software vendors, it opens the way to environments where user intent is captured in the most natural form possible, then converted into software actions.

This evolution ties in with the rise of agents. As long as an assistant handles only text, its range of action remains limited. As soon as it can perceive the screen, hear the user, generate a spoken response, read a document, produce an explanatory image, or trigger a video flow, it becomes much closer to a versatile digital operator. “Anything-to-anything” is therefore not only a creative promise; it is a prerequisite for the agentification of many services. Personal assistants, business copilots, and software robots need this multimodal plasticity to move from advice to execution.

For French-speaking audiences, the interest is immediate across several segments. In France, the luxury, advertising, media, tourism, education, and public service sectors handle enormous volumes of content to adapt, translate, summarize, and republish. A model capable of moving naturally from text to video, from voice to image, or from PDF to presentation can accelerate localization and personalization. In a European context where linguistic diversity is a structural issue, this ability to quickly reformat the same message for several audiences becomes a tangible competitive advantage.

It is nevertheless necessary to keep a realistic reading. The most promising uses are also those that expose the current limits of models: hallucinations, visual inconsistencies, imperfect audio synchronization, factual errors in summaries, sometimes generic style, difficulty complying with fine-grained constraints on duration or graphic guidelines. The more a system touches several modalities, the more points of failure multiply. A convincing but factually wrong video, or a fluid audio summary that is legally risky, can be costly. Scaling will therefore depend as much on guardrails as on raw performance.

The question of rights and data provenance also remains central. Google has an advantage with YouTube and its content ecosystem, but that does not automatically settle debates on training, transformation of works, and compensation of rights holders. In the European Union, the AI Act and transparency obligations on certain generative uses will weigh on how these tools are deployed. For French companies, adoption of an “anything-to-anything” system will require contractual guarantees on confidentiality, traceability, and responsibilities in the event of error.

Despite these reservations, the paradigm shift is real. Generative AI tools are no longer simply specialized generators. They are becoming cognitive conversion machines capable of moving an idea across several media. It is this capability that could transform daily work more deeply than the first chatbots. Writing a text from a prompt was already useful. Transforming a business objective into a series of coherent deliverables, in several formats, with a minimum of human orchestration, is on another scale.

The French-speaking market facing a new wave of technological consolidation

For France and, more broadly, French-speaking Europe, Google’s announcement comes at a particular moment. Public debate on generative AI was initially structured around sovereignty, open models, data protection, and the role of local players such as Mistral AI. This reading grid remains relevant, but the rise of “anything-to-anything” models shifts part of the problem. The question is no longer only: who provides the best text-based LLM? It becomes: who controls the multimodal interface through which documents, meetings, images, videos, tasks, and decisions will pass tomorrow?

For French companies, this creates a complex trade-off. On the one hand, the major American groups have a considerable lead in infrastructure, research, and product integration. Google, Microsoft-OpenAI, Anthropic, and Meta have the means to train and serve massive models, finance specialized chips, and absorb experimentation costs. On the other hand, European organizations have stronger requirements in terms of compliance, data localization, and governance. In this framework, the attractiveness of an “anything-to-anything” solution will depend as much on its legal guarantees as on its technical quality.

The public sector could be one of the major beneficiaries, provided there is strict oversight. Administrations handle forms, letters, calls, scans, screenshots, PDF documents, and often heterogeneous knowledge bases. A well-governed multimodal system could streamline reception, document search, accessibility, and assistance to users. In France, where digitization sometimes remains difficult for part of the population, more natural voice and visual interfaces can have a concrete impact. But they require high standards in terms of explainability and protection of personal data.

French SMEs and mid-sized companies, often less endowed with technical resources than large enterprises, could also benefit from this evolution. Where integrating several specialized tools represented a significant cost, a more unified model can reduce complexity. A small marketing team can produce more multi-format content. An architecture firm can analyze plans, construction-site photos, and reports. An export department can localize its materials faster. The leverage effect is potentially strong, especially in sectors where content creation and documentation weigh heavily.

There is, however, a risk of increased dependency. If the “anything-to-anything” interface becomes the standard layer for content production and transformation, the players that control it capture a growing share of the value. This is one of the reasons why the question of interoperability will be crucial in Europe. Companies will want to avoid finding themselves locked into a single provider for their critical workflows. APIs, output formats, connectors to business software, and data portability will become issues just as important as performance benchmarks.

The job market and skills will also be affected. Content, support, training, design, and even some technical roles will evolve toward orchestration, verification, and creative or operational direction roles. In France, this reinforces the importance of hybrid training combining understanding of models, product culture, digital law, and business skills. The real competitive advantage will not come only from access to the model, but from the ability to insert it into reliable and measurable processes.

On the European competitive front, Google’s announcement also puts pressure on local players. Mistral AI, Aleph Alpha, or other companies in the region have so far mainly been evaluated on the terrain of language models, efficiency, and openness. Tomorrow, they will have to demonstrate a credible vision of advanced multimodality, or risk being confined to narrower segments. This does not mean they must reproduce Google’s strategy exactly. But the market standard is shifting. Customers will increasingly ask for systems capable of reading, seeing, listening, and acting in the same environment.

Finally, the European regulatory context could paradoxically become a competitive advantage for serious deployments. In the short term, it slows some uses. In the long term, it can favor providers capable of offering traceability, documentation, and robust control mechanisms. If Google wants to make “anything-to-anything” a standard in Europe, it will have to convince not only developers and creatives, but also legal departments, CISOs, and regulators. It is on this ability to combine power and governance that part of its adoption in the French-speaking market will depend.

Toward universal models, or toward a new invisible fragmentation?

Google’s announcement opens a broader perspective than that of a simple product cycle. The idea of an “anything-to-anything” model refers to a long-standing ambition in computing: to have a universal layer between human intention and machine execution. The keyboard, the mouse, the touchscreen, and then voice each constituted dominant interfaces. Generative multimodality suggests the emergence of an even more flexible interface, capable of adapting to context, medium, and objective. The user is no longer asked to adapt to the software; the model is asked to understand the most natural form of the user’s intention.

But this displayed universality could conceal a new form of fragmentation. Behind an apparently unified assistant, there may remain a constellation of specialized models, routers, security modules, memory systems, and external tools. The user sees a single interface; the infrastructure, meanwhile, remains composite. That does not detract from the value of the result, but it is a reminder that the “universal model” may not be a single block. It could be a federation of capabilities orchestrated so fluidly that it becomes indistinguishable from a single system.

Google is well positioned to play this score, precisely because it already has multiple building blocks. The real question is whether this orchestration will become a lasting advantage. The recent history of tech shows that impressive integration can be quickly imitated, especially when competitors also have colossal computing power and top-tier research teams. OpenAI is accelerating on agents and the conversational interface, Anthropic on reliability and professional use, Meta on openness and distribution. No player today seems in a position to lock down the next interface layer alone.

Over the longer term, the decisive criterion may be less raw versatility than contextual reliability. A model capable of doing everything, but unevenly, risks being overtaken by systems that know when to speak, when to show, when to act, when to ask for confirmation, and when to refrain. The future of universal multimodal models will therefore depend on their ability to manage uncertainty, explain their limits, and fit into real workflows without multiplying silent errors. That is a much harder challenge than the simple demonstration of conversion between formats.

For Google, the opportunity is immense. If the company succeeds, it can reconnect its major historical assets in the same loop: Search for access to information, Android for daily use, Workspace for productivity, YouTube for video, Cloud for business, and Gemini for cross-cutting intelligence. Few players can claim such continuity. But that same ambition exposes it to a higher level of scrutiny. Every weakness in coherence, security, or monetization will be examined at the scale of billions of potential users.

For the French-speaking market, the issue goes beyond the choice of a provider. It is about understanding that the next wave of AI will not be decided only by the quality of a chatbot, but by organizations’ ability to rethink their information flows as convertible materials. An idea, a document, a voice, an image, or a video become equivalent entry points in a production chain driven by models. Companies that know how to structure their data, define their guardrails, and train their teams will be able to benefit from considerable leverage. The others risk undergoing a standardization dictated by platforms.

The phrase “anything-to-anything” has something advertising-like about it, obviously. But it also captures a deeper direction: generative AI is moving away from simple augmented text to become an infrastructure for the general transformation of content and digital actions. If Google manages to turn this promise into reliable, accessible, and compliant products, competition will no longer be played out only on the best model, but on mastery of the layer that connects all modalities of work. At this stage, the most important thing may not be to know whether the universal model already exists, but to observe that the entire industry is now organizing its roadmap as if it had become the unavoidable strategic horizon.