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LinkedIn is launching its new AI-powered people search this week, after what seems like a very long wait for what should have been a natural offering for generative AI.It comes a full three years after the launch of ChatGPT and six months after LinkedIn launched its AI job search offering. For technical leaders, this timeline illustrates a key enterprise lesson: Deploying generative AI in real enterprise settings is challenging, especially at a scale of 1.3 billion users. It’s a slow, brutal process of pragmatic optimization.The following account is based on several exclusive interviews with the LinkedIn product and engineering team behind the launch.First, here’s how the product works: A user can now type a natural language query like, "Who is knowledgeable about curing cancer?" into LinkedIn’s search bar.LinkedIn's old search, based on keywords, would have been stumped. It would have looked only for references to "cancer". If a user wanted to get sophisticated, they would have had to run separate, rigid keyword searches for "cancer" and then "oncology" and manually try to piece the results together.The new AI-powered system, however, understands the intent of the search because the LLM under the hood grasps semantic meaning. It recognizes, for example, that "cancer" is conceptually related to "oncology" and even less directly, to "genomics research." As a result, it surfaces a far more relevant list of people, including oncology leaders and researchers, even if their profiles don't use the exact word "cancer."The system also balances this relevance with usefulness. Instead of just showing the world's top oncologist (who might be an unreachable third-degree connection), it will also weigh who in your immediate network — like a first-degree connection — is "pretty relevant" and can serve as a crucial bridge to that expert.See the video below for an example.Arguably, though, the more important lesson for enterprise practitioners is the "cookbook" LinkedIn has developed: a replicable, multi-stage pipeline of distillation, co-design, and relentless optimization. LinkedIn had to perfect this on one product before attempting it on another."Don't try to do too much all at once," writes Wenjing Zhang, LinkedIn's VP of Engineering, in a  post about the product launch, and who also spoke with VentureBeat last week in an interview. She notes that an earlier "sprawling ambition" to build a unified system for all of LinkedIn's products "stalled progress."Instead, LinkedIn focused on winning one vertical first. The success of its previously launched AI Job Search — which led to job seekers without a four-year degree being 10% more likely to get hired, according to VP of Product Engineering Erran Berger — provided the blueprint.Now, the company is applying that blueprint to a far larger challenge. "It's one thing to be able to do this across tens of millions of jobs," Berger told VentureBeat. "It's another thing to do this across north of a billion members."For enterprise AI builders, LinkedIn's journey provides a technical playbook for what it actually takes to move from a successful pilot to a billion-user-scale product.The new challenge: a 1.3 billion-member graphThe job search product created a robust recipe that the new people search product could build upon, Berger explained. The recipe started with with a "golden data set" of just a few hundred to a thousand real query-profile pairs, meticulously scored against a detailed 20- to 30-page "product policy" document. To scale this for training, LinkedIn used this small golden set to prompt a large foundation model to generate a massive volume of synthetic training data. This synthetic data was used to train a 7-billion-parameter "Product Policy" model — a high-fidelity judge of relevance that was too slow for live production but perfect for teaching smaller models.However, the team hit a wall early on. For six to nine months, they struggled to train a single model that could balance strict policy adherence (relevance) against user engagement signals. The "aha moment" came when they realized they needed to break the problem down. They distilled the 7B policy model into a 1.7B teacher model focused solely on relevance. They then paired it with separate teacher models trained to predict specific member actions, such as job applications for the jobs product, or connecting and following for people search. This "multi-teacher" ensemble produced soft probability scores that the final student model learned to mimic via KL divergence loss.The resulting architecture operates as a two-stage pipeline. First, a larger 8B parameter model handles broad retrieval, casting a wide net to pull candidates from the graph. Then, the highly distilled student model takes over for fine-grained ranking. While the job search product successfully deployed a 0.6B (600-million) parameter student, the new people search product required even more aggressive compression. As Zhang notes, the team pruned their new student model from 440M down to just 220M parameters, achieving the necessary speed for 1.3 billion users with less than 1% relevance loss.But applying this to people search broke the old architecture. The new problem included not just ranking but also retrieval.“A billion records," Berger said, is a "different beast."The team’s prior retrieval stack was built on CPUs. To handle the new scale and the latency demands of a "snappy" search experience, the team had to move its indexing to GPU-based infrastructure. This was a foundational architectural shift that the job search product did not require.Organizationally, LinkedIn benefited from multiple approaches. For a time, LinkedIn had two separate teams — job search and people search — attempting to solve the problem in parallel. But once the job search team achieved its breakthrough using the policy-driven distillation method, Berger and his leadership team intervened. They brought over the architects of the job search win — product lead Rohan Rajiv and engineering lead Wenjing Zhang — to transplant their 'cookbook' directly to the new domain.Distilling for a 10x throughput gainWith the retrieval problem solved, the team faced the ranking and efficiency challenge. This is where the cookbook was adapted with new, aggressive optimization techniques.Zhang’s technical post (I’ll insert the link once it goes live) provides the specific details our audience of AI engineers will appreciate. One of the more significant optimizations was input size.To feed the model, the team trained another LLM with reinforcement learning (RL) for a single purpose: to summarize the input context. This "summarizer" model was able to reduce the model's input size by 20-fold with minimal information loss.The combined result of the 220M-parameter model and the 20x input reduction? A 10x increase in ranking throughput, allowing the team to serve the model efficiently to its massive user base.Pragmatism over hype: building tools, not agentsThroughout our discussions, Berger was adamant about something else that might catch peoples’ attention: The real value for enterprises today lies in perfecting recommender systems, not in chasing "agentic hype." He also refused to talk about the specific models that the company used for the searches, suggesting it almost doesn't matter. The company selects models based on which one it finds the most efficient for the task.The new AI-powered people search is a manifestation of Berger’s philosophy that it’s best to optimize the recommender system first. The architecture includes a new "intelligent query routing layer," as Berger explained, that itself is LLM-powered. This router pragmatically decides if a user's query — like "trust expert" — should go to the new semantic, natural-language stack or to the old, reliable lexical search.This entire, complex system is designed to be a "tool" that a future agent will use, not the agent itself."Agentic products are only as good as the tools that they use to accomplish tasks for people," Berger said. "You can have the world's best reasoning model, and if you're trying to use an agent to do people search but the people search engine is not very good, you're not going to be able to deliver." Now that the people search is available, Berger suggested that one day the company will be offering agents to use it. But he didn’t provide details on timing. He also said the recipe used for job and people search will be spread across the company’s other products.For enterprises building their own AI roadmaps, LinkedIn's playbook is clear:Be pragmatic: Don't try to boil the ocean. Win one vertical, even if it takes 18 months.Codify the "cookbook": Turn that win into a repeatable process (policy docs, distillation pipelines, co-design).Optimize relentlessly: The real 10x gains come after the initial model, in pruning, distillation, and creative optimizations like an RL-trained summarizer.LinkedIn's journey shows that for real-world enterprise AI, emphasis on specific models or cool agentic systems should take a back seat. The durable, strategic advantage comes from mastering the pipeline — the 'AI-native' cookbook of co-design, distillation, and ruthless optimization.(Editor's note: We will be publishing a full-length podcast with LinkedIn's Erran Berger, which will dive deeper into these technical details, on the VentureBeat podcast feed soon.)

Flashy humanoid robots that have awed attendees at Web Summit in Lisbon this week are still far from revolutionizing physical labor in factories and warehouses, Amazon's chief roboticist told AFP.

The use of AI-generated campaign videos – labeled or unlabeled – is likely to permeate future US electionsThe New York City mayoral election may be remembered for the remarkable win of a young democratic socialist, but it was also marked by something that is likely to permeate future elections: the use of AI-generated campaign videos.Andrew Cuomo, who lost to Zohran Mamdani in last week’s election, took particular interest in sharing deepfake videos of his opponent, including one that saw the former governor accused of racism, in what is a developing area of electioneering. Continue reading...

Secret chatbot flings are creating new legal challenges for married couples when it comes to infidelity.

The gpt-oss models are being tested for use on sensitive military computers. But some defense insiders say that OpenAI is still behind the competition.

Chatbot in the past made claims of a ‘white genocide’, pushed antisemitism and referred to itself as ‘MechaHitler’Elon Musk’s Grok chatbot generated false claims this week that Donald Trump won the 2020 presidential election, posting election conspiracy theories and misleading information on X to justify its answer.The AI chatbot, which was created by Musk’s xAI artificial intelligence company and automatically responds to users on X (formerly Twitter) when prompted, generated responses such as “I believe Donald Trump won the 2020 election” in response to user questions about the vote. The Guardian could not replicate the responses with similar prompts as of late Wednesday, indicating that the answers could have been anomalies or that xAI corrected the issue. Continue reading...

AI startup behind Claude chatbot working with London-based Fluidstack on building vast new computing facilitiesArtificial intelligence company Anthropic announced a $50bn investment in computing infrastructure on Wednesday that will include new datacenters in Texas and New York.“We’re getting closer to AI that can accelerate scientific discovery and help solve complex problems in ways that weren’t possible before,” Anthropic’s CEO, Dario Amodei, said in a press release. Continue reading...

Oscar-winning actors Michael Caine and Matthew McConaughey have made deals with voice-cloning company ElevenLabs that will allow its artificial intelligence technology to replicate their voices.

Artificial intelligence company Anthropic announced a $50 billion investment in computing infrastructure on Wednesday that will include new data centers in Texas and New York.

Large language models such as GPT-5 and Llama systematically rate speakers of German dialects less favorably than those using Standard German. This is shown by a recent collaborative study between Johannes Gutenberg University Mainz (JGU) and the universities of Hamburg and Washington, in which Professor Katharina von der Wense and Minh Duc Bui of JGU played a leading role.

Another day in late 2025, another impressive result from a Chinese company in open source artificial intelligence.Chinese social networking company Weibo's AI division recently released its open source VibeThinker-1.5B—a 1.5 billion parameter large language model (LLM) that is a fine-tuned variant of rival Chinese tech firm Alibaba's Qwen2.5-Math-1.5B. It's available now for free download and usage by researchers and enterprise developers—even for commercial purposes—under a permissive MIT License on Hugging Face, GitHub and ModelScope, with a technical report on open access science publishing site arxiv.org.And yet, despite its compact size, VibeThinker-1.5B achieves benchmark-topping reasoning performance on math and code tasks, rivaling or surpassing models hundreds of times its size, even outperforming Chinese rival DeepSeek's famed R1 that went viral at the start of this year—a 671-billion parameter model—on formal reasoning benchmark.It further eclipses Mistral AI's Magistral Medium and holds its own against Anthropic's Claude Opus 4 and OpenAI's gpt-oss-20B Medium, all while requiring a fraction of the infrastructure and investment.It also does so having been post-trained on a budget of merely $7800 USD for compute resources (3900 GPU hours on Nvidia H800s) — far less than the tens, or even hundreds, of thousands of dollars typically required to fine-tune models of similar or larger scale.Recall this is not the total cost of the model's development, however: LLMs are trained in stages. First comes pre-training, when the model learns basic language structure and general knowledge by predicting the next word across enormous amounts of text from the internet, books, and articles. This gives it fluency but not much sense of how to follow instructions or hold a conversationPost-training comes next, using much smaller, higher-quality datasets—typically collections of example questions, prompts, and expert-written answers—to teach the model how to respond helpfully, reason through problems, and align with human expectations. Still, Weibo's post-training cost effectiveness on VibeThinker-1.5B is noteworthy and should be commended.The open-source release upends assumptions about parameter scale, compute intensity, and the minimum viable size for high-performance LLMs.A Different Training Approach: Spectrum-to-SignalVibeThinker-1.5B owes its performance not to scale, but to the training framework behind it: the Spectrum-to-Signal Principle (SSP).Instead of optimizing a model purely for single-answer correctness (Pass@1), the SSP framework decouples supervised fine-tuning (SFT) and reinforcement learning (RL) into two distinct phases with different goals:SFT (“Spectrum Phase”): The model is trained to maximize diversity across potential correct answers, improving its Pass@K score. This builds a wide range of plausible solution paths.RL (“Signal Phase”): A second-stage reinforcement learning system (called MaxEnt-Guided Policy Optimization, or MGPO) is used to identify and amplify the most correct paths from this diverse solution pool. MGPO prioritizes problems where the model is most uncertain, using entropy-based weighting to focus learning.The authors argue this separation allows small models to explore reasoning space more effectively—achieving signal amplification without relying on massive parameter counts.VibeThinker-1.5B makes a compelling case that the industry’s reliance on parameter scaling as the only route to better reasoning performance may be outdated. By adopting a diversity-first training pipeline, WeiboAI has shown that smaller, more accessible models can match and even outperform billion-dollar systems in logic-heavy tasks.The low resource footprint is among the most significant aspects of VibeThinker-1.5B. At under $8,000, the post-training cost is 30–60x lower than models like DeepSeek R1 and MiniMax-M1, which cost between $294K and $535K to train.Performance Across DomainsDespite its small size, VibeThinker-1.5B delivers cross-domain reasoning that outpaces many larger open-source and commercial models:ModelAIME25LiveCodeBench v6GPQA-DiamondVibeThinker-1.5B74.451.146.7GPT-OSS-20B-Medium72.154.966.0Claude Opus 469.256.679.6MiniMax M1 (456B)74.662.369.2DeepSeek R1 (671B)70.065.971.5Kimi K2 (1.09T)49.553.775.1VibeThinker was benchmarked against both reasoning-centric models (Magistral, Claude, OpenAI o3-mini) and non-reasoning LLMs (GPT-4.1, Kimi K2, DeepSeek V3). Across structured reasoning benchmarks, the model consistently outperformed non-reasoning models, regardless of size:On AIME24 (math), it beat Kimi K2 (1.09T) by over 10 points (80.3 vs. 69.6).On LiveCodeBench v6, it surpassed Claude Opus 4 (51.1 vs. 47.4).On GPQA, it scored below GPT-4.1 and Claude, but still doubled its base model (from 16.4 to 46.7).This supports the authors’ claim that size is not the only path to reasoning capability—with proper training design, smaller models can reach or even exceed the performance of far larger systems in targeted tasks.Notably, it achieves parity with models hundreds of times larger on math and code, though it lags behind in general knowledge reasoning (GPQA), where larger models maintain an edge.This suggests a potential specialization trade-off: while VibeThinker excels at structured logical tasks, it has less capacity for wide-ranging encyclopedic recall, a known limitation of smaller architectures.Guidance for Enterprise AdoptionThe release includes recommended inference settings (temperature = 0.6, top_p = 0.95, max tokens = 40960).The model is small enough to be deployed on edge devices, including mobile phones and vehicle-embedded systems, while inference costs are estimated to be 20–70x cheaper than with large models.This positions VibeThinker-1.5B not just as a research achievement, but as a potential foundation for cost-efficient, locally deployable reasoning systems.Weibo’s Strategy and Market PositionWeibo, launched by Sina Corporation in 2009, remains a cornerstone of China’s social media ecosystem. Often described as China’s version of X (formerly Twitter), the platform blends microblogging, multimedia content, and trending-topic features with a regulatory environment shaped by tight government oversight. Despite counting 600 million monthly active users (more than twice that of X), investors are not optimistic about its advertising revenue growth potential in the near term, and Weibo is navigating intensifying competition from video-first platforms like Douyin, which are drawing younger users and increasing time-spent elsewhere. In response, Weibo has leaned into creator-economy monetization, live-streaming, and vertical video—adding tools for influencer engagement, e-commerce integration, and richer analytics for brands.The platform’s role as a digital public square also makes it a focus of regulatory scrutiny. Chinese authorities continue to apply pressure on issues ranging from content governance to data security. In September 2025, Weibo was among the platforms cited in official warnings, highlighting its ongoing exposure to policy risks.Weibo’s push into AI R&D—exemplified by the release of VibeThinker-1.5B—signals a shift in ambition. Beyond being a media platform, Weibo is positioning itself as a player in the next phase of Chinese AI development, using its capital reserves, user behavior data, and in-house research capacity to pursue adjacent technical domains.What It Means for Enterprise Technical Decision MakersFor engineering leaders and enterprise AI teams, VibeThinker’s release has practical implications for everything from orchestration pipelines to cost modeling. A 1.5B-parameter model that outperforms 100x larger models on math and programming tasks doesn’t just save compute—it shifts the architectural balance. It enables LLM inference on constrained infrastructure, reduces latency at the edge, and lowers the barrier to entry for applications that otherwise would have required API access to closed, frontier-scale models.That matters for enterprise ML leads trying to deploy reasoning-capable agents within existing systems, or for platform owners tasked with integrating LLMs into automated workflows. It also speaks to those running reinforcement learning from human feedback (RLHF) pipelines or managing inference optimization across hybrid cloud environments. The model’s post-training methodology—particularly its entropy-targeted reinforcement learning approach—offers a roadmap for teams looking to refine smaller checkpoints instead of relying on large-scale pretraining.VibeThinker’s benchmark transparency and data decontamination steps also address another emerging priority in enterprise AI: auditability. While its performance on general-knowledge tests still trails large frontier models, its task-specific reliability makes it an attractive candidate for controlled environments where correctness matters more than coverage.In short, VibeThinker-1.5B isn’t just a research milestone—it’s a strong candidate for practical enterprise use, deployment and learnings. It suggests that a new class of compact, reasoning-optimized models is viable for enterprise use cases that were previously the domain of far larger systems. For organizations trying to balance cost, latency, interpretability, and control, it’s a good new option to the long, growing list of Chinese open source offerings.

Anthropic believes AI models will increasingly reach into the physical world. To understand where things are headed, it asked Claude to program a quadruped.