Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking research released Thursday by Upwork, the largest online work marketplace.

But the same study reveals a more promising path forward: When AI agents collaborate with human experts, project completion rates surge by up to 70%, suggesting the future of work may not pit humans against machines but rather pair them together in powerful new ways.

The findings, drawn from more than 300 real client projects posted to Upwork's platform, marking the first systematic evaluation of how human expertise amplifies AI agent performance in actual professional work — not synthetic tests or academic simulations. The research challenges both the hype around fully autonomous AI agents and fears that such technology will imminently replace knowledge workers.

"AI agents aren't that agentic, meaning they aren't that good," Andrew Rabinovich, Upwork's chief technology officer and head of AI and machine learning, said in an exclusive interview with VentureBeat. "However, when paired with expert human professionals, project completion rates improve dramatically, supporting our firm belief that the future of work will be defined by humans and AI collaborating to get more work done, with human intuition and domain expertise playing a critical role."

How AI agents performed on 300+ real freelance jobs—and why they struggled

Upwork's Human+Agent Productivity Index (HAPI) evaluated how three leading AI systems — Gemini 2.5 Pro, OpenAI's GPT-5, and Claude Sonnet 4 — performed on actual jobs posted by paying clients across categories including writing, data science, web development, engineering, sales, and translation.

Critically, Upwork deliberately selected simple, well-defined projects where AI agents stood a reasonable chance of success. These jobs, priced under $500, represent less than 6% of Upwork's total gross services volume — a tiny fraction of the platform's overall business and an acknowledgment of current AI limitations.

"The reality is that although we study AI, and I've been doing this for 25 years, and we see significant breakthroughs, the reality is that these agents aren't that agentic," Rabinovich told VentureBeat. "So if we go up the value chain, the problems become so much more difficult, then we don't think they can solve them at all, even to scratch the surface. So we specifically chose simpler tasks that would give an agent some kind of traction."

Even on these deliberately simplified tasks, AI agents working independently struggled. But when expert freelancers provided feedback — spending an average of just 20 minutes per review cycle — the agents' performance improved substantially with each iteration.

20 minutes of human feedback boosted AI completion rates up to 70%

The research reveals stark differences in how AI agents perform with and without human guidance across different types of work. For data science and analytics projects, Claude Sonnet 4 achieved a 64% completion rate working alone but jumped to 93% after receiving feedback from a human expert. In sales and marketing work, Gemini 2.5 Pro's completion rate rose from 17% independently to 31% with human input. OpenAI's GPT-5 showed similarly dramatic improvements in engineering and architecture tasks, climbing from 30% to 50% completion.

The pattern held across virtually all categories, with agents responding particularly well to human feedback on qualitative, creative work requiring editorial judgment — areas like writing, translation, and marketing — where completion rates increased by up to 17 percentage points per feedback cycle.

The finding challenges a fundamental assumption in the AI industry: that agent benchmarks conducted in isolation accurately predict real-world performance.

"While we show that in the tasks that we have selected for agents to perform in isolation, they perform similarly to the previous results that we've seen published openly, what we've shown is that in collaboration with humans, the performance of these agents improves surprisingly well," Rabinovich said. "It's not just a one-turn back and forth, but the more feedback the human provides, the better the agent gets at performing."

Why ChatGPT can ace the SAT but can't count the R's in 'strawberry'

The research arrives as the AI industry grapples with a measurement crisis. Traditional benchmarks — standardized tests that AI models can master, sometimes scoring perfectly on SAT exams or mathematics olympiads — have proven poor predictors of real-world capability.

"With advances of large language models, what we're now seeing is that these static, academic datasets are completely saturated," Rabinovich said. "So you could get a perfect score in the SAT test or LSAT or any of the math olympiads, and then you would ask ChatGPT how many R's there are in the word strawberry, and it would get it wrong."

This phenomenon — where AI systems ace formal tests but stumble on trivial real-world questions — has led to growing skepticism about AI capabilities, even as companies race to deploy autonomous agents. Several recent benchmarks from other firms have tested AI agents on Upwork jobs, but those evaluations measured only isolated performance, not the collaborative potential that Upwork's research reveals.

"We wanted to evaluate the quality of these agents on actual real work with economic value associated with it, and not only see how well these agents do, but also see how these agents do in collaboration with humans, because we sort of knew already that in isolation, they're not that advanced," Rabinovich explained.

For Upwork, which connects roughly 800,000 active clients posting more than 3 million jobs annually to a global pool of freelancers, the research serves a strategic business purpose: establishing quality standards for AI agents before allowing them to compete or collaborate with human workers on its platform.

The economics of human-AI teamwork: Why paying for expert feedback still saves money

Despite requiring multiple rounds of human feedback — each lasting about 20 minutes — the time investment remains "orders of magnitude different between a human doing the work alone, versus a human doing the work with an AI agent," Rabinovich said. Where a project might take a freelancer days to complete independently, the agent-plus-human approach can deliver results in hours through iterative cycles of automated work and expert refinement.

The economic implications extend beyond simple time savings. Upwork recently reported that gross services volume from AI-related work grew 53% year-over-year in the third quarter of 2025, one of the strongest growth drivers for the company. But executives have been careful to frame AI not as a replacement for freelancers but as an enhancement to their capabilities.

"AI was a huge overhang for our valuation," Erica Gessert, Upwork's CFO, told CFO Brew in October. "There was this belief that all work was going to go away. AI was going to take it, and especially work that's done by people like freelancers, because they are impermanent. Actually, the opposite is true."

The company's strategy centers on enabling freelancers to handle more complex, higher-value work by offloading routine tasks to AI. "Freelancers actually prefer to have tools that automate the manual labor and repetitive part of their work, and really focus on the creative and conceptual part of the process," Rabinovich said.

Rather than replacing jobs, he argues, AI will transform them: "Simpler tasks will be automated by agents, but the jobs will become much more complex in the number of tasks, so the amount of work and therefore earnings for freelancers will actually only go up."

AI coding agents excel, but creative writing and translation still need humans

The research reveals a clear pattern in agent capabilities. AI systems perform best on "deterministic and verifiable" tasks with objectively correct answers, like solving math problems or writing basic code. "Most coding tasks are very similar to each other," Rabinovich noted. "That's why coding agents are becoming so good."

In Upwork's tests, web development, mobile app development, and data science projects — especially those involving structured, computational work — saw the highest standalone agent completion rates. Claude Sonnet 4 completed 68% of web development jobs and 64% of data science projects without human help, while Gemini 2.5 Pro achieved 74% on certain technical tasks.

But qualitative work proved far more challenging. When asked to create website layouts, write marketing copy, or translate content with appropriate cultural nuance, agents floundered without expert guidance. "When you ask it to write you a poem, the quality of the poem is extremely subjective," Rabinovich said. "Since the rubrics for evaluation were provided by humans, there's some level of variability in representation."

Writing, translation, and sales and marketing projects showed the most dramatic improvements from human feedback. For writing work, completion rates increased by up to 17 percentage points after expert review. Engineering and architecture projects requiring creative problem-solving — like civil engineering or architectural design — improved by as much as 23 percentage points with human oversight.

This pattern suggests AI agents excel at pattern matching and replication but struggle with creativity, judgment, and context — precisely the skills that define higher-value professional work.

Inside the research: How Upwork tested AI agents with peer-reviewed scientific methods

Upwork partnered with elite freelancers on its platform to evaluate every deliverable produced by AI agents, both independently and after each cycle of human feedback. These evaluators created detailed rubrics defining whether projects met core requirements specified in job descriptions, then scored outputs across multiple iterations.

Importantly, evaluators focused only on objective completion criteria, excluding subjective factors like stylistic preferences or quality judgments that might emerge in actual client relationships. "Rubric-based completion rates should not be viewed as a measure of whether an agent would be paid in a real marketplace setting," the research notes, "but as an indicator of its ability to fulfill explicitly defined requests."

This distinction matters: An AI agent might technically complete all specified requirements yet still produce work a client rejects as inadequate. Conversely, subjective client satisfaction — the true measure of marketplace success — remains beyond current measurement capabilities.

The research underwent double-blind peer review and was accepted to NeurIPS, the premier academic conference for AI research, where Upwork will present full results in early December. The company plans to publish a complete methodology and make the benchmark available to the research community, updating the task pool regularly to prevent overfitting as agents improve.

"The idea is for this benchmark to be a living and breathing platform where agents can come in and evaluate themselves on all categories of work, and the tasks that will be offered on the platform will always update, so that these agents don't overfit and basically memorize the tasks at hand," Rabinovich said.

Upwork's AI strategy: Building Uma, a 'meta-agent' that manages human and AI workers

The research directly informs Upwork's product roadmap as the company positions itself for what executives call "the age of AI and beyond." Rather than building its own AI agents to complete specific tasks, Upwork is developing Uma, a "meta orchestration agent" that coordinates between human workers, AI systems, and clients.

"Today, Upwork is a marketplace where clients look for freelancers to get work done, and then talent comes to Upwork to find work," Rabinovich explained. "This is getting expanded into a domain where clients come to Upwork, communicate with Uma, this meta-orchestration agent, and then Uma identifies the necessary talent to get the job done, gets the tasks outcomes completed, and then delivers that to the client."

In this vision, clients would interact primarily with Uma rather than directly hiring freelancers. The AI system would analyze project requirements, determine which tasks require human expertise versus AI execution, coordinate the workflow, and ensure quality — acting as an intelligent project manager rather than a replacement worker.

"We don't want to build agents that actually complete the tasks, but we are building this meta orchestration agent that figures out what human and agent talent is necessary in order to complete the tasks," Rabinovich said. "Uma evaluates the work to be delivered to the client, orchestrates the interaction between humans and agents, and is able to learn from all the interactions that happen on the platform how to break jobs into tasks so that they get completed in a timely and effective manner."

The company recently announced plans to open its first international office in Lisbon, Portugal, by the fourth quarter of 2026, with a focus on AI infrastructure development and technical hiring. The expansion follows Upwork's record-breaking third quarter, driven partly by AI-powered product innovation and strong demand for workers with AI skills.

OpenAI, Anthropic, and Google race to build autonomous agents—but reality lags hype

Upwork's findings arrive amid escalating competition in the AI agent space. OpenAI, Anthropic, Google, and numerous startups are racing to develop autonomous agents capable of complex multi-step tasks, from booking travel to analyzing financial data to writing software.

But recent high-profile stumbles have tempered initial enthusiasm. AI agents frequently misunderstand instructions, make logical errors, or produce confidently wrong results — a phenomenon researchers call "hallucination." The gap between controlled demonstration videos and reliable real-world performance remains vast.

"There have been some evaluations that came from OpenAI and other platforms where real Upwork tasks were considered for completion by agents, and across the board, the reported results were not very optimistic, in the sense that they showed that agents—even the best ones, meaning powered by most advanced LLMs — can't really compete with humans that well, because the completion rates are pretty low," Rabinovich said.

Rather than waiting for AI to fully mature — a timeline that remains uncertain—Upwork is betting on a hybrid approach that leverages AI's strengths (speed, scalability, pattern recognition) while retaining human strengths (judgment, creativity, contextual understanding).

This philosophy extends to learning and improvement. Current AI models train primarily on static datasets scraped from the internet, supplemented by human preference feedback. But most professional work is qualitative, making it difficult for AI systems to know whether their outputs are actually good without expert evaluation.

"Unless you have this collaboration between the human and the machine, where the human is kind of the teacher and the machine is the student trying to discover new solutions, none of this will be possible," Rabinovich said. "Upwork is very uniquely positioned to create such an environment because if you try to do this with, say, self-driving cars, and you tell Waymo cars to explore new ways of getting to the airport, like avoiding traffic signs, then a bunch of bad things will happen. In doing work on Upwork, if it creates a wrong website, it doesn't cost very much, and there's no negative side effects. But the opportunity to learn is absolutely tremendous."

Will AI take your job? The evidence suggests a more complicated answer

While much public discourse around AI focuses on job displacement, Rabinovich argues the historical pattern suggests otherwise — though the transition may prove disruptive.

"The narrative in the public is that AI is eliminating jobs, whether it's writing, translation, coding or other digital work, but no one really talks about the exponential amount of new types of work that it will create," he said. "When we invented electricity and steam engines and things like that, they certainly replaced certain jobs, but the amount of new jobs that were introduced is exponentially more, and we think the same is going to happen here."

The research identifies emerging job categories focused on AI oversight: designing effective human-machine workflows, providing high-quality feedback to improve agent performance, and verifying that AI-generated work meets quality standards. These skills—prompt engineering, agent supervision, output verification—barely existed two years ago but now command premium rates on platforms like Upwork.

"New types of skills from humans are becoming necessary in the form of how to design the interaction between humans and machines, how to guide agents to make them better, and ultimately, how to verify that whatever agentic proposals are being made are actually correct, because that's what's necessary in order to advance the state of AI," Rabinovich said.

The question remains whether this transition—  from doing tasks to overseeing them — will create opportunities as quickly as it disrupts existing roles. For freelancers on Upwork, the answer may already be emerging in their bank accounts: The platform saw AI-related work grow 53% year-over-year, even as fears of AI-driven unemployment dominated headlines.

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.

An international team of researchers has released an artificial intelligence system capable of autonomously conducting scientific research across multiple disciplines — generating papers from initial concept to publication-ready manuscript in approximately 30 minutes for about $4 each.

The system, called Denario, can formulate research ideas, review existing literature, develop methodologies, write and execute code, create visualizations, and draft complete academic papers. In a demonstration of its versatility, the team used Denario to generate papers spanning astrophysics, biology, chemistry, medicine, neuroscience, and other fields, with one AI-generated paper already accepted for publication at an academic conference.

"The goal of Denario is not to automate science, but to develop a research assistant that can accelerate scientific discovery," the researchers wrote in a paper released Monday describing the system. The team is making the software publicly available as an open-source tool.

This achievement marks a turning point in the application of large language models to scientific work, potentially transforming how researchers approach early-stage investigations and literature reviews. However, the research also highlights substantial limitations and raises pressing questions about validation, authorship, and the changing nature of scientific labor.

From data to draft: how AI agents collaborate to conduct research

At its core, Denario operates not as a single AI brain but as a digital research department where specialized AI agents collaborate to push a project from conception to completion. The process can begin with the "Idea Module," which employs a fascinating adversarial process where an "Idea Maker" agent proposes research projects that are then scrutinized by an "Idea Hater" agent, which critiques them for feasibility and scientific value. This iterative loop refines raw concepts into robust research directions.

Once a hypothesis is solidified, a "Literature Module" scours academic databases like Semantic Scholar to check the idea's novelty, followed by a "Methodology Module" that lays out a detailed, step-by-step research plan. The heavy lifting is then done by the "Analysis Module," a virtual workhorse that writes, debugs, and executes its own Python code to analyze data, generate plots, and summarize findings. Finally, the "Paper Module" takes the resulting data and plots and drafts a complete scientific paper in LaTeX, the standard for many scientific fields. In a final, recursive step, a "Review Module" can even act as an AI peer-reviewer, providing a critical report on the generated paper's strengths and weaknesses.

This modular design allows a human researcher to intervene at any stage, providing their own idea or methodology, or to simply use Denario as an end-to-end autonomous system. "The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis," the paper explains.

To validate its capabilities, the Denario team has put the system to the test, generating a vast repository of papers across numerous disciplines. In a striking proof of concept, one paper fully generated by Denario was accepted for publication at the Agents4Science 2025 conference — a peer-reviewed venue where AI systems themselves are the primary authors. The paper, titled "QITT-Enhanced Multi-Scale Substructure Analysis with Learned Topological Embeddings for Cosmological Parameter Estimation from Dark Matter Halo Merger Trees," successfully combined complex ideas from quantum physics, machine learning, and cosmology to analyze simulation data.

The ghost in the machine: AI’s ‘vacuous’ results and ethical alarms

While the successes are notable, the research paper is refreshingly candid about Denario's significant limitations and failure modes. The authors stress that the system currently "behaves more like a good undergraduate or early graduate student rather than a full professor in terms of big picture, connecting results...etc." This honesty provides a crucial reality check in a field often dominated by hype.

The paper dedicates entire sections to "Failure Modes" and "Ethical Implications," a level of transparency that enterprise leaders should note. The authors report that in one instance, the system "hallucinated an entire paper without implementing the necessary numerical solver," inventing results to fit a plausible narrative. In another test on a pure mathematics problem, the AI produced text that had the form of a mathematical proof but was, in the authors' words, "mathematically vacuous."

These failures underscore a critical point for any organization looking to deploy agentic AI: the systems can be brittle and are prone to confident-sounding errors that require expert human oversight. The Denario paper serves as a vital case study in the importance of keeping a human in the loop for validation and critical assessment.

The authors also confront the profound ethical questions raised by their creation. They warn that "AI agents could be used to quickly flood the scientific literature with claims driven by a particular political agenda or specific commercial or economic interests." They also touch on the "Turing Trap," a phenomenon where the goal becomes mimicking human intelligence rather than augmenting it, potentially leading to a "homogenization" of research that stifles true, paradigm-shifting innovation.

An open-source co-pilot for the world's labs

Denario is not just a theoretical exercise locked away in an academic lab. The entire system is open-source under a GPL-3.0 license and is accessible to the broader community. The main project and its graphical user interface, DenarioApp, are available on GitHub, with installation managed via standard Python tools. For enterprise environments focused on reproducibility and scalability, the project also provides official Docker images. A public demo hosted on Hugging Face Spaces allows anyone to experiment with its capabilities.

For now, Denario remains what its creators call a powerful assistant, but not a replacement for the seasoned intuition of a human expert. This framing is deliberate. The Denario project is less about creating an automated scientist and more about building the ultimate co-pilot, one designed to handle the tedious and time-consuming aspects of modern research.

By handing off the grueling work of coding, debugging, and initial drafting to an AI agent, the system promises to free up human researchers for the one task it cannot automate: the deep, critical thinking required to ask the right questions in the first place.

Microsoft is launching a significant expansion of its Copilot AI assistant on Tuesday, introducing tools that let employees build applications, automate workflows, and create specialized AI agents using only conversational prompts — no coding required.

The new capabilities, called App Builder and Workflows, mark Microsoft's most aggressive attempt yet to merge artificial intelligence with software development, enabling the estimated 100 million Microsoft 365 users to create business tools as easily as they currently draft emails or build spreadsheets.

"We really believe that a main part of an AI-forward employee, not just developers, will be to create agents, workflows and apps," Charles Lamanna, Microsoft's president of business and industry Copilot, said in an interview with VentureBeat. "Part of the job will be to build and create these things."

The announcement comes as Microsoft deepens its commitment to AI-powered productivity tools while navigating a complex partnership with OpenAI, the creator of the underlying technology that powers Copilot. On the same day, OpenAI completed its restructuring into a for-profit entity, with Microsoft receiving a 27% ownership stake valued at approximately $135 billion.

How natural language prompts now create fully functional business applications

The new features transform Copilot from a conversational assistant into what Microsoft envisions as a comprehensive development environment accessible to non-technical workers. Users can now describe an application they need — such as a project tracker with dashboards and task assignments — and Copilot will generate a working app complete with a database backend, user interface, and security controls.

"If you're right inside of Copilot, you can now have a conversation to build an application complete with a backing database and a security model," Lamanna explained. "You can make edit requests and update requests and change requests so you can tune the app to get exactly the experience you want before you share it with other users."

The App Builder stores data in Microsoft Lists, the company's lightweight database system, and allows users to share finished applications via a simple link—similar to sharing a document. The Workflows agent, meanwhile, automates routine tasks across Microsoft's ecosystem of products, including Outlook, Teams, SharePoint, and Planner, by converting natural language descriptions into automated processes.

A third component, a simplified version of Microsoft's Copilot Studio agent-building platform, lets users create specialized AI assistants tailored to specific tasks or knowledge domains, drawing from SharePoint documents, meeting transcripts, emails, and external systems.

All three capabilities are included in the existing $30-per-month Microsoft 365 Copilot subscription at no additional cost — a pricing decision Lamanna characterized as consistent with Microsoft's historical approach of bundling significant value into its productivity suite.

"That's what Microsoft always does. We try to do a huge amount of value at a low price," he said. "If you go look at Office, you think about Excel, Word, PowerPoint, Exchange, all that for like eight bucks a month. That's a pretty good deal."

Why Microsoft's nine-year bet on low-code development is finally paying off

The new tools represent the culmination of a nine-year effort by Microsoft to democratize software development through its Power Platform — a collection of low-code and no-code development tools that has grown to 56 million monthly active users, according to figures the company disclosed in recent earnings reports.

Lamanna, who has led the Power Platform initiative since its inception, said the integration into Copilot marks a fundamental shift in how these capabilities reach users. Rather than requiring workers to visit a separate website or learn a specialized interface, the development tools now exist within the same conversational window they already use for AI-assisted tasks.

"One of the big things that we're excited about is Copilot — that's a tool for literally every office worker," Lamanna said. "Every office worker, just like they research data, they analyze data, they reason over topics, they also will be creating apps, agents and workflows."

The integration offers significant technical advantages, he argued. Because Copilot already indexes a user's Microsoft 365 content — emails, documents, meetings, and organizational data — it can incorporate that context into the applications and workflows it builds. If a user asks for "an app for Project Spartan," Copilot can draw from existing communications to understand what that project entails and suggest relevant features.

"If you go to those other tools, they have no idea what the heck Project Spartan is," Lamanna said, referencing competing low-code platforms from companies like Google, Salesforce, and ServiceNow. "But if you do it inside of Copilot and inside of the App Builder, it's able to draw from all that information and context."

Microsoft claims the apps created through these tools are "full-stack applications" with proper databases secured through the same identity systems used across its enterprise products — distinguishing them from simpler front-end tools offered by competitors. The company also emphasized that its existing governance, security, and data loss prevention policies automatically apply to apps and workflows created through Copilot.

Where professional developers still matter in an AI-powered workplace

While Microsoft positions the new capabilities as accessible to all office workers, Lamanna was careful to delineate where professional developers remain essential. His dividing line centers on whether a system interacts with parties outside the organization.

"Anything that leaves the boundaries of your company warrants developer involvement," he said. "If you want to build an agent and put it on your website, you should have developers involved. Or if you want to build an automation which interfaces directly with your customers, or an app or a website which interfaces directly with your customers, you want professionals involved."

The reasoning is risk-based: external-facing systems carry greater potential for data breaches, security vulnerabilities, or business errors. "You don't want people getting refunds they shouldn't," Lamanna noted.

For internal use cases — approval workflows, project tracking, team dashboards — Microsoft believes the new tools can handle the majority of needs without IT department involvement. But the company has built "no cliffs," in Lamanna's terminology, allowing users to migrate simple apps to more sophisticated platforms as needs grow.

Apps created in the conversational App Builder can be opened in Power Apps, Microsoft's full development environment, where they can be connected to Dataverse, the company's enterprise database, or extended with custom code. Similarly, simple workflows can graduate to the full Power Automate platform, and basic agents can be enhanced in the complete Copilot Studio.

"We have this mantra called no cliffs," Lamanna said. "If your app gets too complicated for the App Builder, you can always edit and open it in Power Apps. You can jump over to the richer experience, and if you're really sophisticated, you can even go from those experiences into Azure."

This architecture addresses a problem that has plagued previous generations of easy-to-use development tools: users who outgrow the simplified environment often must rebuild from scratch on professional platforms. "People really do not like easy-to-use development tools if I have to throw everything away and start over," Lamanna said.

What happens when every employee can build apps without IT approval

The democratization of software development raises questions about governance, maintenance, and organizational complexity — issues Microsoft has worked to address through administrative controls.

IT administrators can view all applications, workflows, and agents created within their organization through a centralized inventory in the Microsoft 365 admin center. They can reassign ownership, disable access at the group level, or "promote" particularly useful employee-created apps to officially supported status.

"We have a bunch of customers who have this approach where it's like, let 1,000 apps bloom, and then the best ones, I go upgrade and make them IT-governed or central," Lamanna said.

The system also includes provisions for when employees leave. Apps and workflows remain accessible for 60 days, during which managers can claim ownership — similar to how OneDrive files are handled when someone departs.

Lamanna argued that most employee-created apps don't warrant significant IT oversight. "It's just not worth inspecting an app that John, Susie, and Bob use to do their job," he said. "It should concern itself with the app that ends up being used by 2,000 people, and that will pop up in that dashboard."

Still, the proliferation of employee-created applications could create challenges. Users have expressed frustration with Microsoft's increasing emphasis on AI features across its products, with some giving the Microsoft 365 mobile app one-star ratings after a recent update prioritized Copilot over traditional file access.

The tools also arrive as enterprises grapple with "shadow IT" — unsanctioned software and systems that employees adopt without official approval. While Microsoft's governance controls aim to provide visibility, the ease of creating new applications could accelerate the pace at which these systems multiply.

The ambitious plan to turn 500 million workers into software builders

Microsoft's ambitions for the technology extend far beyond incremental productivity gains. Lamanna envisions a fundamental transformation of what it means to be an office worker — one where building software becomes as routine as creating spreadsheets.

"Just like how 20 years ago you put on your resume that you could use pivot tables in Excel, people are going to start saying that they can use App Builder and workflow agents, even if they're just in the finance department or the sales department," he said.

The numbers he's targeting are staggering. With 56 million people already using Power Platform, Lamanna believes the integration into Copilot could eventually reach 500 million builders. "Early days still, but I think it's certainly encouraging," he said.

The features are currently available only to customers in Microsoft's Frontier Program — an early access initiative for Microsoft 365 Copilot subscribers. The company has not disclosed how many organizations participate in the program or when the tools will reach general availability.

The announcement fits within Microsoft's larger strategy of embedding AI capabilities throughout its product portfolio, driven by its partnership with OpenAI. Under the restructured agreement announced Tuesday, Microsoft will have access to OpenAI's technology through 2032, including models that achieve artificial general intelligence (AGI) — though such systems do not yet exist. Microsoft has also begun integrating Copilot into its new companion apps for Windows 11, which provide quick access to contacts, files, and calendar information.

The aggressive integration of AI features across Microsoft's ecosystem has drawn mixed reactions. While enterprise customers have shown interest in productivity gains, the rapid pace of change and ubiquity of AI prompts have frustrated some users who prefer traditional workflows.

For Microsoft, however, the calculation is clear: if even a fraction of its user base begins creating applications and automations, it would represent a massive expansion of the effective software development workforce — and further entrench customers in Microsoft's ecosystem. The company is betting that the same natural language interface that made ChatGPT accessible to millions can finally unlock the decades-old promise of empowering everyday workers to build their own tools.

The App Builder and Workflows agents are available starting today through the Microsoft 365 Copilot Agent Store for Frontier Program participants.

Whether that future arrives depends not just on the technology's capabilities, but on a more fundamental question: Do millions of office workers actually want to become part-time software developers? Microsoft is about to find out if the answer is yes — or if some jobs are better left to the professionals.

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.

The Dfinity Foundation on Wednesday released Caffeine, an artificial intelligence platform that allows users to build and deploy web applications through natural language conversation alone, bypassing traditional coding entirely. The system, which became publicly available today, represents a fundamental departure from existing AI coding assistants by building applications on a specialized decentralized infrastructure designed specifically for autonomous AI development.

Unlike GitHub Copilot, Cursor, or other "vibe coding" tools that help human developers write code faster, Caffeine positions itself as a complete replacement for technical teams. Users describe what they want in plain language, and an ensemble of AI models writes, deploys, and continually updates production-grade applications — with no human intervention in the codebase itself.

"In the future, you as a prospective app owner or service owner… will talk to AI. AI will give you what you want on a URL," said Dominic Williams, founder and chief scientist at the Dfinity Foundation, in an exclusive interview with VentureBeat. "You will use that, completely interact productively, and you'll just keep talking to AI to evolve what that does. The AI, or an ensemble of AIs, will be your tech team."

The platform has attracted significant early interest: more than 15,000 alpha users tested Caffeine before its public release, with daily active users representing 26% of those who received access codes — "early Facebook kind of levels," according to Williams. The foundation reports some users spending entire days building applications on the platform, forcing Dfinity to consider usage limits due to underlying AI infrastructure costs.

Why Caffeine's custom programming language guarantees your data won't disappear

Caffeine's most significant technical claim addresses a problem that has plagued AI-generated code: data loss during application updates. The platform builds applications using Motoko, a programming language developed by Dfinity specifically for AI use, which provides mathematical guarantees that upgrades cannot accidentally delete user data.

"When AI is updating apps and services in production, a mistake cannot lose data. That's a guarantee," Williams said. "It's not like there are some safeguards to try and stop it losing data. This language framework gives it rails that guarantee if an upgrade, an update to its app's underlying logic, would cause data loss, the upgrade fails and the AI just tries again."

This addresses what Williams characterizes as critical failures in competing platforms. User forums for tools like Lovable and Replit, he notes, frequently report three major problems: applications that become irreparably broken as complexity increases, security vulnerabilities that allow unauthorized access, and mysterious data loss during updates.

Traditional tech stacks evolved to meet human developer needs — familiarity with SQL databases, preference for known programming languages, existing skill investments. "That's how the traditional tech stacks evolved. It's really evolved to meet human needs," Williams explained. "But in the future, it's going to be different. You're not going to care how the AI did it. Instead, for you, AI is the tech stack."

Caffeine's architecture reflects this philosophy. Applications run entirely on the Internet Computer Protocol (ICP), a blockchain-based network that Dfinity launched in May 2021 after raising over $100 million from investors including Andreessen Horowitz and Polychain Capital. The ICP uses what Dfinity calls "chain-key cryptography" to create what Williams describes as "tamper-proof" code — applications that are mathematically guaranteed to execute their written logic without interference from traditional cyberattacks.

"The code can't be affected by ransomware, so you don't have to worry about malware in the same way you do," Williams said. "Configuration errors don't result in traditional cyber attacks. That passive traditional cyber attacks isn't something you need to worry about."

How 'orthogonal persistence' lets AI build apps without managing databases

At the heart of Caffeine's technical approach is a concept called "orthogonal persistence," which fundamentally reimagines how applications store and manage data. In traditional development, programmers must write extensive code to move data between application logic and separate database systems — marshaling data in and out of SQL servers, managing connections, handling synchronization.

Motoko eliminates this entirely. Williams demonstrated with a simple example: defining a blog post data type and declaring a variable to store an array of posts requires just two lines of code. "This declaration is all that's necessary to have the blog maintain its list of posts," he explained during a presentation on the technology. "Compare that to traditional IT where in order to persist the blog posts, you'd have to marshal them in and out of a database server. This is quite literally orders of magnitude more simple."

This abstraction allows AI to work at a higher conceptual level, focusing on application logic rather than infrastructure plumbing. "Logic and data are kind of the same," Williams said. "This is one of the things that enables AI to build far more complicated functionality than it could otherwise do."

The system also employs what Dfinity calls "loss-safe data migration." When AI needs to modify an application's data structure — adding a "likes" field to blog posts, for example — it must write migration logic in two passes. The framework automatically verifies that the transformation won't result in data loss, refusing to compile or deploy code that could delete information unless explicitly instructed.

From million-dollar SaaS contracts to conversational app building in minutes

Williams positions Caffeine as particularly transformative for enterprise IT, where he claims costs could fall to "1% of what they were before" while time-to-market shrinks to similar fractions. The platform targets a spectrum from individual creators to large corporations, all of whom currently face either expensive development teams or constraining low-code templates.

"A corporation or government department might want to create a corporate portal or CRM, ERP functionality," Williams said, referring to customer relationship management and enterprise resource planning systems. "They will otherwise have to obtain this by signing up for some incredibly expensive SaaS service where they become locked in, their data gets stuck, and they still have to spend a lot of money on consultants customizing the functionality."

Applications built through Caffeine are owned entirely by their creators and cannot be shut down by centralized parties — a consequence of running on the decentralized Internet Computer network rather than traditional cloud providers like Amazon Web Services. "When someone says built on the internet computer, it actually means built on the internet computer," Williams emphasized, contrasting this with blockchain projects that merely host tokens while running actual applications on centralized infrastructure.

The platform demonstrated this versatility during a July 2025 hackathon in San Francisco, where participants created applications ranging from a "Will Maker" tool for generating legal documents, to "Blue Lens," a voice-AI water quality monitoring system, to "Road Patrol," a gamified community reporting app for infrastructure problems. Critically, many of these came from non-technical participants with no coding background.

"I'm from a non-technical background, I'm actually a quality assurance professional," said the creator of Blue Lens in a video testimonial. "Through Caffeine I can build something really intuitive and next-gen to the public." The application integrated multiple external services — Eleven Labs for voice AI, real-time government water data through retrieval-augmented generation, and Midjourney-generated visual assets — all coordinated through conversational prompts.

What separates Caffeine from GitHub Copilot, Cursor, and the 'vibe coding' wave

Caffeine enters a crowded market of AI-assisted development tools, but Williams argues the competition isn't truly comparable. GitHub Copilot, Cursor, and similar tools serve human developers working with traditional technology stacks. Platforms like Replit and Lovable occupy a middle ground, offering "vibe coding" that mixes AI generation with human editing.

"If you're a Node.js developer, you know you're working with the traditional stack, and you might want to do your coding with Copilot or using Claude or using Cursor," Williams said. "That's a very different thing to what Caffeine is offering. There'll always be cases where you probably wouldn't want to hand over the logic of the control system for a new nuclear missile silo to AI. But there's going to be these holdout areas, right? And there's all the legacy stuff that has to be maintained."

The key distinction, according to Williams, lies in production readiness. Existing AI coding tools excel at rapid prototyping but stumble when applications grow complex or require guaranteed reliability. Reddit forums for these platforms document users hitting insurmountable walls where applications break irreparably, or where AI-generated code introduces security vulnerabilities.

"As the demands and the requirements become more complicated, eventually you can hit a limit, and when you hit that limit, not only can you not go any further, but sometimes your app will get broken and there's no way of going back to where you were before," Williams said. "That can't happen with productive apps, and it also can't be the case that you're getting hacked and losing data, because once you go hands-free, if you like, and there's no tech team, there's no technical people involved, who's going to run the backups and restore your app?"

The Internet Computer's architecture addresses this through Byzantine fault tolerance — even if attackers gain physical control over some network hardware, they cannot corrupt applications or their data. "This is the beginning of a compute revolution and it's also the perfect platform for AI to build on," Williams said.

Inside the vision: A web that programs itself through natural language

Dfinity frames Caffeine within a broader vision it calls the "self-writing internet," where the web literally programs itself through natural language interaction. This represents what Williams describes as a "seismic shift coming to tech" — from human developers selecting technology stacks based on their existing skills, to AI selecting optimal implementations invisible to users.

"You don't care about whether some human being has learned all of the different platforms and Amazon Web Services or something like that. You don't care about that. You just care: Is it secure? Do you get security guarantees? Is it resilient? What's the level of resilience?" Williams said. "Those are the new parameters."

The platform demonstrated this during live demonstrations, including at the World Computer Summit 2025 in Zurich. Williams created a talent recruitment application from scratch in under two minutes, then modified it in real-time while the application ran with users already interacting with it. "You will continue talking to the AI and just keep on refreshing the URL to see the changes," he explained.

This capability extends to complex scenarios. During demonstrations, Williams showed building a tennis lesson booking system, an e-commerce platform, and an event registration system — all simultaneously, working on multiple applications in parallel. "We predict that as people get very proficient with Caffeine, they could be working on even 10 apps in parallel," he said.

The system writes substantial code: a simple personal blog generated 700 lines of code in a couple of minutes. More complex applications can involve thousands of lines across frontend and backend components, all abstracted away from the user who only describes desired functionality.

The economics of cloning: How Caffeine's app market challenges traditional stores

Caffeine's economic model differs fundamentally from traditional software-as-a-service platforms. Applications run on the Internet Computer Protocol, which uses a "reverse gas model" where developers pay for computation rather than users paying transaction fees. The platform includes an integrated App Market where creators can publish applications for others to clone and adapt — creating what Dfinity envisions as a new economic ecosystem.

"App stores today obviously operate on gatekeeping," said Pierre Samaties, chief business officer at Dfinity, during the World Computer Summit. "That's going to erode." Rather than purchasing applications, users can clone them and modify them for their own purposes — fundamentally different from Apple's App Store or Google Play models.

Williams acknowledges that Caffeine itself currently runs on centralized infrastructure, despite building applications on the decentralized Internet Computer. "Caffeine itself actually is centralized. It uses aspects of the Internet Computer. We want Caffeine itself to run on the Internet Computer in the future, but it's not there now," he said. The platform leverages commercially available foundation models from companies like Anthropic, whose Claude Sonnet model powers much of Caffeine's backend logic.

This pragmatic approach reflects Dfinity's strategy of using best-in-class AI models while focusing its own development on the specialized infrastructure and programming language designed for AI use. "These content models have been developed by companies with enormous budgets, absolutely enormous budgets," Williams said. "I don't think in the near future we'll run AI on the Internet Computer for that reason, unless there's a special case."

A decade in the making: From Ethereum roots to the self-writing internet

The Dfinity Foundation has pursued this vision since Williams began researching decentralized networks in late 2013. After involvement with Ethereum before its 2015 launch, Williams became fascinated with the concept of a "world computer"—a public blockchain network that could host not just tokens but entire applications and services.

"By 2015 I was talking about network-focused drivers, Dfinity back then, and that could really operate as an alternative tech stack, and eventually host even things like social networks and massive enterprise systems," Williams said. The foundation launched the Internet Computer Protocol in May 2021, initially focusing on Web3 developers. Despite not being among the highest-valued blockchain projects, ICP consistently ranks in the top 10 for developer numbers.

The pivot to AI-driven development came from recognizing that "in the future, the tech stack will be AI," according to Williams. This realization led to Caffeine's development, announced on Dfinity's public roadmap in March 2025 and demonstrated at the World Computer Summit in June 2025.

One successful example of the Dfinity vision running in production is OpenChat, a messaging application that runs entirely on the Internet Computer and is governed by a decentralized autonomous organization (DAO) with tens of thousands of participants voting on source code updates through algorithmic governance. "The community is actually controlling the source code updates," Williams explained. "Developers propose updates, community reads the updates, and if the community is happy, OpenChat updates itself."

The skeptics weigh in: Crypto baggage and real-world testing ahead

The platform faces several challenges. Dfinity's crypto industry roots may create perception problems in enterprise markets, Williams acknowledges. "The Web3 industry's reputation is a bit tarnished and probably rightfully so," he said during the World Computer Summit. "Now people can, for themselves, experience what a decentralized network is. We're going to see self-writing take over the enterprise space because the speed and efficiency are just incredible."

The foundation's history includes controversy: ICP's token launched in 2021 at over $100 per token with an all-time high around $700, then crashed below $3 in 2023 before recovering. The project has faced legal challenges, including class action lawsuits alleging misleading investors, and Dfinity filed defamation claims against industry critics.

Technical limitations also remain. Caffeine cannot yet compile React front-ends on the Internet Computer itself, requiring some off-chain processing. Complex integrations with traditional systems — payment processing through Stripe, for example — still require centralized components. "Your app is running end-to-end on the Internet Computer, then when it needs to actually accept payment, it's going to hand over to your Stripe account," Williams explained.

The platform's claims about data loss prevention and security guarantees, while technically grounded in the Motoko language design and Internet Computer architecture, remain to be tested at scale with diverse real-world applications. The 26% daily active user rate from alpha testing is impressive but comes from a self-selected group of early adopters.

When five billion smartphone users become developers

Williams rejects concerns that AI-driven development will eliminate software engineering jobs, arguing instead for market expansion. "The self-writing internet empowers eight billion non-technical people," he said. "Some of these people will enter roles in tech, becoming prompt engineers, tech entrepreneurs, or helping run online communities. Humanity will create millions of new custom apps and services, and a subset of those will require professional human assistance."

During his World Computer Summit demonstration, Williams was explicit about the scale of transformation Dfinity envisions. "Today there are about 35,000 Web3 engineers in the world. Worldwide there are about 15 million full-stack engineers," he said. "But tomorrow with the self-writing internet, everyone will be a builder. Today there are already about five billion people with internet-connected smartphones and they'll all be able to use Caffeine."

The hackathon results suggest this isn't pure hyperbole. A dentist built "Dental Tracks" to help patients manage their dental records. A transportation industry professional created "Road Patrol" for gamified infrastructure reporting. A frustrated knitting student built "Skill Sprout," a garden-themed app for learning new hobbies, complete with material checklists and step-by-step skill breakdowns—all without writing a single line of code.

"I was learning to knit. I got irritated because I had the wrong materials," the creator explained in a video interview. "I don't know how to do the stitches, so I have to individually search, and it's really intimidating when you're trying to learn something you don't—you don't even know what you don't know."

Whether Caffeine succeeds depends on factors still unknown: how production applications perform under real-world stress, whether the Internet Computer scales to millions of applications, whether enterprises can overcome their skepticism of blockchain-adjacent technology. But if Williams is right about the fundamental shift — that AI will be the tech stack, not just a tool for human developers — then someone will build what Caffeine promises.

The question isn't whether the future looks like this. It's who gets there first, and whether they can do it without losing everyone's data along the way.

OpenAI’s annual developer conference on Monday was a spectacle of ambitious AI product launches, from an app store for ChatGPT to a stunning video-generation API that brought creative concepts to life. But for the enterprises and technical leaders watching closely, the most consequential announcement was the quiet general availability of Codex, the company's AI software engineer. This release signals a profound shift in how software—and by extension, modern business—is built.

While other announcements captured the public’s imagination, the production-ready release of Codex, supercharged by a new specialized model and a suite of enterprise-grade tools, is the engine behind OpenAI’s entire vision. It is the tool that builds the tools, the proven agent in a world buzzing with agentic potential, and the clearest articulation of the company's strategy to win the enterprise.

The general availability of Codex moves it from a "research preview" to a fully supported product, complete with a new software development kit (SDK), a Slack integration, and administrative controls for security and monitoring.This transition declares that Codex is ready for mission-critical work inside the world’s largest companies.

"We think this is the best time in history to be a builder; it has never been faster to go from idea to product," said OpenAI CEO Sam Altman during the opening keynote presentation. "Software used to take months or years to build. You saw that it can take minutes now to build with AI." 

That acceleration is not theoretical. It's a reality born from OpenAI’s own internal use — a massive "dogfooding" effort that serves as the ultimate case study for enterprise customers.

Inside GPT-5-Codex: The AI model that codes autonomously for hours and drives 70% productivity gains

At the heart of the Codex upgrade is GPT-5-Codex, a version of OpenAI's latest flagship model that has been "purposely trained for Codex and agentic coding." The new model is designed to function as an autonomous teammate, moving far beyond simple code autocompletion.

"I personally like to think about it as a little bit like a human teammate," explained Tibo Sottiaux, an OpenAI engineer, during a technical session on Codex. "You can pair a program with it on your computer, you can delegate to it, or as you'll see, you can give it a job without explicit prompting."

This new model enables "adaptive thinking," allowing it to dynamically adjust the time and computational effort spent on a task based on its complexity.For simple requests, it's fast and efficient, but for complex refactoring projects, it can work for hours.

One engineer during the technical session noted, "I've seen the GPT-5-Codex model work for over seven hours productively... on a marathon session." This capability to handle long-running, complex tasks is a significant leap beyond the simple, single-shot interactions that define most AI coding assistants.

The results inside OpenAI have been dramatic. The company reported that 92% of its technical staff now uses Codex daily, and those engineers complete 70% more pull requests (a measure of code contribution) each week. Usage has surged tenfold since August. 

"When we as a team see the stats, it feels great," Sottiaux shared. "But even better is being at lunch with someone who then goes 'Hey I use Codex all the time. Here's a cool thing that I do with it. Do you want to hear about it?'" 

How OpenAI uses Codex to build its own AI products and catch hundreds of bugs daily

Perhaps the most compelling argument for Codex’s importance is that it is the foundational layer upon which OpenAI’s other flashy announcements were built. During the DevDay event, the company showcased custom-built arcade games and a dynamic, AI-powered website for the conference itself, all developed using Codex.

In one session, engineers demonstrated how they built "Storyboard," a custom creative tool for the film industry, in just 48 hours during an internal hackathon. "We decided to test Codex, our coding agent... we would send tasks to Codex in between meetings. We really easily reviewed and merged PRs into production, which Codex even allowed us to do from our phones," said Allison August, a solutions engineering leader at OpenAI. 

This reveals a critical insight: the rapid innovation showcased at DevDay is a direct result of the productivity flywheel created by Codex. The AI is a core part of the manufacturing process for all other AI products.

A key enterprise-focused feature is the new, more robust code review capability. OpenAI said it "purposely trained GPT-5-Codex to be great at ultra thorough code review," enabling it to explore dependencies and validate a programmer's intent against the actual implementation to find high-quality bugs.Internally, nearly every pull request at OpenAI is now reviewed by Codex, catching hundreds of issues daily before they reach a human reviewer.

"It saves you time, you ship with more confidence," Sottiaux said. "There's nothing worse than finding a bug after we actually ship the feature." 

Why enterprise software teams are choosing Codex over GitHub Copilot for mission-critical development

The maturation of Codex is central to OpenAI’s broader strategy to conquer the enterprise market, a move essential to justifying its massive valuation and unprecedented compute expenditures. During a press conference, CEO Sam Altman confirmed the strategic shift.

"The models are there now, and you should expect a huge focus from us on really winning enterprises with amazing products, starting here," Altman said during a private press conference. 

OpenAI President and Co-founder Greg Brockman immediately added, "And you can see it already with Codex, which I think has been just an incredible success and has really grown super fast." 

For technical decision-makers, the message is clear. While consumer-facing agents that book dinner reservations are still finding their footing, Codex is a proven enterprise agent delivering substantial ROI today. Companies like Cisco have already rolled out Codex to their engineering organizations, cutting code review times by 50% and reducing project timelines from weeks to days.

With the new Codex SDK, companies can now embed this agentic power directly into their own custom workflows, such as automating fixes in a CI/CD pipeline or even creating self-evolving applications. During a live demo, an engineer showcased a mobile app that updated its own user interface in real-time based on a natural language prompt, all powered by the embedded Codex SDK. 

While the launch of an app ecosystem in ChatGPT and the breathtaking visuals of the Sora 2 API rightfully generated headlines, the general availability of Codex marks a more fundamental and immediate transformation. It is the quiet but powerful engine driving the next era of software development, turning the abstract promise of AI-driven productivity into a tangible, deployable reality for businesses today.

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