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 graph

The 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 gain

With 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 agents

Throughout 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:

1. Be pragmatic: Don't try to boil the ocean. Win one vertical, even if it takes 18 months.

2. Codify the "cookbook": Turn that win into a repeatable process (policy docs, distillation pipelines, co-design).

3. 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.)

The International Energy Agency’s annual report highlights the geopolitical dangers of the AI boom’s relentless strain on energy.

Despite record purchases of clean energy, the industry's projected need for 362 GW of additional power by 2035 far exceeds available renewable capacity.

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 conversation

Post-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-Signal

VibeThinker-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 Domains

Despite its small size, VibeThinker-1.5B delivers cross-domain reasoning that outpaces many larger open-source and commercial models:

Model	AIME25	LiveCodeBench v6	GPQA-Diamond
VibeThinker-1.5B	74.4	51.1	46.7
GPT-OSS-20B-Medium	72.1	54.9	66.0
Claude Opus 4	69.2	56.6	79.6
MiniMax M1 (456B)	74.6	62.3	69.2
DeepSeek R1 (671B)	70.0	65.9	71.5
Kimi K2 (1.09T)	49.5	53.7	75.1

VibeThinker 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 Adoption

The 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 Position

Weibo, 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 Makers

For 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.

As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it's creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours.

Deductive AI, a startup emerging from stealth mode Wednesday, believes it has found a solution by applying reinforcement learning — the same technology that powers game-playing AI systems — to the messy, high-stakes world of production software incidents. The company announced it has raised $7.5 million in seed funding led by CRV, with participation from Databricks Ventures, Thomvest Ventures, and PrimeSet, to commercialize what it calls "AI SRE agents" that can diagnose and help fix software failures at machine speed.

The pitch resonates with a growing frustration inside engineering organizations: Modern observability tools can show that something broke, but they rarely explain why. When a production system fails at 3 a.m., engineers still face hours of manual detective work, cross-referencing logs, metrics, deployment histories, and code changes across dozens of interconnected services to identify the root cause.

"The complexities and inter-dependencies of modern infrastructure means that investigating the root cause of an outage or incident can feel like searching for a needle in a haystack, except the haystack is the size of a football field, it's made of a million other needles, it's constantly reshuffling itself, and is on fire — and every second you don't find it equals lost revenue," said Sameer Agarwal, Deductive's co-founder and chief technology officer, in an exclusive interview with VentureBeat.

Deductive's system builds what the company calls a "knowledge graph" that maps relationships across codebases, telemetry data, engineering discussions, and internal documentation. When an incident occurs, multiple AI agents work together to form hypotheses, test them against live system evidence, and converge on a root cause — mimicking the investigative workflow of experienced site reliability engineers, but completing the process in minutes rather than hours.

The technology has already shown measurable impact at some of the world's most demanding production environments. DoorDash's advertising platform, which runs real-time auctions that must complete in under 100 milliseconds, has integrated Deductive into its incident response workflow. The company has set an ambitious 2026 goal of resolving production incidents within 10 minutes.

"Our Ads Platform operates at a pace where manual, slow-moving investigations are no longer viable. Every minute of downtime directly affects company revenue," said Shahrooz Ansari, Senior Director of Engineering at DoorDash, in an interview with VentureBeat. "Deductive has become a critical extension of our team, rapidly synthesizing signals across dozens of services and surfacing the insights that matter—within minutes."

DoorDash estimates that Deductive has root-caused approximately 100 production incidents over the past few months, translating to more than 1,000 hours of annual engineering productivity and a revenue impact "in millions of dollars," according to Ansari. At location intelligence company Foursquare, Deductive reduced the time to diagnose Apache Spark job failures by 90% —t urning a process that previously took hours or days into one that completes in under 10 minutes — while generating over $275,000 in annual savings.

Why AI-generated code is creating a debugging crisis

The timing of Deductive's launch reflects a brewing tension in software development: AI coding assistants are enabling engineers to generate code faster than ever, but the resulting software is often harder to understand and maintain.

"Vibe coding," a term popularized by AI researcher Andrej Karpathy, refers to using natural-language prompts to generate code through AI assistants. While these tools accelerate development, they can introduce what Agarwal describes as "redundancies, breaks in architectural boundaries, assumptions, or ignored design patterns" that accumulate over time.

"Most AI-generated code still introduces redundancies, breaks architectural boundaries, makes assumptions, or ignores established design patterns," Agarwal told Venturebeat. "In many ways, we now need AI to help clean up the mess that AI itself is creating."

The claim that engineers spend roughly half their time on debugging isn't hyperbole. The Association for Computing Machinery reports that developers spend 35% to 50% of their time validating and debugging software. More recently, Harness's State of Software Delivery 2025 report found that 67% of developers are spending more time debugging AI-generated code.

"We've seen world-class engineers spending half of their time debugging instead of building," said Rakesh Kothari, Deductive's co-founder and CEO. "And as vibe coding generates new code at a rate we've never seen, this problem is only going to get worse."

How Deductive's AI agents actually investigate production failures

Deductive's technical approach differs substantially from the AI features being added to existing observability platforms like Datadog or New Relic. Most of those systems use large language models to summarize data or identify correlations, but they lack what Agarwal calls "code-aware reasoning"—the ability to understand not just that something broke, but why the code behaves the way it does.

"Most enterprises use multiple observability tools across different teams and services, so no vendor has a single holistic view of how their systems behave, fail, and recover—nor are they able to pair that with an understanding of the code that defines system behavior," Agarwal explained. "These are key ingredients to resolving software incidents and it is exactly the gap Deductive fills."

The system connects to existing infrastructure using read-only API access to observability platforms, code repositories, incident management tools, and chat systems. It then continuously builds and updates its knowledge graph, mapping dependencies between services and tracking deployment histories.

When an alert fires, Deductive launches what the company describes as a multi-agent investigation. Different agents specialize in different aspects of the problem: one might analyze recent code changes, another examines trace data, while a third correlates the timing of the incident with recent deployments. The agents share findings and iteratively refine their hypotheses.

The critical difference from rule-based automation is Deductive's use of reinforcement learning. The system learns from every incident which investigative steps led to correct diagnoses and which were dead ends. When engineers provide feedback, the system incorporates that signal into its learning model.

"Each time it observes an investigation, it learns which steps, data sources, and decisions led to the right outcome," Agarwal said. "It learns how to think through problems, not just point them out."

At DoorDash, a recent latency spike in an API initially appeared to be an isolated service issue. Deductive's investigation revealed that the root cause was actually timeout errors from a downstream machine learning platform undergoing a deployment. The system connected these dots by analyzing log volumes, traces, and deployment metadata across multiple services.

"Without Deductive, our team would have had to manually correlate the latency spike across all logs, traces, and deployment histories," Ansari said. "Deductive was able to explain not just what changed, but how and why it impacted production behavior."

The company keeps humans in the loop—for now

While Deductive's technology could theoretically push fixes directly to production systems, the company has deliberately chosen to keep humans in the loop—at least for now.

"While our system is capable of deeper automation and could push fixes to production, currently, we recommend precise fixes and mitigations that engineers can review, validate, and apply," Agarwal said. "We believe maintaining a human in the loop is essential for trust, transparency and operational safety."

However, he acknowledged that "over time, we do think that deeper automation will come and how humans operate in the loop will evolve."

Databricks and ThoughtSpot veterans bet on reasoning over observability

The founding team brings deep expertise from building some of Silicon Valley's most successful data infrastructure platforms. Agarwal earned his Ph.D. at UC Berkeley, where he created BlinkDB, an influential system for approximate query processing. He was among the first engineers at Databricks, where he helped build Apache Spark. Kothari was an early engineer at ThoughtSpot, where he led teams focused on distributed query processing and large-scale system optimization.

The investor syndicate reflects both the technical credibility and market opportunity. Beyond CRV's Max Gazor, the round included participation from Ion Stoica, founder of Databricks and Anyscale; Ajeet Singh, founder of Nutanix and ThoughtSpot; and Ben Sigelman, founder of Lightstep.

Rather than competing with platforms like Datadog or PagerDuty, Deductive positions itself as a complementary layer that sits on top of existing tools. The pricing model reflects this: Instead of charging based on data volume, Deductive charges based on the number of incidents investigated, plus a base platform fee.

The company offers both cloud-hosted and self-hosted deployment options and emphasizes that it doesn't store customer data on its servers or use it to train models for other customers — a critical assurance given the proprietary nature of both code and production system behavior.

With fresh capital and early customer traction at companies like DoorDash, Foursquare, and Kumo AI, Deductive plans to expand its team and deepen the system's reasoning capabilities from reactive incident analysis to proactive prevention. The near-term vision: helping teams predict problems before they occur.

DoorDash's Ansari offers a pragmatic endorsement of where the technology stands today: "Investigations that were previously manual and time-consuming are now automated, allowing engineers to shift their energy toward prevention, business impact, and innovation."

In an industry where every second of downtime translates to lost revenue, that shift from firefighting to building increasingly looks less like a luxury and more like table stakes.

ChatGPT is about to become faster and more conversational as OpenAI upgrades its flagship model GPT-5 to GPT-5.1.

OpenAI announced two updates to the GPT-5 series: GPT-5.1 Instant and GPT-5.1 Thinking. Both models are now accessible on ChatGPT. 

GPT-5.1 Instant, essentially the default and most-used model, is now “warmer, more intelligent, and better at following your instructions,” according to OpenAI. Meanwhile, GPT-5.1 Thinking is an advanced reasoning model that responds faster for simple tasks and more persistently on complex ones.

“We heard clearly from users that great AI should not only be smart, but also enjoyable to talk to,” OpenAI said in a blog post. “GPT-5.1 improves meaningfully on both intelligence and communication style.” 

The company added that both models offer a way for users to “shape ChatGPT’s tone,” allowing people to control how the chat platform responds depending on the conversation they are having. 

Both models were rolled out to ChatGPT Pro, Plus, Go and Business users, as well as the free tier. Those on the Enterprise and Edu plans will get a seven-day early-access toggle for the models before GPT-5.1 becomes the default model. OpenAI said the models can also be accessible through the API, both with adapted reasoning. 

OpenAI has noted that it will soon update GPT-5 Pro to version 5.1. 

Instant and Thinking models 

The 5.1 tag reflects improvements to the base model, and OpenAI considers these part of the GPT-5 family, trained on the same stack and data as its reasoning models. The biggest difference between 5.1 and 5 is its more natural and conversational tone, OpenAI CEO for Applications Fidji Simo said in a Substack post. 

“Based on early testing, it often surprises people with its playfulness while remaining clear and useful,” OpenAI said in its post. 

Instant can use adaptive reasoning to help it decide when it needs to think about its answers, especially when it comes to more complicated questions. OpenAI noted that it has improved the model's instruction following, so that while it continues to respond quickly, it also directly addresses the user’s query. 

Recent model releases, such as Baidu’s ERNIE-4.5-VL-28B-A3B-Thinking, have been outperforming GPT-5 in benchmarks like instruction-following. 

GPT-5.1 Thinking can figure out on its own how much reasoning power it should devote to a prompt. It adapts to the type and complexity of a query, so it will take longer to answer a fuller, complex question than a simple summary request. 

OpenAI said evaluations showed that GPT-5.1 Thinking spends less time and therefore uses fewer tokens on simple tasks compared to GPT-5, outperforming the base model in speed of response. 

One thing enterprises should note is that GPT-5.1 Thinking answers “with less jargon and fewer undefined terms.” OpenAI said removing jargony responses makes Thinking more approachable when it comes to explaining technical concepts.

More personalization

Another big update to ChatGPT is increased personalization. This allows users to toggle between a friendly and authoritative chat platform experience in their conversations. 

ChatGPT already allows users to choose preset options for model tone, but the new update expands these options “to better reflect the most common ways people use ChatGPT.”

Options include "default," "friendly" (formerly "listener"), "efficient" (previously "robot"), "professional," "candid" and "quirky." Two other personalities, "cynical" and "nerdy," remain unchanged. 

“We think many people will find that GPT-5.1 does a better job of bringing IQ and EQ together, but one default clearly can’t meet everyone’s needs," Simo said. "That’s why we’re also making it easier to customize ChatGPT with a range of presets to choose from. The model has the same capabilities whether you select default or one of these options, but the style of its responses will differ — more formal or familiar, more playful or direct, more or less jargon or slang. Of course, eight personalities still don't cover the full range of human diversity, but we know from our research that many people prefer simple, guided control over too many settings or open-ended options."

People can also adjust how much ChatGPT uses emojis. OpenAI offers granular controls for responses and is experimenting with the ability to make the models more concise, warm or scannable.

Saving a rollout

OpenAI’s GPT-5 rollout was…less than perfect. While company executives, including CEO Sam Altman, touted the new model’s capabilities, a decision to initially sunset older and beloved models on ChatGPT was met with dissatisfaction. Worse yet, many early adopters found that GPT-5 didn’t perform better than older options in domains such as math, science and writing. 

This led Altman to walk back some of his statements around model removal, blaming performance issues on GPT-5’s router. The router, which automatically directs queries to the most suited models, is not going away, as GPT-5.1 Auto will route prompts to the model type that can best answer queries.  

OpenAI is careful to note that GPT-5 models Instant, Thinking and Pro are still available in ChatGPT’s model dropdown, although paid subscribers only have three months to compare these older versions with the 5.1 update. The sunset period for GPT-5, however, will not impact models like GPT-4o.

“Going forward, when we introduce new ChatGPT models, our approach is to give people ample space to evaluate what’s changed and share feedback, allowing us to continue innovating our frontier models while transitioning smoothly,” the company said. “Sunset periods will be communicated clearly and with plenty of advance notice.”

The companies will start with new data center sites in Texas and New York, with more builds to come.

Immersion cooling technology promises to address BESS thermal challenges, but adoption hurdles remain for widespread deployment in data centers.

The 700,000 sq.ft data center will be located in Beaver Dam, north of Madison and Milwaukee.