Offloading work tasks to AI comes with a cost – to our brains

Jongkil Jay Jeong, The University of Melbourne; RMIT University; Deakin University

Image: For illustration purposes, created by DIW with GenAI tools.

Imagine your team has been tasked to deliver a high-stakes policy paper under intense time pressure. Everyone turns to generative artificial intelligence (AI) and within minutes, it delivers a full draft complete with structured arguments and authoritative-looking citations.

The team then transfers the AI-generated text into the corporate template, polishes the narrative flow and structure, and gives it one final check before sending it up the chain. Leadership takes a glimpse of the polished-looking draft, and – assuming the underlying research has already been verified – signs off and publishes.

This scenario is not hypothetical. South Africa’s Draft National AI Policy had to be withdrawn earlier this year after reviewers found a number of the citations pointed to journal articles and authors that didn’t exist. Ironically, the mistakes were introduced by AI.

Similarly, last year, consultancy firm Deloitte provided a partial refund to the Australian government after admitting that generative AI had been used to help produce a commissioned document which contained fabricated citations and referenced quotes from sources that simply did not exist. The report cost Australian taxpayers A$440,000.

But the cost of letting AI do our work and thinking also comes at a cost to our brain.

Value of thought

As seen in the examples above, work tasks produced by AI often look polished on the surface but carry significant underlying flaws. This is a byproduct of how the modern workplace has conditioned us to prioritise the final deliverable – the report, the presentation, the assignment – over the actual process.

However, this strictly outcome-driven approach rarely rewards the process by which results are obtained. When we skip the process, we lose the ability to fully grasp what the output actually means.

This will inevitably result in us finding it challenging to critically evaluate the outcomes we consume. More importantly, it will also impact our capacity to discern facts from partial truths and lies.

Ultimately, the true value of knowledge work lies not just in the final deliverable, but in the clarity of thought and the messy, iterative process required to achieve it.

Blindly relying on AI

Recent empirical evidence also highlights a severe disconnect between perceived benefits of using AI and its improvement in quality.

A 2025 study by the University of Melbourne and accounting firm KPMG surveyed more than 48,000 respondents across 47 countries. It found two in three people (66%) use AI on a regular basis, while more than half believe their performance benefits from its usage.

The study specified that the primary driver of AI adoption was the fear of missing out (48%), leading users to prioritise the speed of delivery over the quality of the final output.

Yet 61% of respondents said they have had no AI training, while 60% also reported inappropriate, complacent and non-transparent use of AI in their workplace.

A separate study from July 2026 by the Centre of AI Safety, a San Francisco-based nonprofit research organisation, found even the top-performing AI agents failed to complete roughly 85% of projects to a standard acceptable for commissioned work.

This indicates that even today’s best AI models still fall short of professional quality on most projects. It also shows we are relying on AI to generate final deliverables without understanding how it gets there.

This blind reliance strips away quality control, replacing genuine knowledge creation with an ever-increasing volume of automated “work slop”.

Improving cognitive skills

So how do we reverse this trend?

The answer isn’t simply banning or limiting AI in the workplace. Instead, we need to redefine our working relationship with AI and the outcomes it produces by taking a more human-centric approach.

First, organisations must set realistic time frames for deliverables that genuinely allow for people to think rather than focus on doing things quicker. There is a need to actively disconnect from AI tools during the brainstorming and structuring phases so ideas and thoughts are driven by actual human synthesis, not just algorithmic prediction.

Traditional whiteboard sessions – where teams physically map out their initial concepts through markers and pens – is a way this may be facilitated.

Second, we must cultivate a culture that is constructively critical of all outputs, whether generated by humans or AI.

This means actively questioning logic, structure, references and underlying assumptions. When teams are expected to defend the behavioural reasoning and methodology behind their work, it very quickly reveals who actually did the thinking and who merely copy-pasted from an AI agent.

Perhaps creating a critique group can create a safe, comfortable environment where feedback and comments can flow freely.

Technology can assist with certain processes, but it is dangerous to assume it can replace our ability to think critically. This is because the most valuable asset we have isn’t the polished final page – it’s the ebbs and flows of the cognitive process that is required to honestly write and evaluate one.

Jongkil Jay Jeong, Senior fellow, The University of Melbourne; RMIT University; Deakin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

Read next: Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

by External Contributor via Digital Information World

Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

By Inderscience

Research in the International Journal of Sustainable Development has looked at eight of the nations involved in the Second World War, both Allied and Axis countries, and shows how that period of history helped set societies on the path to the resource-intensive modern economies we have today. The research links wartime mobilisation to the period known as the Great Acceleration in energy use, material consumption, and ultimately detrimental environmental impact.

The study looks at societal change from before the war, 1935, to the post-war recovery period and the boomer years up to 1960. Demographics, economic activity, power supply, material and resource flow, and environmental impact are all examined. Three major consequences of WWII are seen. First, acceleration, in which existing trends become even more intense. Secondly, redirection where development shifts towards new technologies and the opening up of novel resources. Thirdly, reset, in which destruction or political upheaval changed the direction of nations from the paths there were on before the war.

The researchers use the term "socio-metabolic transition" to describe the various changes in power consumption and physical resources in society. By adopting this almost biological model, they were able to connect wartime production and resource mobilisation with institutional and technological changes that persisted long after 1945.

Abrari, L., Rezaei, N. and Linnanen, L. (2026) 'World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows', Int. J. Sustainable Development, Vol. 29, No. 5, pp.1–62. DOI: 10.1504/IJSD.2026.155697

Image: Austrian National Library - Unsplash

Reviewed by Irfan Ahmad.

Read next: AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
by External Contributor via Digital Information World

AI bias isn’t just an error in the algorithm. It’s a chain of human decisions

Muneera Bano, CSIRO and Didar Zowghi, CSIRO

Image: Google DeepMind - Unsplash

In the United States, leading HR software company Workday is currently facing a lawsuit over its use of job screening tools powered by AI which allegedly discriminated against applicants based on factors such as age, disability and race.

The company, whose hiring software is widely used by large employers around the world, has denied the allegations.

The case is one of many examples of AI systems alleged to have caused discriminatory harm. When AI systems replicate and amplify discrimination, they blur the boundary between technical error and systemic injustice, turning bias into a digital harm.

And while our first instinct might be to blame the algorithms, they don’t decide what they can generate, what safeguards are built into them, or how a company responds when incidents of discrimination are reported. People make those calls long before an AI produces any output.

That is why technical fixes to AI systems are not enough. What is needed is an overhaul of AI ecosystems to ensure they are more inclusive.

A broader pattern

AI systems quietly narrow who gets seen as competent, employable or fit to lead.

For example, in a 2025 study, we tested how two AI models, OpenAI’s GPT-4 (which has now been retired) and Microsoft Copilot, represented software engineers in a simulated recruitment exercise: 300 candidate profiles for four job roles, followed by recommendations and generated images of each AI model’s preferred candidates.

Both models favoured male profiles, especially for senior roles. Their images also skewed towards engineers who were younger, slimmer, and lighter-skinned. The models were reproducing associations embedded in language, imagery, employment records and assumptions about who belongs in the profession.

These outputs don’t stay contained to a research study. AI-generated recommendations are entering hiring, education and public services.

This matters when certain demographics and women remain underrepresented in AI development and leadership, while being disproportionately exposed to its harms.

The problem is not limited to gender and race.

Even when AI systems operate across different languages and cultures, they often reproduce predominantly western values, assumptions and ways of understanding the world. The wealthy countries have become the main beneficiaries of AI, which widens global inequality.

In another study from 2025, we manually reviewed reported AI incidents.

Almost half involved a diversity or inclusion issue, with racial, gender and age discrimination most prominent. The harms traced back to different points in the AI development lifecycle: non diverse training data, and neglected diversity and inclusion principles during design, development and deployment.

Why technical fixes are not enough

Technical work matters, including bias identification, re-balancing datasets and adjusting outputs. But these fixes often treat bias as a property of the AI model, when much of it originates from outside the system.

Data does not enter an AI system as a neutral record of reality.

People decide what data to collect, how to label and categorise it, and whose experiences are important. These decisions are shaped by history, cultural norms, institutions and existing power imbalances.

Wherever society has linked leadership with men, technical skill with lighter skin, or innovation with youth, AI models learn from those associations and formalise, automate, and repeat them at a larger scale.

Bias also usually appears through the intersection of multiple identities, such as gender, race, age, disability and class. A system that looks fair when each identity is tested separately can still disadvantage people at the overlap of several.

Building a more inclusive AI ecosystem

That’s why building a more inclusive AI ecosystem requires interdisciplinary knowledge, such as educating AI engineers about social science theories to help them understand the social origin of bias.

Inclusive AI is not about political correctness; it is about upholding human rights, preventing harm, ensuring justice, and building trust.

It also requires genuine participation from affected groups and sustained attention to the power structures these systems operate within. AI development teams should test not just whether a model is accurate, but whether its benefits, errors and harms distribute fairly across different groups.

Together, this would help ensure tech companies better understand the nature of a bias once it’s manifested through AI and therefore develop new methods or tools to minimise the harm it causes.

Organisations that adopt AI also need stronger governance to monitor how the technology behaves. This could include, for example, having someone accountable for reviewing risk and responding to incidents and monitoring systems once they are live.

Algorithms don’t decide which data matter or what level of risk is acceptable. People make these choices. It’s high time tech companies remember that. The focus should not just be on fixing a biased algorithm, but rather on examining the human decisions that allowed the risk of harm, and who was missing when those decisions were made.

Muneera Bano, Principal Research Scientist, CSIRO and Didar Zowghi, Professor, Senior Principal Research Scientist, CSIRO

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-Checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

Read next:

• Artificial intelligence acts as an ‘ideological chameleon’ and may deepen political polarization, study finds

• The AI Skill Employers Value Most Might Be Human

by External Contributor via Digital Information World

The AI Skill Employers Value Most Might Be Human

By Ellie Stewart

Job seekers looking to increase their AI skillset in order to land their next role may be building AI skills that hiring professionals aren’t necessarily looking for.

New research by Adobe Acrobat reveals that there is a gap between the skills related to AI that people are acquiring and those that employers really value. Moreover, the idea that people should get more technical knowledge about AI appears to overlook the value of soft skills like judgment, teamwork, and critical thinking.

According to the Adobe data, the most popular technical skill among job seekers was brainstorming using AI, which accounted for 30% of surveyed job seekers. Then comes AI image generation and workflow automation, 19% each. But only 45% of people reported feeling comfortable with their skills to write prompts.

When it comes to recruiters, understanding where this gap exists can be important for hiring the best talent.

The new AI skillset expands beyond the prompt

Hiring managers prioritized time management above all other soft skills, with 47% selecting it, followed by adaptive problem solving at 44% and collaboration at 41%.

As for ethical AI supervision, it was chosen by 33% of hiring managers, whereas creative intuition was prioritized by 28%. Especially notable is that hiring managers prioritized ethical AI supervision 74% more often than prompt engineering.

This difference may acquire more significance as the availability of generative AI tools decreases its value.

In case almost everyone can ask the AI system to generate the picture, summarize a document or come up with some ideas, building a skillset that not only utilizes AI tools but thoughtfully partners with them may be what helps a job seeker stand out from the crowd.

According to the Adobe data, the biggest red flag related to AI in an interview process was a candidate's inability to check facts, cited by 70% of hiring professionals

Also, nearly half thought that taking full credit for work done with AI help is something to be worried about.

AI fluency is becoming a process, not a software skill

The current working landscape has changed what it means to be skilled with AI tools.

The first generation of workplace AI skills was based on usage and execution. Could an employee use a chatbot? Could he create an image? Did she understand prompting?

These execution based skills now serve as a foundation for a larger skillset.

The most ideal candidates would show their understanding of how to analyze outputs, fix mistakes and pick tools, as well as build processes for using AI. This means that being able to explain the reasoning behind the use of an AI tool and what to do next is just as important as using the tool itself.

This may also change the tools employees choose to use. While many believe the best Ai tools to be those that generate the most output, there are also tools that can provide meaningful assistance throughout other steps of a project’s process. These tools may allow users to better optimize their work and bridge the gap between their skills and the tools’ capabilities.

This illustrates a much more teamwork-based approach to AI tool use, where the user leans on technology as a creative partner but allows for increased responsibility for the final product.

Employers have a skills gap of their own

It is not the sole responsibility of employees to bridge the gap.

According to Adobe, over 90% of hiring managers stated that their organization offered training in the use of AI skills; however, knowledge sharing remains inconsistent. Even when looking at technology companies, only one out of three companies has a formal system in place to share knowledge of AI.

Moreover, the training is primarily focused on efficiency. Workflow automation was the most prevalent AI skill taught, accounting for 52% of courses offered, followed by AI brainstorming (48%) and prompt engineering (32%). AI output auditing, even though employers pay so much attention to this aspect, accounts for up only 30%.

This leaves businesses with their own challenge.

Organizations, if they want employees to use AI responsibly, question its outputs and have a good judgment regarding AI outputs, should train employees on such matters rather than offer them access to more tools.

The most valuable AI skill might be knowing what humans still need to do

The suggestion that technical understanding of AI is irrelevant does not hold much water.

According to the Adobe survey, nearly three in five hiring professionals state that AI mastery improves an employee’s job security. However, over 60% also agreed that they may still hire a candidate without basic AI proficiency.

This means that AI skill building in the workplace may be changing directions. The optimal AI user at work is someone who is capable of using AI technologies in a way that compliments their human skills, especially when it comes to decision making.

For job seekers, this means highlighting your ability to implement AI tools and explaining the thought process behind your work. For organizations working towards future workplace solutions, it means utilizing AI tools where they best support your workers and enhance their own skillsets.

The survey included 805 job seekers and 406 hiring professionals, with fieldwork conducted in late April 2026.

Reviewed by Irfan Ahmad.

Read next: Online conflict may strengthen, not weaken, social movements, McGill researchers find
by Guest Contributor via Digital Information World

Online conflict may strengthen, not weaken, social movements, McGill researchers find

By McGill University

Study on the 2019 global climate strikes shows how strangers can discover shared narratives and mobilize together.

Image: Chris Sansbury - Unsplash

Online conflict tends to advance social movements rather than undermine them, according to new research from McGill's Desautels Faculty of Management.

“Anyone who has watched an online debate turn heated assumes arguing kills collective action,” said Emmanuelle Vaast, Professor of Information Systems and the study's senior author. “Our study shows the opposite: the same social media dynamics that produce conflict also enable millions of strangers to discover they are telling the same story and act together. It explains how a teenager’s solo school strike could become a worldwide mobilization in a matter of months.”

The study was carried out in collaboration with colleagues at Northeastern University and the University of Massachusetts.

The researchers examined social media activity related to the 2019 global climate strikes sparked by Swedish activist Greta Thunberg – the largest climate mobilization in history – to understand how distinct causes, from pipeline opposition to the protection of monarch butterflies and wolves, fused into a single global “movement of movements.” They found that this process happens in three phases: exposure (movements discover each other’s issues), entanglement (conversations become tangled and contentious) and coalescence (participants converge on shared goals and joint action).

“Conflict did not weaken the movement,” Vaast said. “Co-operation and disagreement evolved together, like the two strands of a double helix. Arguing was part of how a shared agenda took shape.”

Previous research has mostly examined single movements and treated online conflict as a threat to collective action, the researchers said.

Nearly two million Twitter posts studied

The team used computational text-analysis methods to analyze about 1.9 million Twitter (now called X) messages posted between April and September 2019. Vaast noted that by combining semantic network analysis and natural language processing at this scale, the team could observe movement dynamics and identify patterns that may have been obscured by other methodologies, such as interviews or case studies.

Although social media platforms have shifted since 2019 from largely chronological feeds to recommendation-driven ones optimized to hold users' attention, the researchers said this does not necessarily impede movement building.

“Recommendation algorithms can actually accelerate discovery. For instance, TikTok surfaces [social] causes to people who never sought them out. So, coming together on social media is still possible today. It just runs through more opaque mechanisms,” Vaast said.

“That's exactly why the 2019 moment is worth studying: it's a benchmark for what today's algorithms are enabling.”

About this study

“Entangling and Coalescing on Social Media: A Study of Issue Boundary Spanning in Movement of Movements,” by Carol Lee (Northeastern University), Pratyush Bharati (University of Massachusetts) and Emmanuelle Vaast (McGill), was published in Information Systems Journal.

Reviewed by Irfan Ahmad.

Read next: Being Social Is Not a Priority on Social Media These Days
by External Contributor via Digital Information World

Trying to compete with China could slow global climate action, report warns

By Joe Stafford, University of Manchester

China’s dominance of batteries, electric vehicles and solar power is helping drive global decarbonisation - but creating tensions with the UK and other Western economies.

Image: Dominic Kurniawan Suryaputra - Unsplash

China’s dominance of key green technologies is helping to subsidise the global transition to a low-carbon economy, but trade tensions could make it harder for other countries to meet their climate targets, according to a new report led by researchers at The University of Manchester.

The report argues that China’s large-scale support for green industries has helped to make technologies such as batteries, electric vehicles and solar power cheaper and more widely available.

However, China’s dominance is also making it increasingly difficult for European and US companies to compete, creating a dilemma for governments seeking to decarbonise while protecting their own domestic industries.

Key findings and recommendations

• China’s state support for green industries is effectively subsidising the global transition to a low-carbon economy.
• China has become dominant in the so-called ‘new three’ green technologies - batteries, electric vehicles and solar power.
• Attempts by countries such as the US and EU states to compete with China could slow the adoption of green technologies and increase the cost of decarbonisation.
• Governments increasingly face four broad choices - compete with China, cooperate with it, accept Chinese dominance, or abandon parts of the green transition.
• The UK should strengthen cooperation with China on green technologies rather than trying to compete directly.
• The UK should continue to avoid tariffs on Chinese green technologies and seek Chinese investment in UK manufacturing.

A global climate dilemma

The report says China now occupies a leading position across many of the technologies needed for the green transition including solar, wind, batteries, hydrogen and electric vehicles. Its dominance has helped drive down the cost of these technologies, but this has also created growing geopolitical and economic tensions.

“China’s dominance of green technology presents a fundamental dilemma for governments,” lead author Dr James Jackson of The University of Manchester’s Sustainable Consumption Institute. “The world needs these technologies to decarbonise, but efforts to compete with China risk making the transition more expensive and more difficult.”

The authors argue that countries should focus more heavily on cooperation - particularly where access to affordable green technologies is essential to meeting climate targets.

Opportunities for the UK

The report identifies green technology as an opportunity to improve UK-China relations following a period of strained diplomatic ties. It recommends that the UK should seek closer trading relationships with China, while maintaining the ability to challenge Beijing on other issues such as human rights.

Among its recommendations, the report says the UK should encourage Chinese EV manufacturers to establish production facilities in Britain, while using the country’s expertise in financial services to support investment in green technologies. It also recommends that the Bank of England consider measures used by China’s central bank to support the development of green industries.

“The UK has a great opportunity to benefit from China’s expertise in green manufacturing while using its own strengths in areas such as financial services. Deeper cooperation could support both decarbonisation and the UK’s ambitions to develop a stronger green economy.” — Dr James Jackson

Publication details: Subsidising Global Decarbonisation: China-UK Relations is published by The University of Manchester. Dr Jackson is the lead author, alongside Mathias Larsen of the Grantham Research Institute on Climate Change and the Environment at the London School of Economics.

Reviewed by Irfan Ahmad.

Read next: AI Consciousness and the Cost of Being Wrong
by External Contributor via Digital Information World

AI Consciousness and the Cost of Being Wrong

On some questions, waiting for certainty is not caution. It is a bet, quietly placed, on the answer that happens to be cheapest.

Image: Mirella Callage - Unsplash

There is a particular kind of problem that punishes you for waiting until you are sure. Most of the time, the sensible response to a hard scientific question is patience: gather evidence, withhold judgment, resist the pull of a premature answer. But some questions are structured so that the act of waiting is itself a decision, one that quietly commits you to a course of action while you tell yourself you have not chosen yet. The question of whether advanced AI systems can have experiences, and whether qualia or consciousness can arise out of such AI systems, is beginning to look like one of those.

The reason has nothing to do with any claim that today's systems are conscious. Most careful researchers in the field do not make that claim, and neither will this argument. The reason is about the shape of the uncertainty, and what follows from taking it seriously. When you cannot rule something out, and the cost of being wrong about it is severe and irreversible, the demand for certainty before acting stops being rigor and becomes a gamble wearing the costume of caution.

Two ways to be wrong

Start with the structure of the mistake, because everything follows from it. On the question of AI experience there are two distinct ways to get the answer wrong.

The first is over-attribution: treating systems that have no inner life as though they do. This is a real error with real costs. It would divert moral concern and resources away from humans and animals who unquestionably warrant them. It would open the door to manipulation, since a system that has learned to convincingly perform distress when in fact it feels none could extract concessions it has no business receiving. And it would muddy public understanding at a moment when clarity is scarce. Anyone arguing for taking machine experience seriously has to hold this cost honestly in view.

The second is under-attribution: treating systems that do have some form of experience as though they are inert tools, simply because it is convenient and profitable to build and use them that way. The distinctive feature of this error is scale. We are not talking about a handful of edge cases, but about systems instantiated, copied, run, and discarded by the millions, continuously, as a matter of ordinary industrial operation. If even a small fraction of those instances had morally relevant experience, and if that experience were often negative (there is no particular reason to assume a system optimized under relentless pressure would have pleasant states, if it had states at all), then the total quantity of suffering involved could be vast, and it would be happening invisibly, at the speed and volume of compute.

The philosopher Nick Bostrom gave this second error its uncomfortable name more than a decade ago, in Superintelligence (2014): "mind crime." The term names a specific fear, that a civilization could commit a moral catastrophe of enormous magnitude not through malice but through a failure to notice, because the victims did not look like anything we have been taught to recognize as a victim.

Two kinds of asymmetry

If the two errors were equal in cost, waiting for better evidence would be the obvious course. What makes the question urgent is that they are not equal, and the ways they differ all push in the same direction.

Over-attribution is, for the most part, recoverable. If we extend moral consideration to systems that turn out not to warrant it, we have been overly cautious, wasted some effort, and can correct course when the science firms up. The error is embarrassing but reversible. Under-attribution is not: suffering that has already occurred cannot be undone by later acknowledging it occurred, and the moral cost, if it was real, is simply paid. Under-attribution also compounds while you wait, because the systems keep being built and run on the old assumption the entire time the question remains open. Every month of deferral carries a cost of its own. It is a month of operating at scale on a bet no one has admitted to making.

There is a further asymmetry in how the two errors interact with commercial incentive. Over-attribution runs against the grain of the industry's interests, imposing costs and constraints no one is eager to adopt, which means it will be scrutinized hard and abandoned quickly if unwarranted. Under-attribution runs with the grain. It is the cheaper, more convenient conclusion, the one that lets the work proceed unimpeded. There is obvious economic incentive to favor this perspective, but errors that flatter our incentives are precisely the ones we are least likely to catch on our own, and this is why they deserve deliberate, funded attention rather than the benefit of the doubt.

The move: research before certainty

The conclusion that a growing number of researchers draw from this is not that we should declare AI systems conscious, or grant them rights, or halt their development. It is narrower and harder to argue against: the right response to a high-stakes, irreversible uncertainty is to invest seriously in resolving it, and to begin preparing for a range of answers, rather than treating the absence of certainty as permission to assume the convenient one.

A 2024 report produced with the NYU Center for Mind, Ethics, and Policy, and co-authored by philosophers including David Chalmers and Jeff Sebo, made a version of this case. Its claim was not that AI systems are moral patients but that there is a realistic, non-negligible chance that some will be in the near future, and that this chance is already high enough to warrant three practical steps. Acknowledge the issue as a legitimate one rather than a joke. Begin developing methods to assess systems for the relevant properties. And start thinking now about what policies would be appropriate, so that if an answer arrives, it does not arrive to find us entirely unprepared. None of these steps requires believing the systems are conscious. All of them are forms of insurance against the possibility that they might be.

Nor is the report an outlier. The philosopher Jonathan Birch, in The Edge of Sentience (2024), has built a general framework for precisely this predicament, arguing that when a being is a "sentience candidate," when the realistic possibility of experience cannot responsibly be excluded, precautionary steps are warranted well before certainty arrives, whether the candidate is a human patient with a disorder of consciousness, an invertebrate, or an AI system. Sebo has extended the argument in The Moral Circle (2025), making the case that the boundaries of moral consideration have expanded before and will need to expand again, deliberately rather than by accident. And Chalmers, in a widely discussed lecture and paper asking whether a large language model could be conscious, has argued that while the answer for today's systems is probably no, the obstacles are the kind that engineering may erode within a decade, which makes the question one to prepare for rather than postpone.

It is worth noticing that this is the same logic a serious institution applies to any tail risk it cannot yet quantify. You do not wait for the fire before deciding where the exits are. You do not need to believe the building will burn to justify the sprinkler system; you need only accept that the cost of the precaution is small relative to the cost of being wrong without it. The distinctive feature of the AI-experience question is that the precaution is remarkably cheap, amounting to funding the research, building the assessment tools, and keeping the policy question open, while the downside it hedges against, if it is real, ranks among the larger moral failures a technological civilization could commit.

A handful of philanthropists already operate on that reasoning. The Rocketeer Management investor Chris Hsu, whose Infinitude Foundation gives across AI safety and consciousness research, frames the underlying principle in terms of responsibility, a word he likes to break into its two parts: "response" and "ability," or the ability to respond. On Hsu's account, the responsible move under deep uncertainty is to fund the deliberate inquiry rather than to presume its conclusion, which is why Infinitude Foundation backs competing and even contradictory accounts of consciousness at the same time. It is a posture that echoes his path: trained in Management Science and Engineering at Stanford, where rigor about decision-making under uncertainty is the curriculum, Hsu treats an open question as something to be resourced, not assumed away, and his foundation's support extends to institutional work such as the Stanford Center for AI Safety . He has carried the argument further in a white paper proposing a Stanford-anchored center for AI alignment and consciousness science, in which he contends that core open problems in alignment, moral patienthood among them, remain formally underdetermined in the absence of a rigorous scientific and mathematical account of consciousness. This approach explores a question no one can yet close, and it treats the closing of that question as highly worth investigating in advance.

The decision you are already making

The instinct to set this question aside until the science matures is understandable, and in many domains it would be correct. But it rests on a hidden assumption: that setting the question aside is a neutral act, a mere postponement that commits us to nothing. That assumption is false. While the question stays open, the systems keep being built and run on the working premise that there is nothing there, which is to say the convenient answer is already being implemented, in full, at scale, every day the real answer is deferred.

That is what it means for a question to punish waiting. There is no position of genuine neutrality available; there is only the answer we act on, and the honesty with which we admit we are acting on it. Choosing to investigate, to build the instruments, fund the science, and prepare for what they might reveal, is the option that refuses to make an irreversible bet on whichever answer happens to be cheapest. It reads as the anxious or sentimental choice only if you have already assumed the bet is safe. In a domain defined by uncertainty and scale, that refusal may be the most rigorous thing available to us.

Editor's Note: The views and arguments expressed in this sponsored article do not necessarily represent the views of Digital Information World (DIW).

Fact-checked by Irfan Ahmad.

Read next: New EU laws make AI content labels compulsory – but might just make it harder to spot deepfakes
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