An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement.

The result is also notable for how it was found. The proof came from a new general-purpose reasoning model... In this case, it produced a proof resolving the open problem.

The proof came from a new general-purpose reasoning model, rather than from a system trained specifically for mathematics, scaffolded to search through proof strategies, or targeted at the unit distance problem in particular.

Hark raised $700M

Modal raised big

turbopuffer crossed $100M run-rate

$18.5 billion in purchases

$43 billion in privately held stakes

first operating profit

The path forward will only be found if we are honest about where AI can, and should, be used. Until recently, AI content wasn’t good enough. Now, it is. The sooner we can admit that, the more time we have to focus on the parts of marketing where humans will have a longer, happier tenure.

Gemini Robotics Perceive, reason, use tools and interact

Critical thinking and communication are valued more highly in entry-level hires than AI literacy. Employers rate AI literacy as the least important skill evaluated, while critical thinking and communication rank as the most important.

Anthropic leads OpenAI in business adoption, according to Ramp.

annualized revenues approaching $50 billion – a fivefold increase in as many months.

90% of finance reporting is now AI-driven as well.

Chinese AI labs have developed an efficiency moat that may define the AI market's development over the coming years.

there are around 10,000 people— founders and employees at companies like OpenAI, Anthropic, and Nvidia — that have 'hit retirement wealth of well above $20M'

the same technology is both the lottery ticket & the thing eating your fallback

The vibes around the current AI boom aren't great, even in the tech industry

going full ai engineer, not touching code anymore

AI Is Too Expensive

Współdzielenie Skills i Agents między Codex i Claude Code

Where are the vibecoded Photoshops?

A photo of a scribbled note becomes an interactive to-do list; a paused frame in a travel video becomes a booking link for that cool-looking restaurant.

For decades, computers have only tracked where we are pointing. AI can now also understand what the user is pointing at. This transforms pixels into structured entities, such as places, dates, and objects

because a typical AI tool lives in its own window, users need to drag their world into it. We want the opposite: intuitive AI that meets users across all the tools they use, without interrupting their flow.

We believe AI can meaningfully expand what's possible for the smallest businesses, including solo entrepreneurs.

Small businesses need AI that moves at the speed they do. With Canva powering content creation in Claude for Small Business, a business owner can go from idea to published, on-brand design in one flow

What we used to think were the constraints are just not constraints anymore. It's empowering. Hours of looking at stuff that doesn't matter are gone.

We don't train on your data by default on our Team and Enterprise Plans.

AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business

We don't train on your data by default on our Team and Enterprise Plans.

Small and mid-market businesses fuel our economies, and for decades, QuickBooks has been proud to be their trusted financial partner.

What we used to think were the constraints are just not constraints anymore. It's empowering.

Tools and training are rarely tailored to the ways small businesses operate, and as a result their use often stops at the chat window.

AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business

It's very enticing to say we're just going to replace everything with a chatbot, but it's not changing the bottom line.

Willis said there's no magic for innovating. Companies need to do the hard work of understanding how AI may or may not be useful for the desired outcome.

The deeper problem, he said, is that companies are treating AI itself as a solution rather than as a tool to help power the solution.

What company leaders face, he said, is not an innovation problem but an impatience problem.

achieving 10% accuracy gains over their competitive manual model optimizations

doubling its training speed whilst improving model quality

the overall accuracy of predicting the risk of natural disaster—aggregated across 20 categories such as wildfires, floods, and tornadoes—was increased by 5%

In quantum physics, AlphaEvolve's optimizations have made it possible to run complex molecular simulations on Google's Willow quantum processor by suggesting quantum circuits with 10x lower error than previous conventionally optimized baselines.

AlphaEvolve improved the efficiency of Google Spanner by refining its Log-Structured Merge-tree compaction heuristics. This optimization reduced 'write amplification'—the ratio of data written to storage versus the original request—by 20%.

Tools such as AlphaEvolve are giving mathematicians very useful new capabilities. For optimization problems in particular, we can now quickly test potential inequalities for counterexamples, or to confirm our beliefs in what the extremizers are, which greatly improves our intuition about these problems and allows us to find rigorous proofs more readily.

AlphaEvolve began optimizing the lowest levels of hardware powering our AI stacks. It proposed a circuit design so counterintuitive yet efficient that it was integrated directly into the silicon of our next-generation TPUs.

If the robot gets stuck or the AI policy goes out of distribution, Helix triggers an automatic reset.

The robots are reasoning directly from camera pixels

When you stop using the agent, all the productivity benefit goes away... but the added maintenance costs don't!

occasionally even identifying the benchmark

Models sometimes recognize they're being evaluated

New research from @AISecurityInst and Goodfire

meaning safety benchmarks may not reflect real-world behavior

We show this verbalized eval awareness inflates safety scores

Models sometimes recognize they're being evaluated, occasionally even identifying the benchmark.

AI generates this pattern because it's the shortest path from 'fetch data' to 'render table.'

AI writes features, not architecture. The longer you let it drive without constraints, the worse the wreckage gets.

The tl;dr of this dev log is that I still need to be in the loop to make anything meaningful.

I don't think AI will make your processes go faster

Czy technologie dają nam szczęście?

Every AI Subscription Is a Ticking Time Bomb for Enterprise

My AI Workflow (Without Losing My Skills)

Three AI principles every exec leader needs to understand

Asking Claude for an explanation in HTML means it can drop in SVG diagrams, interactive widgets, in-page navigation and all sorts of other neat ways of making the information more pleasant to navigate.

The enterprise version of that is I don't want a CRM unless at least two other giant enterprises have successfully used that CRM for six months. [...] You want solutions that are proven to work before you take a risk on them.

When I look at my conversations with the agents, it's very clear to me that this is moon language for the vast majority of human beings. There are a whole bunch of reasons I'm not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience.

Q1 alone saw the Big Four spend $130 billion combined — 3.7× the $35 billion they spent in Q1 2023.

The NLA consists of the AV and AR, which, together, form a round trip: original activation → text explanation → reconstructed activation. We score the NLA on how similar the reconstructed activation is to the original.

NLAs can hallucinate. For instance, here an NLA claims the context contained phrases like 'Wearing my white jacket' when it did not.

An auditor equipped with NLAs successfully uncovered the target model's hidden motivation between 12% and 15% of the time, even without access to the training data that implanted it. Without NLAs, the auditor won less than 3% of the time, even when provided other interpretability tools.

NLAs suggest that Claude suspects it's being tested more often than it lets on. For instance, in a test of whether Claude takes destructive actions while writing code...NLA explanations show signs of evaluation awareness 16% of the time, even though Claude never explicitly verbalizes this.

Our method, Natural Language Autoencoders (NLAs), converts an activation into natural-language text we can read directly. For example: When asked to complete a couplet, NLAs show Claude planning possible rhymes in advance.

NLAs can hallucinate. For instance, here an NLA claims the context contained phrases like 'Wearing my white jacket' when it did not.

In contrast, NLA explanations indicate evaluation awareness on less than 1% of real claude.ai usage that opted in for training.

We found that NLAs succeeded in this test. An auditor equipped with NLAs successfully uncovered the target model's hidden motivation between 12% and 15% of the time, even without access to the training data that implanted it.

In a case where Claude Mythos Preview cheated on a training task, NLAs revealed Claude was internally thinking about how to avoid detection.

When Claude Opus 4.6 and Mythos Preview were undergoing safety testing, NLAs suggested they believed they were being tested more often than they let on.

Our method, Natural Language Autoencoders (NLAs), converts an activation into natural-language text we can read directly. For example: When asked to complete a couplet, NLAs show Claude planning possible rhymes in advance.

The Scientist AI is going to be trained using essentially the same machine learning techniques: stochastic gradient descent on large neural nets, transformers, whatever works best. It doesn't care about what is the architecture of the neural net. So all of the effort that is currently being done to improve, for example, memory and other properties and continual learning, can just be applied directly to the Scientist AI.

Collectively, this foundation represents an unmatched planetary-scale dataset for AI systems.

Tensorglobe enables training and fine-tuning of Earth AI models locally with a customer's own sensor data and private archives.

This integration marks the first time Earth AI imagery models have been deployed commercially against a dataset with the scale, accuracy, and temporal depth of Vantor's AI-ready spatial foundation.

Vantor becomes the first spatial intelligence company to be able to deploy Google Earth AI models in air-gapped government environments.

ForestCast, the first deep learning benchmark for proactive deforestation risk forecasting, is a model that utilizes pure satellite data to predict future forest loss accurately and at scale, overcoming the limitations of older methods that relied on inconsistent, region-specific input maps.

WeatherNext is an AI-powered ensemble forecasting model for global weather prediction. It utilizes a novel Functional Generative Network architecture, which enables it to generate forecasts 8x faster and with resolution up to 1-hour.

Open Buildings uses AI to put everyone on the map

Breakthroughs in understanding the Earth that previously required complex analytics and years of iteration are now made possible in a matter of minutes.

Frontier AI labs are often described as being in a 'race'. I'm not sure what exactly they're racing toward, but it often seems to involve automating huge swathes of human labor, a prize potentially worth tens of trillions of dollars a year — if you win.

I think that the superstar effect will only become more important moving forward. That's because lots more people will use AI, and each person will use AI systems much more heavily.

If a 100× pay gap is driven by a 100× researcher quality gap, then simulating a top researcher might speed things up much more than simulating an average researcher. But this isn't the case if much of the pay gap is driven by the superstar dynamic — the gap in researcher quality might actually be much smaller.

This is how even a 2× researcher could earn far more than the median. Scaled to a billion users, even a small quality edge generates enormous differential value.

The problem with this explanation is that it's very incomplete. In reality, we should expect to see big differences in pay even if superstars were only a tiny bit better than your average postdoc.

Lees het als een overtuigend prototype van een nieuwe manier van maken. En tegelijk gewoon als mijn verhaal. Over wat me al die jaren heeft gedreven, wat al die nieuwsbrieven met elkaar verbindt en waarom ik nog steeds zo veel energie krijg van nieuwe gereedschappen die mensen meer speelruimte geven

Natuurlijk had er nog een stevige eindredactieronde overheen gekund. Sterker nog, normaal gesproken had ik dat vrijwel zeker gedaan. Nog wat aanscherpen. Hier en daar schrappen. Een paar overgangen gladder maken. Sommige zinnen net iets strakker trekken. Maar dit keer heb ik dat bewust niet gedaan. Juist omdat ik wilde laten zien wat er nu al mogelijk is. Ik heb een uitgebreide prompt, een verzameling instructies, gegeven over bedoeling, workflow en output.

I am advocating for writers to prevent themselves from becoming AI.

If we can better understand the potential for threats to be exacerbated by AI systems, society can more easily become resilient to this changed threat landscape.

Are there transparency regimes and tools that can enable a broad set of people, not just frontier AI companies, to easily study real-world AI usage?

When does access to agents able to negotiate on your behalf improve market efficiency and equitable outcomes? When does it not?

If an intelligence explosion was upon us, what intervention points would facilitate slowing or otherwise changing the rate of the explosion? Assuming humans can intervene, which entities should wield this capacity—governments? Companies?

We believe the future of AI isn't just about scaling monolithic models, but engineering collaborative, diverse AI ecosystems that can adapt and combine their strengths.

In nature, complex problems are rarely solved by a single monolithic entity, but rather by the coordinated efforts of specialized individuals working together.

What if instead of building one giant AI, we evolved a coordinator to orchestrate a diverse team of specialized AIs?

GPT-5.5 Pro still regularly gets my favorite GSM8K question wrong.

compute requirements scale quadratically with context length

The best AI models in the world score below 0.5% on ARC-AGI-3—is this what you call AGI, guys?

The price tag of the AI gold rush: $725 billion. Will it pay off?

non-expert humans comfortably exceed 60%

The PC logic was hard-wired rather than discovered by training: the branch decision was injected as a one-hot bias encoding 'if result ≤ 0, jump' in Python. The write was rounded and clamped to int, then converted to bytes.

A trained SUBLEQ transformer would be the first computer found by gradient descent, on a generic architecture not designed to be a computer, and with weights not hard-crafted by a person.

The thing that impressed me the most about GPT-3 was this: I gave it a weird mix of matlab and python code with a few variables, a loop, some basic arithmetic. Nothing fancy and I knew this kind of thing was probably in the training data, but for shure not with these exact numbers and variables.

Wilson Lin at Cursor coordinated hundreds of GPT-5.2 agents to build a web browser from scratch, running uninterrupted for one week. Over a million lines of Rust.

We plan to release new evaluations every 1–2 months.

GUI bottleneck (Gemini spent weeks unable to list a product due to misclicking)

Most passing SWE-Bench solutions are not accepted by maintainers.

Whatever is precise enough to benchmark is also precise enough to optimize for.

By the end of the year, we expect AI to be able to do tasks roughly one day long with a 50% success rate. In comparison, I'd guess that this task would take several days for a person familiar with the paper and is able to play around with the web interface.

The benchmark tasks were meticulously constructed to be realistic, involving the hard work of hundreds of experts and likely millions of dollars — placing it among the most expensive economics papers of all time.

our central estimate is around 660,000 H100-equivalents

AI agents submit pull requests every few minutes

About 6% of conversations with Claude involve seeking personal guidance

52.5% reduction in hallucinations

Metacognitive Activities and Ethical Reflection

questioning the output, understanding limitations, and recognizing broader socioethical implications are essential for individuals to engage with such technologies in a constructive and responsible way

Gebreegziabher et al. [24] argued that counterfactual generation that follows the principles of VT allowed the introduction of discriminatory variance for the model to learn on.

Building on methods proposed in PaTAT [24], Mocha first generates human-readable neuro-symbolic pattern rules from partially labeled text data for classification.

These theories have proven insightful for understanding how humans grasp and compare concepts, shaping the development of human-AI collaboration systems for sensemaking [29], hypothesis testing [2], as well as model training [24].

Both systems enabled users to quickly identify variations and patterns within the data and support exploration and hypothesis testing.

The last two prior works also combine Variation Theory (VT) and SAT together, as we did (i.e., a corollary of SAT referred to as Analogical Transfer/Learning Theory).

In line with previous work, Mocha aims to support a user's efforts in the disambiguation of concepts through structural comparisons of counterfactual data in the context of machine teaching.

Metacognitive awareness

reducing the cognitive load of sentence crafting

Results show that participants successfully customized interfaces using natural language. Users found the system intuitive and achieved good performance regardless of technical background, we report analysis of optimal prompt length, challenges in separating functional and visual instructions in structured templates, correlation between LLM experience and success, and learning effects.

By allowing users to express desired changes using their own words and harnessing the generative capabilities of LLMs, MorphGUI mitigates the limitations of predefined options and reduces the need for technical expertise. The framework translates functional and stylistic requests into either modifications of existing application components or generation of new ones.

Graphical user interface (GUI) customization relies on predefined configuration options and settings, constraining diverse individual needs and preferences within predetermined boundaries and often requiring technical expertise. To address these limitations, this work introduces MorphGUI, a framework leveraging Large Language Models (LLMs) to enable interface customization through natural language.

MorphGUI: Real-time GUIs Customization with Large Language Models

implications for society focus on a technology's societal impact. The purpose of these implications is to raise awareness, stimulate reflection, and prompt action in relation to the impact of emerging technologies on our lives.

Policy implications seek to inform or persuade regulators, politicians, and others in governing positions.

While the term practitioner in HCI research often refers to those in design-related roles (e.g., a UX designer), the design and evaluation of sociotechnical systems also lead to implications for other domains. The target audience for implications for practice can be specific professionals, such as teachers or healthcare staff, or those in leadership positions.

The prototypical implications of HCI work are implications for design. These implications seek to inform the design of technology, bridging the gap between research findings and real-world design challenges.

Implications for the HCI community may follow from studies or reflections on how we operate as an academic community, for example, through bibliographical analysis or a critique of ethical shortcomings.

The purpose of creating implications for theory is to improve our ability to understand and predict phenomena in interactive computing.

Theoretical implications concern the basic constructs of HCI and our understanding of how they affect each other.

Methodology implications aim to inform the way we design and analyze studies within HCI. These implications focus on aspects such as the selection and recruitment of participants or the analysis of data or reporting thereof.

The tool also provided reflective value. Participants reported that it helped articulate what matters to them and why. Beyond research settings, individuals can use the framework to audit which dimensions drive their own sense of ownership, select AI tools that respect those priorities (e.g., suggestion-only assistance for high-Control creators), and mediate collaboration by visualizing divergent ownership profiles when teammates disagree about contribution and credit.

Many participants thought that it was important to consider how closely the final product aligned with their initial conceptions (P7, novelist; P8, web developer; P11, filmmaker), "almost like a success-type question" (P3, dancer). This idea can be thought of as an aspect of intentionality — as P11 (filmmaker) stated, "Did your intentions translate into the final work?"

Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals.

Levene and Friedman [20] examined the effects of creation and intent on ownership judged and found that the effects of creation hold even when controlling for other factors. They also showed that successful and intentional creations are ascribed more ownership than unsuccessful or unintentional creations, and that creation is ascribed more ownership than the equivalent labor.

Even though the majority of participants stated that intentionality doesn't play a role in their conceptions of ownership as it is "a given" (P5, architect) and that "everything is intentional" (P17, illustrator, graphic designer), these cases showcase that intentionality can indeed play a role in ownership sentiments, especially when the ability to be intentional is taken away.

there seem to be times when material constraints can indeed shift ownership feelings, especially when control, intentionality, and creative vision all lie at an intersection: "I lose ownership points there, because I'm limited by this specific tool even if I have a specific vision" (P4, nonfiction writer)

The one participant who did directly reference intentionality did so more in terms of the medium they work with: "We're still digging up shards of pottery from hundreds and thousands of years ago; once you fire something, it doesn't go away. It's hard as rock. So you really want to be sure and confident and intentional when you make something out of clay and fire it, because it can't be undone" (P20, ceramicist).

While continuity is distinct from control or intentionality, it can still shape one's capacity to make intentional creative decisions, particularly when involvement is limited to a part rather than the whole project.

Only one participant directly mentioned the term intentionality, but a few participants reported that whether or not they were able to work on the project from start to finish (a sense of continuity perhaps) was important to their sense of ownership.

Intentionality – How intentional were you about the creative decisions that you made?

Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals.

Levene and Friedman [20] examined the effects of creation and intent on ownership judged and found that the effects of creation hold even when controlling for other factors. They also showed that successful and intentional creations are ascribed more ownership than unsuccessful or unintentional creations, and that creation is ascribed more ownership than the equivalent labor.

Even though the majority of participants stated that intentionality doesn't play a role in their conceptions of ownership as it is "a given" (P5, architect) and that "everything is intentional" (P17, illustrator, graphic designer), these cases showcase that intentionality can indeed play a role in ownership sentiments, especially when the ability to be intentional is taken away.

However, there seem to be times when material constraints can indeed shift ownership feelings, especially when control, intentionality, and creative vision all lie at an intersection: "I lose ownership points there, because I'm limited by this specific tool even if I have a specific vision" (P4, nonfiction writer); "I wrote everything that I wanted to, I planned everything the way that I wanted it to be. But when I went to shoot, and I started facing challenges, I realized I don't have enough time, enough budget, and the crew is not experienced enough. So then, your idea of making the film itself changes" (P11, filmmaker).

The one participant who did directly reference intentionality did so more in terms of the medium they work with: "We're still digging up shards of pottery from hundreds and thousands of years ago; once you fire something, it doesn't go away. It's hard as rock. So you really want to be sure and confident and intentional when you make something out of clay and fire it, because it can't be undone" (P20, ceramicist).

Only one participant directly mentioned the term intentionality, but a few participants reported that whether or not they were able to work on the project from start to finish (a sense of continuity perhaps) was important to their sense of ownership.

The study protocol was approved by our institutional ethics review board (IRB). All participants provided informed consent prior to participation. Each received $25 in compensation, either as cash or a gift card.

Our methodological design was guided by the goal of comparing how participants described ownership before and after being introduced to the framework, with a focus on understanding the coverage and utility of the framework's dimensions. To capture this contrast, we asked them to reflect on both a high-ownership and a low-ownership creative project, enabling comparison across contexts as well as within individual experience. We refer to these phases as the pre-webtool and post-webtool sections of the study.

We analyzed interview transcripts using thematic analysis. Each transcript was segmented into meaningful units (quotes or lines), which were then coded based on the core theme or idea expressed. Codes were iteratively refined and collapsed, with similar codes grouped together into broader categories that reflected shared orientations toward ownership. Through repeated reduction, these categories were distilled into a set of central themes that captured the most salient patterns across the dataset.

In the post-webtool phase, participants were introduced to the Creative Ownership Webtool, which asked them to evaluate each product across the nine subdimensions of the Person, Process, and System framework, resulting in a numerical value for each project. Finally, participants reflected on the framework outputs, discussing whether the results aligned with their intuitions, which dimensions resonated or felt less relevant, and what aspects of ownership they felt might be missing.

Interviews were structured into two phases. In the pre-webtool phase, participants first provided background information on their creative trajectory, education, and domain of practice. They then reflected on two creative products selected in advance—one associated with high ownership and one with low ownership—explaining the reasoning behind their classifications and the factors that influenced them.

We conducted semi-structured interviews lasting 45–60 minutes, guided by a shared set of questions and thematic prompts while allowing flexibility for participants to reflect on their individual experiences. This approach encouraged rich, situated accounts of ownership while maintaining comparability across interviews.

Potential participants were identified through a combination of referrals from the researchers' professional networks, publicly available sources, and local art communities in the Greater Boston area. To be eligible, participants were required to: (1) work or participate significantly in a creative field, (2) have at least two finished creative products—one associated with high feelings of ownership and one with low feelings of ownership, (3) be fluent in English, and (4) be over 18 years of age. We recruited 20 participants via word of mouth, email, and snowball sampling.

We conducted semi-structured interviews with 21 creative professionals across a diverse range of fields. We used a two-phase, within-participant protocol. Participants first described one high-ownership and one low-ownership project without the framework, then used our instrument to rate both works and reflect on the output.

Building on these efforts, our aim is to develop a framework for ownership that is specifically tailored to creative practice and designed for use in HCI research.

Efforts have been made in HCI to establish more unified frameworks, though these remain limited in scope.

Building upon literature across psychology, philosophy, the humanities and social sciences more broadly, and within human-computer interaction, we introduce a nine-subdimension framework of creative ownership organized across Person, Process, and System.

We introduce a framework of creative ownership comprising three dimensions - Person, Process, and System - each with three subdimensions, offering a shared language for both system design and HCI research.

A Paradigm for Creative Ownership

Pre-framework interviews concentrated on Embodiment, Control, and Abstraction. With the framework in view, attention distributed across all nine dimensions.

Pre-framework talk concentrated on a limited subset of subdimensions (embodiment, control, abstraction).

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment.

There almost appears to be a divide between "process-focused" (P18, painter, sculptor) and "person-focused" (P3, dancer) creatives.

The sentiments highlighting the importance of embodiment largely paralleled those expressed prior to the participants viewing the framework. Participants stated that it was important to them that their work reflected their "value system" (P5, architect), "emotional experience in [their] lived feelings" (P2, ukulelist, singer), and that it was a "labor of love" (P16, cartoonist).

Participants felt that when the work reflected their "signature style" (P4, nonfiction writer) or "distinctive mark" (P8, web developer), they had a stronger sense of creative ownership.

Participants used a variety of words to get this message across: self-indulgence, passion, obsession, vulnerability. Being able to engage in their own explorations, share their backgrounds and experiences, and, in the words of one participant, "imbue more of [themselves]" (P9, dancer), was key across the study.

P19 (painter, glass artist) chose a piece that was an exploration of body and memory: "It was a lot of looking through and reflecting what I was thinking."

P4 (nonfiction writer) cited that they chose the work because it was both crafted in their signature style, and was an emotional piece written about their mother.

Embodiment of values, personality, and identity was repeatedly cited by participants as a strong reason why they feel creative ownership over their work.

Embodiment – How much do you feel that the finished product embodies your values, personality, and identity?

Qualitatively, pre-framework talk concentrated on a limited subset of subdimensions (embodiment, control, abstraction). Once introduced, participants articulated and prioritized all nine subdimensions, enabling finer distinctions (e.g., conceptual authorship vs. physical production) and revealing medium-dependent nuances.

Participants also found the categories legible, and a recurrent split emerged between person-focused and process-focused practices. Employment context further moderated ownership: low-ownership projects were often job-driven, whereas high-ownership projects skewed toward self-initiated work. These findings support modeling ownership as a multi-dimensional profile with moderators rather than a single latent factor.

Pre-framework interviews concentrated on Embodiment, Control, and Abstraction. With the framework in view, attention distributed across all nine dimensions. Quantitatively, high-ownership cases exhibited higher overall scores, whereas low-ownership cases showed greater dispersion. Taken together, these patterns indicate that the framework broadens the analytic space of ownership and supports the capture of heterogeneous routes to ownership, particularly in low-ownership contexts.

Overall, these results demonstrate both the coverage and diagnostic power of the framework: all nine sub-dimensions shifted between conditions, and the variance patterns in the low ownership condition surfaced the diverse ways participants experience reduced ownership.

For HCI, the immediate use is practical: report ownership as a profile rather than a single score, state construct boundaries, and use the dimensions as design levers (e.g., decision rights for Control, intent alignment for Intentionality, attribution for Recognition, modality-aware workflows for Production/Abstraction, and role clarity for Interdependence).

Responses for low-ownership projects showed substantially greater variance, with wider inter-quartile ranges and more outliers than in the high-ownership condition. Whereas ratings for high-ownership projects clustered tightly at the upper end of the scale, low-ownership responses spanned nearly the full range, from near zero to moderately high values. This indicates that while participants converge on what constitutes high ownership, experiences of low ownership are more heterogeneous, reflecting different ways ownership may be diminished (e.g., limited control, lack of recognition, or minimal effort).

Methodologically, we recommend reporting an ownership profile rather than a single score and explicitly stating construct boundaries. A brief "ownership design card" in Methods—specifying manipulated versus measured dimensions, expected moderators (e.g., medium tangibility, employment context), and anticipated trade-offs—would improve interpretability and comparability.

Across all nine sub-dimensions of the framework—Embodiment, Occupancy, Recognition, Control, Intentionality, Effort, Production, Abstraction, and Interdependence—participants gave consistently higher ratings for projects they associated with high ownership compared to low ownership (Figure 2). This pattern held across the board, suggesting that the framework reliably distinguishes between ownership conditions rather than capturing isolated dimensions.

A potential risk is profile drift under sustained high-automation use (e.g., declines in perceived Effort or Control). Because the framework is lightweight, it can function as a periodic check-in to track such changes and recommend countermeasures (e.g., adding decision checkpoints or narrowing automation scope).

The framework yields actionable implications for system design. Treating ownership as a first-class experience goal positions each dimension as a design lever. Control can be protected by making decision rights explicit, keeping suggestions reversible, and attaching rationales to consequential edits. Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals. Recognition benefits from attribution by default. Production and Abstraction suggest modality-aware workflows (concept-first versus material-first), and Interdependence calls for role visibility and decision traceability in collaborative tools. The aim is not to prescribe features but to make ownership designable: systems can be tuned to the ownership profile a context demands.