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Research
12 posts about Research. Every summary links to the original.
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Clear career paths for high-performing Data Science and Research teams
Intercom created a unified RAD function 18 months ago, merging Product Research and Product Analytics to drive evidence-based decisions. To build a high-performing team, they developed career ladders with two paths: individual contributor and manager, where management is a lateral move, not a promotion. The IC track spans Associate to Senior Principal and assesses six skill sets: mastery, partnerships and influence, impact, leadership, communication, and feedback and collaboration, with specific competencies like tools and methods, product knowledge, and mentorship. The management track focuses on five skill sets: leadership, people management, business strategy, product strategy, and execution and impact, where managers are expected to be role models, uplevel the RAD function, and coach their teams. These ladders, created with managers Nick, Louis, and Hayley, clarify roles, align expectations, promote development, and enable fair performance calibration, prioritizing mastery and communication across both paths.
Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems
Designed artifacts are ontological, shaping what becomes possible, yet systems enabling user authorship are typically evaluated on usability rather than ontological boundary negotiation. Two open-ended probes using a Wizard of Oz technique were created to let participants experience training a personalized machine learning system on phenomena they defined themselves. In a week-long exploratory study, participants used one of two probes during their everyday lives, allowing observation of boundary negotiations. Four sites of ontological boundary negotiation emerged: the boundaries of a phenomenon, the subject as part of relations, distinguishing signal from noise, and the objectivity of data. The paper offers starting points for supporting boundary negotiation through design and proposes open-ended probes as a method for ontological design.
How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?
Recent autonomous machine learning engineering agents increasingly rely on elaborate machinery such as multi-agent orchestrators and dedicated retrieval subagents, motivated by progress stagnation over long-horizon cycles and limited Large Language Model primitives. This paper finds that under an equal time budget and the same frontier LLM backbone, open-source state-of-the-art harnesses provide no advantages over a single session of a minimal-harness coding agent baseline that uses direct read, write, and bash primitives. The authors argue via large-scale systematic ablation studies that the machinery layers become redundant in the coding agent setting. The backbone, not the harness complexity, is the primary driver for performance. Consequently, the effort spent elaborating hand-crafted harnesses around strong models yields poor returns for current MLE benchmarks.
Working with the US Department of Energy
Anthropic and the US Department of Energy (DOE) announce a multi-year partnership under the Genesis Mission, targeting American energy dominance, biological and life sciences, and scientific productivity across potentially all 17 national laboratories. Anthropic plans to give DOE researchers access to Claude and engineers who can build purpose-built AI agents, Model Context Protocol servers connecting Claude with scientific instruments and tools, and Claude Skills for specialized workflows. Proposed applications include speeding permitting reviews, supporting nuclear technology research and energy security, developing pandemic and biological-threat early-warning systems, accelerating drug discovery, and using fifty years of DOE research to surface ideas and patterns. The partnership extends prior DOE work, including a nuclear risk classifier co-developed with the National Nuclear Security Administration and Claude’s rollout at Lawrence Livermore National Laboratory. Anthropic says future arrangements could develop a model for collaboration between AI and human researchers.
Expanding our support for scientists
Anthropic is expanding support for scientific researchers through free and discounted Claude subscriptions, research credits, and products designed for scientific work. The new Claude team plan for scientists opens 10,000 seats worldwide for one year: standard seats are free, while premium seats with five-times usage limits cost $15 per month; the program is intended to grow beyond that initial allocation. Its AI for Science program, previously focused largely on biological sciences, will offer credits to researchers in additional fields, including compute-heavy work, with up to $50,000 available per project after lab usage exceeds standard or premium allowances. Access requires principal investigators or equivalent leaders at academic or nonprofit institutions to verify their labs, while biology and chemistry researchers remain limited to Opus-class models and Fable continues to block professional biology and drug-development queries because of dual-use risks.
How scientists are using Claude to accelerate research and discovery
Claude is being used by scientific research groups as a collaborator across experiment planning, data analysis, and discovery, rather than only for literature reviews or coding. The post describes Biomni, a Stanford platform that gives a Claude-powered agent access to hundreds of biological tools and databases; in an early trial, it completed a genome-wide association study analysis in 20 minutes, while reported validation cases included wearable data and 336,000-cell gene-activity data. Other systems encode expert workflows, including MozzareLLM for interpreting gene-knockout clusters, while a Lundberg lab project uses molecular relationship maps to propose genes for under-studied biological processes. These systems remain imperfect and require guardrails, expert-defined skills, or prospective testing, but the reported examples suggest AI can reduce research bottlenecks and support new approaches to scientific investigation.
Anthropic partners with Allen Institute and Howard Hughes Medical Institute to accelerate scientific discovery
Modern biological research generates data at unprecedented scale, but transforming it into validated biological insights remains a fundamental bottleneck. Knowledge synthesis, hypothesis generation, and experimental interpretation still depend on manual processes, while scientific AI systems must provide reasoning researchers can evaluate, trace, and build upon. Anthropic announces partnerships with the Allen Institute and Howard Hughes Medical Institute (HHMI) to extend Claude’s capabilities into frontier research and help scientists plan and execute experiments. HHMI will develop specialized lab agents integrated with scientific instruments and analysis pipelines, while the Allen Institute will build coordinated multi-agent systems for multimodal analysis, including multi-omic integration, knowledge graphs, temporal dynamics, and experimental design. Together, the collaborations aim to compress manual analysis, surface overlooked patterns, and create transparent systems that augment rather than replace scientific judgment while keeping researchers in control.
Supporting ambitious external research through the Anthropic Economic Futures Research Fund
The Anthropic Economic Futures Research Fund commits $200 million to external research on interventions that could help society prepare for AI’s economic impacts. It prioritizes five areas: workplace integration, support for AI-driven transitions, income support for displacement, worker stakes in AI-driven growth, and evidence on public investments. The fund will favor large-scale randomized controlled trials, ambitious pilots, and program evaluations, with particular interest in projects that share findings at key milestones and can scale rapidly if promising; typical awards will be $5 million to $30 million, with nothing below $1 million funded directly. Eligible applicants include accredited degree-granting institutions, independent research and policy organizations, and nonprofits with experience running field experiments at scale, while the fund remains global despite US-centric examples.
Anthropic Economic Index: Insights from Claude 3.7 Sonnet
Anthropic’s second Economic Index report examines how Claude.ai usage changed after Claude 3.7 Sonnet launched, using 1 million anonymized Free and Pro conversations from the following 11 days. Clio maps conversations to 17,000 tasks in the U.S. Department of Labor’s O*NET database, enabling comparisons across occupations and interaction modes, while the report also introduces a 630-category bottom-up taxonomy. Coding, education, and science usage increased modestly, and extended thinking appeared most often in technical and creative work, including computer science research, software development, multimedia art, and video game design. Augmentation remained 57% of usage, learning interactions rose from about 23% to 28%, and the report releases task- and occupation-level datasets covering thinking mode and augmentation-versus-automation patterns.
New book: Intercom on Jobs
Intercom launched its fourth book, Intercom on Jobs-to-be-Done, to document lessons from applying the Jobs-to-be-Done framework to product, design, and marketing for software. The book addresses a gap: although the framework is nearly 30 years old and often discussed through physical products, few materials cover its application to software companies. Intercom says the framework helped it understand the needs customers addressed with its product and improve their experience. Rather than offering a prescriptive, step-by-step method, the book presents hard-learned lessons intended to be timeless and universal, including customer switching, real competitors, product scope, exact jobs, Job Stories versus personas, and software research.
The AI deployment gap is widening, don’t get left behind
The 2026 Customer Service Transformation Report examines how organizations are adopting AI in customer service, based on a survey of over 2,400 global customer service professionals. It identifies a widening difference between narrow deployments that handle simple questions or workflow steps and mature deployments that integrate AI into critical operations, assign it more responsibility, and improve it continuously. Although 62% of respondents report improved customer-service metrics after implementing AI, mature teams report this at 87%, while only 10% say deployment is mature. The report also finds mature deployment associated with higher quality and consistency, clearer ROI measurement, and less time spent handling support volume; 43% report higher quality and consistency. With customer experience becoming the top priority for 58% in 2026, support is positioned as a proving ground for expanding AI into customer success, marketing, and sales, with 52% planning that scale.
New research: Customer service team evolution
An analysis of 166 interviews examines how customer service roles, workflows, and team structures changed after adopting Fin or similar AI agents. Using interviews with Intercom customers or prospects, the researchers cleaned conversation transcripts with Python and coded them through the OpenAI API against a closed codebook. Workflow disruption was reported by 94.58% of participants, while 82.53% reported changed responsibilities; 27.71% reported headcount or hiring effects, and 6.02% reported changed team structures or reporting lines. The findings describe hybrid human–AI operations in which AI handles triage, routing, translation, repetitive responses, and some refunds, while people increasingly oversee quality, testing, metrics, and complex or sensitive interactions, although organizational strategies and required skills remain uneven.
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