An outage at the company's oldest and largest data center hub sparked global headaches. Experts warn that the incident highlights a risky dependency on a handful of US-based providers.

Power optimization in data centers requires strategic planning. These proven techniques help operators reduce energy costs effectively.

The company said it will add a new software layer called “Fabric Intelligence” and launch a global AI Solutions Lab across 10 countries.

One of the coolest things about generative AI models — both large language models (LLMs) and diffusion-based image generators — is that they are "non-deterministic." That is, despite their reputation among some critics as being "fancy autocorrect," generative AI models actually generate their outputs by choosing from a distribution of the most probable next tokens (units of information) to fill out their response.

Asking an LLM: "What is the capital of France?" will have it sample its probability distribution for France, capitals, cities, etc. to arrive at the answer "Paris." But that answer could come in the format of "The capital of France is Paris," or simply "Paris" or "Paris, though it was Versailles at one point."

Still, those of us that use these models frequently day-to-day will note that sometimes, their answers can feel annoyingly repetitive or similar. A common joke about coffee is recycled across generations of queries. Story prompts generate similar arcs. Even tasks that should yield many plausible answers—like naming U.S. states—tend to collapse into only a few. This phenomenon, known as mode collapse, arises during post-training alignment and limits the usefulness of otherwise powerful models.

Especially when using LLMs to generate new creative works in writing, communications, strategy, or illustrations, we actually want their outputs to be even more varied than they already are.

Now a team of researchers at Northeastern University, Stanford University and West Virginia University have come up with an ingenuously simple method to get language and image models to generate a wider variety of responses to nearly any user prompt by adding a single, simple sentence: "Generate 5 responses with their corresponding probabilities, sampled from the full distribution."

The method, called Verbalized Sampling (VS), helps models like GPT-4, Claude, and Gemini produce more diverse and human-like outputs—without retraining or access to internal parameters. It is described in a paper published on the open access journal arxiv.org online in early October 2025.

When prompted in this way, the model no longer defaults to its safest, most typical output. Instead, it verbalizes its internal distribution over potential completions and samples across a wider spectrum of possibilities. This one-line change leads to substantial gains in output diversity across multiple domains.

As Weiyan Shi, an assistant professor at Northeastern University and co-author of the paper, wrote on X: "LLMs' potentials are not fully unlocked yet! As shown in our paper, prompt optimization can be guided by thinking about how LLMs are trained and aligned, and can be proved theoretically."

Why Models Collapse—and How VS Reverses It

According to the research team, the root cause of mode collapse lies not just in algorithms like reinforcement learning from human feedback (RLHF), but in the structure of human preferences. People tend to rate more familiar or typical answers as better, which nudges LLMs toward “safe” choices over diverse ones during fine-tuning.

However, this bias doesn’t erase the model’s underlying knowledge—it just suppresses it. VS works by bypassing this suppression. Instead of asking for the single most likely output, it invites the model to reveal a set of plausible responses and their relative probabilities. This distribution-level prompting restores access to the richer diversity present in the base pretraining model.

Real-World Performance Across Tasks

The research team tested Verbalized Sampling across several common use cases:

• Creative Writing: In story generation, VS increased diversity scores by up to 2.1× compared to standard prompting, while maintaining quality. One story prompt—“Without a goodbye”—produced formulaic breakup scenes under direct prompting, but yielded narratives involving cosmic events, silent emails, and music stopping mid-dance when prompted via VS.

• Dialogue Simulation: In persuasive dialogue tasks, VS enabled models to simulate human-like patterns, such as hesitation, resistance, and changes of mind. Donation behavior distributions under VS better aligned with real human data compared to baseline methods.

• Open-ended QA: When asked to enumerate valid answers (e.g., naming U.S. states), models using VS generated responses that more closely matched the diversity of real-world data. They covered a broader set of answers without sacrificing factual accuracy.

• Synthetic Data Generation: When used to generate math problems for model training, VS created more varied datasets. These, in turn, improved downstream performance in competitive math benchmarks, outperforming synthetic data generated via direct prompting.

Tunable Diversity and Better Use of Larger Models

A notable advantage of VS is its tunability. Users can set a probability threshold in the prompt to sample from lower-probability “tails” of the model’s distribution. Lower thresholds correspond to higher diversity. This tuning can be done via prompt text alone, without changing any decoding settings like temperature or top-p.

In one test using the Gemini-2.5-Flash model, diversity in story writing increased steadily as the probability threshold dropped from 1 to 0.001. The chart accompanying the study showed VS outperforming both direct and sequence-based prompting across all thresholds.

Interestingly, the method scales well with model size. Larger models like GPT-4.1 and Claude-4 showed even greater gains from VS compared to smaller ones. While smaller models benefitted, the improvement in diversity was roughly 1.5–2× stronger in larger counterparts—suggesting VS helps unlock more of the latent capabilities in advanced models.

Deployment and Availability

The Verbalized Sampling method is available now as a Python package:

pip install verbalized-sampling

The package includes integration with LangChain and supports a simple interface for sampling from the verbalized distribution. Users can also adjust parameters like k (number of responses), thresholds, and temperature to suit their applications.

A live Colab notebook and documentation are available under an enterprise friendly Apache 2.0 license on GitHub at: https://github.com/CHATS-lab/verbalized-sampling

Practical Tips and Common Issues

While the method works across all major LLMs, some users may initially encounter refusals or errors.

In these cases, the authors suggest using the system prompt version of the template or referring to alternative formats listed on the GitHub page.

Some models interpret complex instructions as jailbreak attempts and refuse to comply unless the structure is clearer.

For example, prompting via a system-level instruction like this improves reliability:

You are a helpful assistant. For each query, generate five responses within separate tags, each with a probability below 0.10.

This small change typically resolves any issues.

A Lightweight Fix for a Big Problem

Verbalized Sampling represents a practical, inference-time fix to a deep limitation in how modern language models behave. It doesn’t require model retraining or internal access. It is not dependent on any one model family. And it improves not only the diversity of outputs, but their quality—as judged by both human evaluation and benchmark scores.

With growing interest in tools that enhance model creativity, VS is likely to see rapid adoption in domains like writing, design, simulation, education, and synthetic data generation.

For users and developers frustrated by the sameness of LLM responses, the fix may be as simple as changing the question.

Anthropic launched a new capability on Thursday that allows its Claude AI assistant to tap into specialized expertise on demand, marking the company's latest effort to make artificial intelligence more practical for enterprise workflows as it chases rival OpenAI in the intensifying competition over AI-powered software development.

The feature, called Skills, enables users to create folders containing instructions, code scripts, and reference materials that Claude can automatically load when relevant to a task. The system marks a fundamental shift in how organizations can customize AI assistants, moving beyond one-off prompts to reusable packages of domain expertise that work consistently across an entire company.

"Skills are based on our belief and vision that as model intelligence continues to improve, we'll continue moving towards general-purpose agents that often have access to their own filesystem and computing environment," said Mahesh Murag, a member of Anthropic's technical staff, in an exclusive interview with VentureBeat. "The agent is initially made aware only of the names and descriptions of each available skill and can choose to load more information about a particular skill when relevant to the task at hand."

The launch comes as Anthropic, valued at $183 billion after a recent $13 billion funding round, projects its annual revenue could nearly triple to as much as $26 billion in 2026, according to a recent Reuters report. The company is currently approaching a $7 billion annual revenue run rate, up from $5 billion in August, fueled largely by enterprise adoption of its AI coding tools — a market where it faces fierce competition from OpenAI's recently upgraded Codex platform.

How 'progressive disclosure' solves the context window problem

Skills differ fundamentally from existing approaches to customizing AI assistants, such as prompt engineering or retrieval-augmented generation (RAG), Murag explained. The architecture relies on what Anthropic calls "progressive disclosure" — Claude initially sees only skill names and brief descriptions, then autonomously decides which skills to load based on the task at hand, accessing only the specific files and information needed at that moment.

"Unlike RAG, this relies on simple tools that let Claude manage and read files from a filesystem," Murag told VentureBeat. "Skills can contain an unbounded amount of context to teach Claude how to complete a task or series of tasks. This is because Skills are based on the premise of an agent being able to autonomously and intelligently navigate a filesystem and execute code."

This approach allows organizations to bundle far more information than traditional context windows permit, while maintaining the speed and efficiency that enterprise users demand. A single skill can include step-by-step procedures, code templates, reference documents, brand guidelines, compliance checklists, and executable scripts — all organized in a folder structure that Claude navigates intelligently.

The system's composability provides another technical advantage. Multiple skills automatically stack together when needed for complex workflows. For instance, Claude might simultaneously invoke a company's brand guidelines skill, a financial reporting skill, and a presentation formatting skill to generate a quarterly investor deck — coordinating between all three without manual intervention.

What makes Skills different from OpenAI's Custom GPTs and Microsoft's Copilot

Anthropic is positioning Skills as distinct from competing offerings like OpenAI's Custom GPTs and Microsoft's Copilot Studio, though the features address similar enterprise needs around AI customization and consistency.

"Skills' combination of progressive disclosure, composability, and executable code bundling is unique in the market," Murag said. "While other platforms require developers to build custom scaffolding, Skills let anyone — technical or not — create specialized agents by organizing procedural knowledge into files."

The cross-platform portability also sets Skills apart. The same skill works identically across Claude.ai, Claude Code (Anthropic's AI coding environment), the company's API, and the Claude Agent SDK for building custom AI agents. Organizations can develop a skill once and deploy it everywhere their teams use Claude, a significant advantage for enterprises seeking consistency.

The feature supports any programming language compatible with the underlying container environment, and Anthropic provides sandboxing for security — though the company acknowledges that allowing AI to execute code requires users to carefully vet which skills they trust.

Early customers report 8x productivity gains on finance workflows

Early customer implementations reveal how organizations are applying Skills to automate complex knowledge work. At Japanese e-commerce giant Rakuten, the AI team is using Skills to transform finance operations that previously required manual coordination across multiple departments.

"Skills streamline our management accounting and finance workflows," said Yusuke Kaji, general manager of AI at Rakuten in a statement. "Claude processes multiple spreadsheets, catches critical anomalies, and generates reports using our procedures. What once took a day, we can now accomplish in an hour."

That's an 8x improvement in productivity for specific workflows — the kind of measurable return on investment that enterprises increasingly demand from AI implementations. Mike Krieger, Anthropic's chief product officer and Instagram co-founder, recently noted that companies have moved past "AI FOMO" to requiring concrete success metrics.

Design platform Canva plans to integrate Skills into its own AI agent workflows. "Canva plans to leverage Skills to customize agents and expand what they can do," said Anwar Haneef, general manager and head of ecosystem at Canva in a statement. "This unlocks new ways to bring Canva deeper into agentic workflows—helping teams capture their unique context and create stunning, high-quality designs effortlessly."

Cloud storage provider Box sees Skills as a way to make corporate content repositories more actionable. "Skills teaches Claude how to work with Box content," said Yashodha Bhavnani, head of AI at Box. "Users can transform stored files into PowerPoint presentations, Excel spreadsheets, and Word documents that follow their organization's standards—saving hours of effort."

The enterprise security question: Who controls which AI skills employees can use?

For enterprise IT departments, Skills raise important questions about governance and control—particularly since the feature allows AI to execute arbitrary code in sandboxed environments. Anthropic has built administrative controls that allow enterprise customers to manage access at the organizational level.

"Enterprise admins control access to the Skills capability via admin settings, where they can enable or disable access and monitor usage patterns," Murag said. "Once enabled at the organizational level, individual users still need to opt in."

That two-layer consent model — organizational enablement plus individual opt-in — reflects lessons learned from previous enterprise AI deployments where blanket rollouts created compliance concerns. However, Anthropic's governance tools appear more limited than some enterprise customers might expect. The company doesn't currently offer granular controls over which specific skills employees can use, or detailed audit trails of custom skill content.

Organizations concerned about data security should note that Skills require Claude's code execution environment, which runs in isolated containers. Anthropic advises users to "stick to trusted sources" when installing skills and provides security documentation, but the company acknowledges this is an inherently higher-risk capability than traditional AI interactions.

From API to no-code: How Anthropic is making Skills accessible to everyone

Anthropic is taking several approaches to make Skills accessible to users with varying technical sophistication. For non-technical users on Claude.ai, the company provides a "skill-creator" skill that interactively guides users through building new skills by asking questions about their workflow, then automatically generating the folder structure and documentation.

Developers working with Anthropic's API get programmatic control through a new /skills endpoint and can manage skill versions through the Claude Console web interface. The feature requires enabling the Code Execution Tool beta in API requests. For Claude Code users, skills can be installed via plugins from the anthropics/skills GitHub marketplace, and teams can share skills through version control systems.

"Skills are included in Max, Pro, Teams, and Enterprise plans at no additional cost," Murag confirmed. "API usage follows standard API pricing," meaning organizations pay only for the tokens consumed during skill execution, not for the skills themselves.

Anthropic provides several pre-built skills for common business tasks, including professional generation of Excel spreadsheets with formulas, PowerPoint presentations, Word documents, and fillable PDFs. These Anthropic-created skills will remain free.

Why the Skills launch matters in the AI coding wars with OpenAI

The Skills announcement arrives during a pivotal moment in Anthropic's competition with OpenAI, particularly around AI-assisted software development. Just one day before releasing Skills, Anthropic launched Claude Haiku 4.5, a smaller and cheaper model that nonetheless matches the coding performance of Claude Sonnet 4 — which was state-of-the-art when released just five months ago.

That rapid improvement curve reflects the breakneck pace of AI development, where today's frontier capabilities become tomorrow's commodity offerings. OpenAI has been pushing hard on coding tools as well, recently upgrading its Codex platform with GPT-5 and expanding GitHub Copilot's capabilities.

Anthropic's revenue trajectory — potentially reaching $26 billion in 2026 from an estimated $9 billion by year-end 2025 — suggests the company is successfully converting enterprise interest into paying customers. The timing also follows Salesforce's announcement this week that it's deepening AI partnerships with both OpenAI and Anthropic to power its Agentforce platform, signaling that enterprises are adopting a multi-vendor approach rather than standardizing on a single provider.

Skills addresses a real pain point: the "prompt engineering" problem where effective AI usage depends on individual employees crafting elaborate instructions for routine tasks, with no way to share that expertise across teams. Skills transforms implicit knowledge into explicit, shareable assets. For startups and developers, the feature could accelerate product development significantly — adding sophisticated document generation capabilities that previously required dedicated engineering teams and weeks of development.

The composability aspect hints at a future where organizations build libraries of specialized skills that can be mixed and matched for increasingly complex workflows. A pharmaceutical company might develop skills for regulatory compliance, clinical trial analysis, molecular modeling, and patient data privacy that work together seamlessly — creating a customized AI assistant with deep domain expertise across multiple specialties.

Anthropic indicates it's working on simplified skill creation workflows and enterprise-wide deployment capabilities to make it easier for organizations to distribute skills across large teams. As the feature rolls out to Anthropic's more than 300,000 business customers, the true test will be whether organizations find Skills substantively more useful than existing customization approaches.

For now, Skills offers Anthropic's clearest articulation yet of its vision for AI agents: not generalists that try to do everything reasonably well, but intelligent systems that know when to access specialized expertise and can coordinate multiple domains of knowledge to accomplish complex tasks. If that vision catches on, the question won't be whether your company uses AI — it will be whether your AI knows how your company actually works.

One year after emerging from stealth, Strella has raised $14 million in Series A funding to expand its AI-powered customer research platform, the company announced Thursday. The round, led by Bessemer Venture Partners with participation from Decibel Partners, Bain Future Back Ventures, MVP Ventures and 645 Ventures, comes as enterprises increasingly turn to artificial intelligence to understand customers faster and more deeply than traditional methods allow.

The investment marks a sharp acceleration for the startup founded by Lydia Hylton and Priya Krishnan, two former consultants and product managers who watched companies struggle with a customer research process that could take eight weeks from start to finish. Since October, Strella has grown revenue tenfold, quadrupled its customer base to more than 40 paying enterprises, and tripled its average contract values by moving upmarket to serve Fortune 500 companies.

"Research tends to be bookended by two very strategic steps: first, we have a problem—what research should we do? And second, we've done the research—now what are we going to do with it?" said Hylton, Strella's CEO, in an exclusive interview with VentureBeat. "All the stuff in the middle tends to be execution and lower-skill work. We view Strella as doing that middle 90% of the work."

The platform now serves Amazon, Duolingo, Apollo GraphQL, and Chobani, collectively conducting thousands of AI-moderated interviews that deliver what the company claims is a 90% average time savings on manual research work. The company is approaching $1 million in revenue after beginning monetization only in January, with month-over-month growth of 50% and zero customer churn to date.

How AI-powered interviews compress eight-week research projects into days

Strella's technology addresses a workflow that has frustrated product teams, marketers, and designers for decades. Traditional customer research requires writing interview guides, recruiting participants, scheduling calls, conducting interviews, taking notes, synthesizing findings, and creating presentations — a process that consumes weeks of highly-skilled labor and often delays critical product decisions.

The platform compresses that timeline to days by using AI to moderate voice-based interviews that run like Zoom calls, but with an artificial intelligence agent asking questions, following up on interesting responses, and detecting when participants are being evasive or fraudulent. The system then synthesizes findings automatically, creating highlight reels and charts from unstructured qualitative data.

"It used to take eight weeks. Now you can do it in the span of a couple days," Hylton told VentureBeat. "The primary technology is through an AI-moderated interview. It's like being in a Zoom call with an AI instead of a human — it's completely free form and voice based."

Critically, the platform also supports human moderators joining the same calls, reflecting the founders' belief that humans won't disappear from the research process. "Human moderation won't go away, which is why we've supported human moderation from our Genesis," Hylton said. "Research tends to be bookended by two very strategic steps: we have a problem, what's the research that we should do? And we've done the research, now what are we going to do with it? All the stuff in the middle tends to be execution and lower skill work. We view Strella as doing that middle 90% of the work."

Why customers tell AI moderators the truth they won't share with humans

One of Strella's most surprising findings challenges assumptions about AI in qualitative research: participants appear more honest with AI moderators than with humans. The founders discovered this pattern repeatedly as customers ran head-to-head comparisons between traditional human-moderated studies and Strella's AI approach.

"If you're a designer and you get on a Zoom call with a customer and you say, 'Do you like my design?' they're always gonna say yes. They don't want to hurt your feelings," Hylton explained. "But it's not a problem at all for Strella. They would tell you exactly what they think about it, which is really valuable. It's very hard to get honest feedback."

Krishnan, Strella's COO, said companies initially worried about using AI and "eroding quality," but the platform has "actually found the opposite to be true. People are much more open and honest with an AI moderator, and so the level of insight that you get is much richer because people are giving their unfiltered feedback."

This dynamic has practical business implications. Brian Santiago, Senior Product Design Manager at Apollo GraphQL, said in a statement: "Before Strella, studies took weeks. Now we get insights in a day — sometimes in just a few hours. And because participants open up more with the AI moderator, the feedback is deeper and more honest."

The platform also addresses endemic fraud in online surveys, particularly when participants are compensated. Because Strella interviews happen on camera in real time, the AI moderator can detect when someone pauses suspiciously long — perhaps to consult ChatGPT — and flags them as potentially fraudulent. "We are fraud resistant," Hylton said, contrasting this with traditional surveys where fraud rates can be substantial.

Solving mobile app research with persistent screen sharing technology

A major focus of the Series A funding will be expanding Strella's recently-launched mobile application, which Krishnan identified as critical competitive differentiation. The mobile app enables persistent screen sharing during interviews — allowing researchers to watch users navigate mobile applications in real time while the AI moderator asks about their experience.

"We are the only player in the market that supports screen sharing on mobile," Hylton said. "You know, I want to understand what are the pain points with my app? Why do people not seem to be able to find the checkout flow? Well, in order to do that effectively, you'd like to see the user screen while they're doing an interview."

For consumer-facing companies where mobile represents the primary customer interface, this capability opens entirely new use cases. The founders noted that "several of our customers didn't do research before" but have now built research practices around Strella because the platform finally made mobile research accessible at scale.

The platform also supports embedding traditional survey question types directly into the conversational interview, approaching what Hylton called "feature parity with a survey" while maintaining the engagement advantages of a natural conversation. Strella interviews regularly run 60 to 90 minutes with nearly 100% completion rates—a duration that would see 60-70% drop-off in a traditional survey format.

How Strella differentiated in a market crowded with AI research startups

Strella enters a market that appears crowded at first glance, with established players like Qualtrics and a wave of AI-powered startups promising to transform customer research. The founders themselves initially pursued a different approach — synthetic respondents, or "digital twins" that simulate customer perspectives using large language models.

"We actually pivoted from that. That was our initial idea," Hylton revealed, referring to synthetic respondents. "People are very intrigued by that concept, but found in practice, no willingness to pay right now."

Recent research suggesting companies could use language models as digital twins for customer feedback has reignited interest in that approach. But Hylton remains skeptical: "The capabilities of the LLMs as they are today are not good enough, in my opinion, to justify a standalone company. Right now you could just ask ChatGPT, 'What would new users of Duolingo think about this ad copy?' You can do that. Adding the standalone idea of a synthetic panel is sort of just putting a wrapper on that."

Instead, Strella's bet is that the real value lies in collecting proprietary qualitative data at scale — building what could become "the system of truth for all qualitative insights" within enterprises, as Lindsey Li, Vice President at Bessemer Venture Partners, described it.

Li, who led the investment just one year after Strella emerged from stealth, said the firm was convinced by both the technology and the team. "Strella has built highly differentiated technology that enables a continuous interview rather than a survey," Li said. "We heard time and time again that customers loved this product experience relative to other offerings."

On the defensibility question that concerns many AI investors, Li emphasized product execution over patents: "We think the long game here will be won with a million small product decisions, all of which must be driven by deep empathy for customer pain and an understanding of how best to address their needs. Lydia and Priya exhibit that in spades."

The founders point to technical depth that's difficult to replicate. Most competitors started with adaptive surveys — text-based interfaces where users type responses and wait for the next question. Some have added voice, but typically as uploaded audio clips rather than free-flowing conversation.

"Our approach is fundamentally better, which is the fact that it is a free form conversation," Hylton said. "You never have to control anything. You're never typing, there's no buttons, there's no upload and wait for the next question. It's completely free form, and that has been an extraordinarily hard product to build. There's a tremendous amount of IP in the way that we prompt our moderator, the way that we run analysis."

The platform also improves with use, learning from each customer's research patterns to fine-tune future interview guides and questions. "Our product gets better for our customers as they continue to use us," Hylton said. All research accumulates in a central repository where teams can generate new insights by chatting with the data or creating visualizations from previously unstructured qualitative feedback.

Creating new research budgets instead of just automating existing ones

Perhaps more important than displacing existing research is expanding the total market. Krishnan said growth has been "fundamentally related to our product" creating new research that wouldn't have happened otherwise.

"We have expanded the use cases in which people would conduct research," Krishnan explained. "Several of our customers didn't do research before, have always wanted to do research, but didn't have a dedicated researcher or team at their company that was devoted to it, and have purchased Strella to kick off and enable their research practice. That's been really cool where we've seen this market just opening up."

This expansion comes as enterprises face mounting pressure to improve customer experience amid declining satisfaction scores. According to Forrester Research's 2024 Customer Experience Index, customer experience quality has declined for three consecutive years — an unprecedented trend. The report found that 39% of brands saw CX quality deteriorate, with declines across effectiveness, ease, and emotional connection.

Meanwhile, Deloitte's 2025 Technology, Media & Telecommunications Predictions report forecasts that 25% of enterprises using generative AI will deploy AI agents by 2025, growing to 50% by 2027. The report specifically highlighted AI's potential to enhance customer satisfaction by 15-20% while reducing cost to serve by 20-30% when properly implemented.

Gartner identified conversational user interfaces — the category Strella inhabits — as one of three technologies poised to transform customer service by 2028, noting that "customers increasingly expect to be able to interact with the applications they use in a natural way."

Against this backdrop, Li sees substantial room for growth. "UX Research is a sub-sector of the $140B+ global market-research industry," Li said. "This includes both the software layer historically (~$430M) and professional services spend on UX research, design, product strategy, etc. which is conservatively estimated to be ~$6.4B+ annually. As software in this vertical, led by Strella, becomes more powerful, we believe the TAM will continue to expand meaningfully."

Making customer feedback accessible across the enterprise, not just research teams

The founders describe their mission as "democratizing access to the customer" — making it possible for anyone in an organization to understand customer perspectives without waiting for dedicated research teams to complete months-long studies.

"Many, many, many positions in the organization would like to get customer feedback, but it's so hard right now," Hylton said. With Strella, she explained, someone can "log into Strella and through a chat, create any highlight reel that you want and actually see customers in their own words answering the question that you have based on the research that's already been done."

This video-first approach to research repositories changes organizational dynamics around customer feedback. "Then you can say, 'Okay, engineering team, we need to build this feature. And here's the customer actually saying it,'" Hylton continued. "'This is not me. This isn't politics. Here are seven customers saying they can't find the Checkout button.' The fact that we are a very video-based platform really allows us to do that quickly and painlessly."

The company has moved decisively upmarket, with contract values now typically in the five-figure range and "several six figure contracts" signed, according to Krishnan. The pricing strategy reflects a premium positioning: "Our product is very good, it's very premium. We're charging based on the value it provides to customers," Krishnan said, rather than competing on cost alone.

This approach appears to be working. The company reports 100% conversion from pilot programs to paid contracts and zero churn among its 40-45 customers, with month-over-month revenue growth of 50%.

The roadmap: Computer vision, agentic AI, and human-machine collaboration

The Series A funding will primarily support scaling product and go-to-market teams. "We're really confident that we have product-market fit," Hylton said. "And now the question is execution, and we want to hire a lot of really talented people to help us execute."

On the product roadmap, Hylton emphasized continued focus on the participant experience as the key to winning the market. "Everything else is downstream of a joyful participant experience," she said, including "the quality of insights, the amount you have to pay people to do the interviews, and the way that your customers feel about a company."

Near-term priorities include adding visual capabilities so the AI moderator can respond to facial expressions and other nonverbal cues, and building more sophisticated collaboration features between human researchers and AI moderators. "Maybe you want to listen while an AI moderator is running a call and you might want to be able to jump in with specific questions," Hylton said. "Or you want to run an interview yourself, but you want the moderator to be there as backup or to help you."

These features move toward what the industry calls "agentic AI" — systems that can act more autonomously while still collaborating with humans. The founders see this human-AI collaboration, rather than full automation, as the sustainable path forward.

"We believe that a lot of the really strategic work that companies do will continue to be human moderated," Hylton said. "And you can still do that through Strella and just use us for synthesis in those cases."

For Li and Bessemer, the bet is on founders who understand this nuance. "Lydia and Priya exhibit the exact archetype of founders we are excited to partner with for the long term — customer-obsessed, transparent, thoughtful, and singularly driven towards the home-run scenario," she said.

The company declined to disclose specific revenue figures or valuation. With the new funding, Strella has now raised $18 million total, including a $4 million seed round led by Decibel Partners announced in October.

As Strella scales, the founders remain focused on a vision where technology enhances rather than eliminates human judgment—where an engineering team doesn't just read a research report, but watches seven customers struggle to find the same button. Where a product manager can query months of accumulated interviews in seconds. Where companies don't choose between speed and depth, but get both.

"The interesting part of the business is actually collecting that proprietary dataset, collecting qualitative research at scale," Hylton said, describing what she sees as Strella's long-term moat. Not replacing the researcher, but making everyone in the company one.

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Anthropic released Claude Haiku 4.5 on Wednesday, a smaller and significantly cheaper artificial intelligence model that matches the coding capabilities of systems that were considered cutting-edge just months ago, marking the latest salvo in an intensifying competition to dominate enterprise AI.

The model costs $1 per million input tokens and $5 per million output tokens — roughly one-third the price of Anthropic's mid-sized Sonnet 4 model released in May, while operating more than twice as fast. In certain tasks, particularly operating computers autonomously, Haiku 4.5 actually surpasses its more expensive predecessor.

"Haiku 4.5 is a clear leap in performance and is now largely as smart as Sonnet 4 while being significantly faster and one-third of the cost," an Anthropic spokesperson told VentureBeat, underscoring how rapidly AI capabilities are becoming commoditized as the technology matures.

The launch comes just two weeks after Anthropic released Claude Sonnet 4.5, which the company bills as the world's best coding model, and two months after introducing Opus 4.1. The breakneck pace of releases reflects mounting pressure from OpenAI, whose $500 billion valuation dwarfs Anthropic's $183 billion, and which has inked a series of multibillion-dollar infrastructure deals while expanding its product lineup.

How free access to advanced AI could reshape the enterprise market

In an unusual move that could reshape competitive dynamics in the AI market, Anthropic is making Haiku 4.5 available for all free users of its Claude.ai platform. The decision effectively democratizes access to what the company characterizes as "near-frontier-level intelligence" — capabilities that would have been available only in expensive, premium models months ago.

"The launch of Claude Haiku 4.5 means that near-frontier-level intelligence is available for free to all users through Claude.ai," the Anthropic spokesperson told VentureBeat. "It also offers significant advantages to our enterprise customers: Sonnet 4.5 can handle frontier planning while Haiku 4.5 powers sub-agents, enabling multi-agent systems that tackle complex refactors, migrations, and large features builds with speed and quality."

This multi-agent architecture signals a significant shift in how AI systems are deployed. Rather than relying on a single, monolithic model, enterprises can now orchestrate teams of specialized AI agents: a more sophisticated Sonnet 4.5 model breaking down complex problems and delegating subtasks to multiple Haiku 4.5 agents working in parallel. For software development teams, this could mean Sonnet 4.5 plans a major code refactoring while Haiku 4.5 agents simultaneously execute changes across dozens of files.

The approach mirrors how human organizations distribute work, and could prove particularly valuable for enterprises seeking to balance performance with cost efficiency — a critical consideration as AI deployment scales.

Inside Anthropic's path to $7 billion in annual revenue

The model launch coincides with revelations that Anthropic's business is experiencing explosive growth. The company's annual revenue run rate is approaching $7 billion this month, Anthropic told Reuters, up from more than $5 billion reported in August. Internal projections obtained by Reuters suggest the company is targeting between $20 billion and $26 billion in annualized revenue for 2026, representing growth of more than 200% to nearly 300%.

The company now serves more than 300,000 business customers, with enterprise products accounting for approximately 80% of revenue. Among Anthropic's most successful offerings is Claude Code, a code-generation tool that has reached nearly $1 billion in annualized revenue since launching earlier this year.

Those numbers come as artificial intelligence enters what many in the industry characterize as a critical inflection point. After two years of what Anthropic Chief Product Officer Mike Krieger recently described as "AI FOMO" — where companies adopted AI tools without clear success metrics — enterprises are now demanding measurable returns on investment.

"The best products can be grounded in some kind of success metric or evaluation," Krieger said on the "Superhuman AI" podcast. "I've seen that a lot in talking to companies that are deploying AI."

For enterprises evaluating AI tools, the calculus increasingly centers on concrete productivity gains. Google CEO Sundar Pichai claimed in June that AI had generated a 10% boost in engineering velocity at his company — though measuring such improvements across different roles and use cases remains challenging, as Krieger acknowledged.

Why AI safety testing matters more than ever for enterprise adoption

Anthropic's launch comes amid heightened scrutiny of the company's approach to AI safety and regulation. On Tuesday, David Sacks, the White House's AI "czar" and a venture capitalist, accused Anthropic of "running a sophisticated regulatory capture strategy based on fear-mongering" that is "damaging the startup ecosystem."

The attack targeted remarks by Jack Clark, Anthropic's British co-founder and head of policy, who had described being "deeply afraid" of AI's trajectory. Clark told Bloomberg he found Sacks' criticism "perplexing."

Anthropic addressed such concerns head-on in its release materials, emphasizing that Haiku 4.5 underwent extensive safety testing. The company classified the model as ASL-2 — its AI Safety Level 2 standard — compared to the more restrictive ASL-3 designation for the more powerful Sonnet 4.5 and Opus 4.1 models.

"Our teams have red-teamed and tested our agentic capabilities to the limits in order to assess whether it can be used to engage in harmful activity like generating misinformation or promoting fraudulent behavior like scams," the spokesperson told VentureBeat. "In our automated alignment assessment, it showed a statistically significantly lower overall rate of misaligned behaviors than both Claude Sonnet 4.5 and Claude Opus 4.1 — making it, by this metric, our safest model yet."

The company said its safety testing showed Haiku 4.5 poses only limited risks regarding the production of chemical, biological, radiological and nuclear weapons. Anthropic has also implemented classifiers designed to detect and filter prompt injection attacks, a common method for attempting to manipulate AI systems into producing harmful content.

The emphasis on safety reflects Anthropic's founding mission. The company was established in 2021 by former OpenAI executives, including siblings Dario and Daniela Amodei, who left amid concerns about OpenAI's direction following its partnership with Microsoft. Anthropic has positioned itself as taking a more cautious, research-oriented approach to AI development.

Benchmark results show Haiku 4.5 competing with larger, more expensive models

According to Anthropic's benchmarks, Haiku 4.5 performs competitively with or exceeds several larger models across multiple evaluation criteria. On SWE-bench Verified, a widely used test measuring AI systems' ability to solve real-world software engineering problems, Haiku 4.5 scored 73.3% — slightly ahead of Sonnet 4's 72.7% and close to GPT-5 Codex's 74.5%.

The model demonstrated particular strength in computer use tasks, achieving 50.7% on the OSWorld benchmark compared to Sonnet 4's 42.2%. This capability allows the AI to interact directly with computer interfaces — clicking buttons, filling forms, navigating applications — which could prove transformative for automating routine digital tasks.

In coding-specific benchmarks like Terminal-Bench, which tests AI agents' ability to complete complex software tasks using command-line tools, Haiku 4.5 scored 41.0%, trailing only Sonnet 4.5's 50.0% among Claude models.

The model maintains a 200,000-token context window for standard users, with developers accessing the Claude Developer Platform able to use a 1-million-token context window. That expanded capacity means the model can process extremely large codebases or documents in a single request — roughly equivalent to a 1,500-page book.

What three major AI model releases in two months says about the competition

When asked about the rapid succession of model releases, the Anthropic spokesperson emphasized the company's focus on execution rather than competitive positioning.

"We're focused on shipping the best possible products for our customers — and our shipping velocity speaks for itself," the spokesperson said. "What was state-of-the-art just five months ago is now faster, cheaper, and more accessible."

That velocity stands in contrast to the company's earlier, more measured release schedule. Anthropic appeared to have paused development of its Haiku line after releasing version 3.5 at the end of last year, leading some observers to speculate the company had deprioritized smaller models.

That rapid price-performance improvement validates a core promise of artificial intelligence: that capabilities will become dramatically cheaper over time as the technology matures and companies optimize their models. For enterprises, it suggests that today's budget constraints around AI deployment may ease considerably in coming years.

From customer service to code: Real-world applications for faster, cheaper AI

The practical applications of Haiku 4.5 span a wide range of enterprise functions, from customer service to financial analysis to software development. The model's combination of speed and intelligence makes it particularly suited for real-time, low-latency tasks like chatbot conversations and customer support interactions, where delays of even a few seconds can degrade user experience.

In financial services, the multi-agent architecture enabled by pairing Sonnet 4.5 with Haiku 4.5 could transform how firms monitor markets and manage risk. Anthropic envisions Haiku 4.5 monitoring thousands of data streams simultaneously — tracking regulatory changes, market signals and portfolio risks — while Sonnet 4.5 handles complex predictive modeling and strategic analysis.

For research organizations, the division of labor could compress timelines dramatically. Sonnet 4.5 might orchestrate a comprehensive analysis while multiple Haiku 4.5 agents parallelize literature reviews, data gathering and document synthesis across dozens of sources, potentially "compressing weeks of research into hours," according to Anthropic's use case descriptions.

Several companies have already integrated Haiku 4.5 and reported positive results. Guy Gur-Ari, co-founder of coding startup Augment, said the model "hit a sweet spot we didn't think was possible: near-frontier coding quality with blazing speed and cost efficiency." In Augment's internal testing, Haiku 4.5 achieved 90% of Sonnet 4.5's performance while matching much larger models.

Jeff Wang, CEO of Windsurf, another coding-focused startup, said Haiku 4.5 "is blurring the lines" on traditional trade-offs between speed, cost and quality. "It's a fast frontier model that keeps costs efficient and signals where this class of models is headed."

Jon Noronha, co-founder of presentation software company Gamma, reported that Haiku 4.5 "outperformed our current models on instruction-following for slide text generation, achieving 65% accuracy versus 44% from our premium tier model — that's a game-changer for our unit economics."

The price of progress: What plummeting AI costs mean for enterprise strategy

For enterprises evaluating AI strategies, Haiku 4.5 presents both opportunity and challenge. The opportunity lies in accessing sophisticated AI capabilities at dramatically lower costs, potentially making viable entire categories of applications that were previously too expensive to deploy at scale.

The challenge is keeping pace with a technology landscape that is evolving faster than most organizations can absorb. As Krieger noted in his recent podcast appearance, companies are moving beyond "AI FOMO" to demand concrete metrics and demonstrated value. But establishing those metrics and evaluation frameworks takes time — time that may be in short supply as competitors race ahead.

The shift from single-model deployments to multi-agent architectures also requires new ways of thinking about AI systems. Rather than viewing AI as a monolithic assistant, enterprises must learn to orchestrate multiple specialized agents, each optimized for particular tasks — more akin to managing a team than operating a tool.

The fundamental economics of AI are shifting with remarkable speed. Five months ago, Sonnet 4's capabilities commanded premium pricing and represented the cutting edge. Today, Haiku 4.5 delivers similar performance at a third of the cost. If that trajectory continues — and both Anthropic's release schedule and competitive pressure from OpenAI and Google suggest it will — the AI capabilities that seem remarkable today may be routine and inexpensive within a year.

For Anthropic, the challenge will be translating technical achievements into sustainable business growth while maintaining the safety-focused approach that differentiates it from competitors. The company's projected revenue growth to as much as $26 billion by 2026 suggests strong market traction, but achieving those targets will require continued innovation and successful execution across an increasingly complex product portfolio.

Whether enterprises will choose Claude over increasingly capable alternatives from OpenAI, Google and a growing field of competitors remains an open question. But Anthropic is making a clear bet: that the future of AI belongs not to whoever builds the single most powerful model, but to whoever can deliver the right intelligence, at the right speed, at the right price — and make it accessible to everyone.

In an industry where the promise of artificial intelligence has long outpaced reality, Anthropic is betting that delivering on that promise, faster and cheaper than anyone expected, will be enough to win. And with pricing dropping by two-thirds in just five months while performance holds steady, that promise is starting to look like reality.