Google DeepMind just published a paper that reframes how we should think about video generation models. Not as creative tools for making clips, but as visual reasoning engines that might replace the entire stack of specialized computer vision systems we've spent decades building. The claim is ambitious, the research is compelling, and the implications—if they're right—fundamentally reshape the economics of visual AI. Big if.
The paper, titled "Video models are zero-shot learners and reasoners," positions Veo 3 as the visual equivalent of large language models. Just as LLMs learned to perform tasks they weren't explicitly trained for—translation, summarization, code generation—DeepMind's researchers demonstrate that Veo 3 can handle more than 60 distinct vision tasks without task-specific fine-tuning. Segmentation, edge detection, denoising, super-resolution, physical property understanding, object affordance recognition, even simulating tool use. None of this required specialized training. The model learned these capabilities simply by being trained on web-scale video data with a continuation objective—predicting what comes next, frame by frame.
The parallel to language models is deliberate. LLMs emerged from a simple primitive: train massive generative models on internet-scale text, and capabilities emerge that no one explicitly programmed. DeepMind argues the same primitives apply to video. Scale up generative pre-training on visual data, and you get a foundation model that understands not just how to generate realistic footage, but how the physical world actually works. The paper's lead author, Thaddäus Wiedemer, and team published their findings on arXiv in September 2025, making the bold assertion that video models are "on a path to becoming unified, generalist vision foundation models."
The most intriguing claim involves what researchers call "chain-of-frames reasoning"—using temporal cues across generated sequences to solve visual logic problems. The paper demonstrates Veo 3 solving mazes and symmetry tasks by leveraging continuity between frames, essentially thinking through visual problems step-by-step the way language models chain thoughts together. This isn't just pattern matching from training data. It's using the structure of sequential generation to work through problems that require spatial and logical reasoning.
If this holds up under broader testing, it's significant. Most computer vision systems today are trained for single, narrow tasks: this model detects objects, that model segments scenes, another handles depth estimation. Each requires its own training pipeline, labeled datasets, and deployment infrastructure. A zero-shot video model that can handle all of these tasks plus reason about physical relationships and temporal dynamics would collapse an entire toolchain into a single foundation model. DeepMind is betting that as inference costs decline—following the same trajectory as LLMs—generative video could simply replace specialized vision models entirely.
On the commercial side, Google is upgrading AI Mode in Image Search with conversational querying powered by Gemini 2.5 and built on Google Lens. Instead of filtering by static attributes like color, size, or category, users describe what they want in natural language, combine text prompts with reference photos, and iteratively refine results. Shopping for clothing that matches a specific vibe? Browsing interior design ideas with particular aesthetic constraints? The system parses subtle visual context, secondary objects in scenes, stylistic cues, and surfaces shoppable links directly in Search.
This is the consumer-facing instantiation of the same underlying thesis: video and image models trained generatively can understand visual semantics well enough to replace specialized search and recommendation systems. You're not querying a database with structured filters. You're having a conversation with a model that understands what "mid-century modern but warmer" means when you show it a reference photo of a living room.
Let's acknowledge what's genuinely impressive here before we interrogate the claims. Training a single model that performs 60+ vision tasks zero-shot represents a meaningful consolidation of capabilities. According to the DeepMind paper, Veo 3 handles segmentation, detection, editing, physical simulation, and early reasoning tasks without ever being explicitly trained on those objectives. That's a departure from the current paradigm where each vision task requires dedicated architectures, training regimes, and often proprietary datasets. If DeepMind's claims hold across broader evaluation, it suggests we're approaching an inflection point where generalist models outperform specialists on most practical tasks.
But the economics matter as much as the capabilities. DeepMind expects inference costs to decline similarly to LLMs, but video processing is computationally orders of magnitude more expensive than text. A single frame contains vastly more data than a sentence. A 10-second video at 30fps is 300 frames of high-dimensional visual information. Processing that through a foundation model at scale, in real-time, for billions of queries? The infrastructure requirements are staggering. LLMs became economically viable because inference costs dropped fast enough that per-query margins made sense. Video models need the same cost curve, and we're not there yet.
If DeepMind's trajectory proves out, we're looking at a fundamental reordering of the computer vision market. Companies that built businesses around specialized vision models—object detection APIs, semantic segmentation tools, video enhancement services—face commoditization by generalist foundations. Winsome's analysis of AI model consolidation trends in 2024 showed that specialized APIs lose pricing power once foundation models achieve comparable performance, even if they're not technically superior on every benchmark. Good enough at zero marginal cost beats slightly better at per-call pricing.
For marketers and product teams, this has immediate strategic implications. If video models become true zero-shot vision foundations, you're not building integrations with a dozen specialized tools. You're prompting a single model to handle image analysis, content moderation, visual search, product tagging, and creative generation. That's architecturally simpler but strategically riskier—more platform dependency, less negotiating leverage, and reliance on whoever controls the foundation model infrastructure (likely Google, OpenAI, or Anthropic).
Here's where skepticism is warranted. DeepMind demonstrates chain-of-frames reasoning on mazes and symmetry tasks, which is genuinely novel. But these are constrained, well-defined problems with clear success criteria. Visual reasoning in the real world—understanding causality, predicting physical interactions in novel contexts, recognizing affordances in unfamiliar objects—remains extraordinarily difficult. The paper shows promising early results, but "early forms of visual reasoning" is doing a lot of rhetorical work in that sentence.
Language models benefited from decades of NLP research, structured datasets, and clear evaluation benchmarks before the GPT moment arrived. Vision lacks equivalent foundations for reasoning tasks. We have benchmarks for detection and segmentation, but not for "does this model understand why this configuration of objects is unstable?" or "can it predict how this tool would be used in a novel context?" The gap between generating plausible-looking frames and actually modeling the physics and causality those frames represent is where the research needs to land before we can claim video models are truly reasoning, not just pattern-matching at unprecedented scale.
DeepMind is making a multi-billion dollar bet that video models follow the same scaling laws, cost trajectories, and emergent capability curves as language models. They might be right. The primitives are similar: massive generative pre-training on web-scale data, continuation objectives that force models to predict future states, and architectural innovations that improve sample efficiency. But video is fundamentally harder—more data per token, more computational overhead, less mature training infrastructure, and trickier evaluation protocols.
If inference costs decline fast enough and zero-shot performance generalizes well enough, we're watching the foundation layer for visual AI get defined in real time. If costs stay high or reasoning capabilities plateau before reaching practical utility, we'll have very impressive video generation tools that complement—but don't replace—specialized vision systems. Either outcome is fascinating. But which one we get determines whether this is a paradigm shift or just another impressive research demo that doesn't scale.
We help marketing and product leaders evaluate when to adopt foundation models versus specialized tools—where generalist AI creates leverage, where specialists still win, and how to structure infrastructure that doesn't lock you into a single platform's trajectory. If you're figuring out what Veo 3's claims mean for your vision AI strategy, let's talk.