Lit bots beware: Readers less favorable toward AI-generated creative writing, U-M research finds

When it comes to creative writing, score one for the humans over the machines. For now, anyway.

Image: Andrea Piacquadio / Pexels

New research finds that people evaluate creative writing less favorably when they learn it was generated in whole or part by artificial intelligence. And the anti-AI bias is persistent and difficult to reduce, even when steps were taken to lessen the aversion within the experiments.

That strength and consistency of the negative attitude toward AI-generated or assisted writing jumped out at researchers, and they say it poses implications for integrating AI in creative fields. As it stands, the study finds people tend to view the creative works of machines as “relatively inauthentic and therefore less worthy of their appreciation.”

The researchers say previous research has offered preliminary evidence that AI disclosure can have negative effects on how people evaluate creative content, but their study builds on it by revealing a “surprising level of robustness” across 16 experiments involving 27,000 participants conducted between March 2023 and June 2024.

“What surprised us most was how incredibly ‘sticky’ this penalty is,” said Justin Berg, the study’s co-author and an associate professor of management and organizations at University of Michigan’s Ross School of Business.

“We threw everything at it, from changing the story’s perspective to humanizing the AI or framing it as a collaboration, and nothing reliably reduced the bias. Across all the experiments, the pattern was clear: If readers believe AI is involved, they view the work as less authentic and enjoy it less, even when the content is identical.”

Throughout the study, the researchers asked participants to read and evaluate AI-generated writing samples created using ChatGPT—chosen because it was the most well-known large language model at the time of the initial study. Across all the experiments, AI disclosure decreased evaluations by an average of 6.2%.

Berg and his colleagues, Manav Raj of the University of Pennsylvania’s Wharton School of Business and Rob Seamans of New York University’s Stern School of Business, note the results reflect attitudes during a period of rapid advancements in AI capabilities and shifting perceptions of its role in creative work. It’s an open question—and fertile ground for further study—whether the AI disclosure penalty will persist, diminish or reverse as such content becomes more pervasive.

What does appear clear—at least for now—is the use of AI in creative writing triggers different psychological responses than when the technology is employed in other domains. Understanding that bias is crucial for helping navigate the challenges for those working toward fuller, broader human-AI collaboration.

The findings, published in the Journal of Experimental Psychology, also pose practical implications for creative producers using AI, as the U.S. Congress considers AI disclosure legislation. Mandated disclosure of AI involvement in creative work could usher in negative biases toward such content and potentially affect its reception.

Contact: Jeff Karoub.

Editor’s Notes:
1. This article was originally published on Michigan News, and republished here with permission. A representative of the University of Michigan news team confirmed that AI tools were not used in its production. 
2. The study notes:"We have studied the effect of AI disclosure on evaluations in one specific domain (creative writing)"… "We are also careful to note that our study does not address whether and in what circumstances output created by an AI tool may be more or less creative than output created by a human"… and "it is important to note that the AI disclosure effects we document may evolve over time".

Read next: The Dangers of Not Teaching Students How to Use AI Responsibly

by External Contributor via Digital Information World

AI is failing ‘Humanity’s Last Exam’. So what does that mean for machine intelligence?

Image: Egor Komarov/Unsplash

Kai Riemer, University of Sydney and Sandra Peter, University of Sydney

How do you translate ancient Palmyrene script from a Roman tombstone? How many paired tendons are supported by a specific sesamoid bone in a hummingbird? Can you identify closed syllables in Biblical Hebrew based on the latest scholarship on Tiberian pronunciation traditions?

These are some of the questions in “Humanity’s Last Exam”, a new benchmark introduced in a study published this week in Nature. The collection of 2,500 questions is specifically designed to probe the outer limits of what today’s artificial intelligence (AI) systems cannot do.

The benchmark represents a global collaboration of nearly 1,000 international experts across a range of academic fields. These academics and researchers contributed questions at the frontier of human knowledge. The problems required graduate-level expertise in mathematics, physics, chemistry, biology, computer science and the humanities. Importantly, every question was tested against leading AI models before inclusion. If an AI could not answer it correctly at the time the test was designed, the question was rejected.

This process explains why the initial results looked so different from other benchmarks. While AI chatbots score above 90% on popular tests, when Humanity’s Last Exam was first released in early 2025, leading models struggled badly. GPT-4o managed just 2.7% accuracy. Claude 3.5 Sonnet scored 4.1%. Even OpenAI’s most powerful model, o1, achieved only 8%.

The low scores were the point. The benchmark was constructed to measure what remained beyond AI’s grasp. And while some commentators have suggested that benchmarks like Humanity’s Last Exam chart a path toward artificial general intelligence, or even superintelligence – that is, AI systems capable of performing any task at human or superhuman levels – we believe this is wrong for three reasons.

Benchmarks measure task performance, not intelligence

When a student scores well on the bar exam, we can reasonably predict they’ll make a competent lawyer. That’s because the test was designed to assess whether humans have acquired the knowledge and reasoning skills needed for legal practice – and for humans, that works. The understanding required to pass genuinely transfers to the job.

But AI systems are not humans preparing for careers.

When a large language model scores well on the bar exam, it tells us the model can produce correct-looking answers to legal questions. It doesn’t tell us the model understands law, can counsel a nervous client, or exercise professional judgment in ambiguous situations.

The test measures something real for humans; for AI it measures only performance on the test itself.

Using human ability tests to benchmark AI is common practice, but it’s fundamentally misleading. Assuming a high test score means the machine has become more human-like is a category error, much like concluding that a calculator “understands” mathematics because it can solve equations faster than any person.

Human and machine intelligence are fundamentally different

Humans learn continuously from experience. We have intentions, needs and goals. We live lives, inhabit bodies and experience the world directly. Our intelligence evolved to serve our survival as organisms and our success as social creatures.

But AI systems are very different.

Large language models derive their capabilities from patterns in text during training. But they don’t really learn.

For humans, intelligence comes first and language serves as a tool for communication – intelligence is prelinguistic. But for large language models, language is the intelligence – there’s nothing underneath.

Even the creators of Humanity’s Last Exam acknowledge this limitation:

High accuracy on [Humanity’s Last Exam] would demonstrate expert-level performance on closed-ended, verifiable questions and cutting-edge scientific knowledge, but it would not alone suggest autonomous research capabilities or artificial general intelligence.

Subbarao Kambhampati, professor at Arizona State University and former president of the Association for the Advancement of Artificial Intelligence, puts it more clearly:

Humanity’s essence isn’t captured by a static test but rather by our ability to evolve and tackle previously unimaginable questions.

Developers like leaderboards

There’s another problem. AI developers use benchmarks to optimise their models for leaderboard performance. They’re essentially cramming for the exam. And unlike humans, for whom the learning for the test builds understanding, AI optimisation just means getting better at the specific test.

But it’s working.

Since Humanity’s Last Exam was published online in early 2025, scores have climbed dramatically. Gemini 3 Pro Preview now tops the leaderboard at 38.3% accuracy, followed by GPT-5 at 25.3% and Grok 4 at 24.5%.

Does this improvement mean these models are approaching human intelligence? No. It means they’ve gotten better at the kinds of questions the exam contains. The benchmark has become a target to optimise against.

The industry is recognising this problem.

OpenAI recently introduced a measure called GDPval specifically designed to assess real-world usefulness.

Unlike academic-style benchmarks, GDPval focuses on tasks based on actual work products such as project documents, data analyses and deliverables that exist in professional settings.

What this means for you

If you’re using AI tools in your work or considering adopting them, don’t be swayed by benchmark scores. A model that aces Humanity’s Last Exam might still struggle with the specific tasks you need done.

It’s also worth noting the exam’s questions are heavily skewed toward certain domains. Mathematics alone accounts for 41% of the benchmark, with physics, biology and computer science making up much of the rest. If your work involves writing, communication, project management or customer service, the exam tells you almost nothing about which model might serve you best.

A practical approach is to devise your own tests based on what you actually need AI to do, then evaluate newer models against criteria that matter to you. AI systems are genuinely useful – but any discussion about superintelligence remains science fiction and a distraction from the real work of making these tools relevant to people’s lives.

Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

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

Read next: Should companies replace human workers with robots? New study takes a closer look

by External Contributor via Digital Information World

Should companies replace human workers with robots? New study takes a closer look

Written By Anthony Borrelli. Edited by Ayaz Khan.

Binghamton University School of Management researchers show how companies create more value through human-robot collaboration.

Image: Simon Kadula / Unsplash

Last year, when The New York Times reported that Amazon’s robotics team’s ultimate goal was to automate 75% of the company’s operations, replacing more than half a million human jobs in an attempt to pass cost savings onto customers, it was a stark reminder of robots’ ever-expanding role in reshaping the American workplace.

Meanwhile, at Hyundai’s auto plant in Georgia, more than 1,000 robots work alongside almost 1,500 human employees.

But as new research involving the Binghamton University School of Management (SOM) found, companies could risk losing their competitive edge by leaning too heavily on replacing human workers with robots, since competitors could easily follow suit. Instead, researchers determined businesses could generate more value by focusing on human-robot collaboration, amplifying their existing human capital into hard-to-imitate resources.

“Simply put, deploying robots in a collaborative manner with humans can alter social dynamics in ways that encourage unit members to feel, act and think together,” the study, published in the Journal of Organizational Behavior, stated. “By leveraging these resources through the deployment of robots in collaborative settings, organizations can not only generate additional economic value from their human capital but also improve their ability to capture a greater share of that value in the competitive market.”

Chou-Yu (Joey) Tsai, SOM Osterhout associate professor of entrepreneurship and the study’s co-author, said researchers initially wanted to explore how an organization’s human-robot interface could affect leadership, but then realized it could be more beneficial to focus on its impact on the organization as a whole.

The study examined the issue from two viewpoints: a substitute view and a complementary view. Both can enhance an organization’s desired outcomes in efficiency and productivity, researchers determined, but those who adopt a complementary view of human-robot collaboration were more likely to foster a greater and more positive sense of commitment among human employees.

“The most successful organizations will find a way to extract the best value from these technologies to achieve their unique goals,” Tsai said. “If you’re focused on going up against other companies by introducing robots to replace some key roles traditionally carried out by human employees, that’s not always the best strategic thinking because your competitors could easily do the same thing.”

Additionally, the study noted that on-the-job learning also remains fundamental for understanding the best ways to implement such changes.

Delegating robots to tasks that potentially offer meaning, autonomy or opportunities for mastery could undermine not only employees’ mental health, researchers said, but also the very efficiency gains employers are striving for.

“Discussion of AI and robots often centers on adoption speed, workplace disruption and job displacement,” said SOM Associate Dean for Faculty Research Rory Eckardt, another co-author on the study. “Our paper shifts attention to complementary integration by considering when these technologies strengthen teamwork and coordination, improve the work environment, and support value creation and competitive advantage.”

One effective example the researchers cited involved members of a company’s research and development team working with robot systems to better analyze complex datasets. Doing so amplifies the team’s effectiveness in achieving results and helps them work together more efficiently, according to the study.

Another example involved hospital staff using surgical robots to achieve higher-definition 3-D visualization, surpassing the limitations of the human hand to perform increasingly delicate medical procedures.

Using this collaborative approach can increase employee loyalty, according to the study, because it shows the company is providing additional support for the work being done.

“When I began my research career in leadership and organization science, I could have never predicted that technology would advance to the point where we’re researching the impact of robots on leadership development and organization effectiveness,” said SOM Dean Shelley Dionne, who co-authored the study. “But now it informs how we think about the future of workforce development and employee performance, no matter what type of organization we consider.”

The study, “Human Capital Robotic Integration and Value Creation for Organizations,” was also co-authored by Jason Marshall from Creighton University in Nebraska, Malte Jung from Cornell University, YoYo Tsung-Yu Hou from National Chengchi University in Taiwan and Biying Yang from South Dakota State University.

Originally published by Binghamton University / BingUNews (State University of New York) and republished on DIW with permission.

Read next: Which Roles Use AI More Frequently in U.S. Workplaces? Leaders Report Higher Frequency, Gallup Survey Shows
by External Contributor via Digital Information World

Which Roles Use AI More Frequently in U.S. Workplaces? Leaders Report Higher Frequency, Gallup Survey Shows

by Andy Kemp. Edited by Asim BN.

U.S. employees already using artificial intelligence (AI) in the workplace used it slightly more often in the fourth quarter of 2025 than in the prior quarter, continuing a gradual increase since 2023. The proportion of employees using AI daily has risen from 10% to 12%. Frequent use, defined as using AI at work at least a few times a week, has also inched up three percentage points to 26%.

These increases are on par with the expansion of frequent workplace AI use reported throughout 2025. Meanwhile, the percentage of total users, those who use AI at work at least a few times a year, was flat in Q4 after sharp increases earlier in the trend. Nearly half of U.S. workers (49%) report that they “never” use AI in their role.

Organizational AI adoption has not changed meaningfully from the previous quarter. In Q4, 38% of employees said their organization has integrated AI technology to improve productivity, efficiency and quality. Forty-one percent said their organization has not implemented AI tools, and 21% said they don’t know. These results closely mirror Q3 figures.

AI Use Varies by Industry and Role Type

AI use in the workplace is most prevalent in knowledge-based industries and least common in production and service-based sectors. Employees in technology, finance and higher education report the highest levels of AI use, especially compared with U.S. employees in retail, manufacturing and healthcare.

Line charts show trends in workplace AI use by industry among U.S. employees, from 2023 to 2025. Across all industries, total AI use increases over time, with notable variation in adoption levels. Technology shows the highest use, with total AI use at 77%, including 57% frequent users and 31% daily users. College or university and finance also report high adoption, with total AI use at 63% and 64%, respectively. Professional services reaches 62% total AI use, including 36% frequent and 16% daily users. K-12 education shows rising use to 56% total AI use. Community or social services, government or public policy, healthcare and manufacturing show more moderate adoption, with total AI use ranging from about 41% to 43%. Retail reports the lowest adoption, with total AI use at 33%, including 19% frequent users and 10% daily users.

Gains in AI use were uneven across industries in Q4. The total AI user base increased most in finance and professional services, moving up six and five points, respectively. These increases widened existing gaps between higher-growth industries and those with lower AI use. In retail, total AI use did not increase in Q4 from Q3, while manufacturing saw a three-point increase.

In industries such as technology where AI use has been most prevalent, growth in total users shows signs of leveling, with gains found primarily among those already using AI. Total AI use in technology increased by just one percentage point in Q4, from 76% to 77%, while frequent use rose from 50% to 57%.

Across industries, AI use is concentrated in roles that employees describe as remote-capable, meaning the job could reasonably be completed remotely regardless of where the employee actually works. These roles are typically desk- and office-based positions.

Since Q2 2023, total AI use among employees in remote-capable roles has increased from 28% to 66%, while frequent use has risen from 13% to 40%. Growth has been slower in roles that are not remote-capable: AI use in these positions has increased from 15% to 32%, with frequent use rising from 8% to 17%.

Line charts compare AI use among U.S. employees in remote-capable and non-remote-capable roles from 2023 to 2025. Employees in remote-capable roles show substantially higher AI adoption throughout the period. By 2025, total AI use among remote-capable employees reached 66%, including 40% who use AI frequently and 19% who use it daily. Employees in non-remote-capable roles reported much lower use. In the most recent data, total AI use among these employees is 32%, with 17% using AI frequently and 7% using it daily.

Leaders Continue to Use AI More Than Other Employees

Employees in leadership positions are more likely than managers and individual contributors to use AI at work. In Q4, 69% of leaders said they use AI at least a few times a year, compared with 55% of managers and 40% of individual contributors. Part of this difference likely reflects role type, as leaders are more likely to hold office-based and remote-capable roles where AI tools are more easily applied.

Line graph. Percentages of Americans who think the coronavirus situation in the U.S. is getting a lot or a little better, staying the same, or getting a lot or a little worse, from April 2020 to February 2022. In the latest poll, 63% of U.S. adults said it is getting better, up from 20% in January. Twelve percent said it is getting worse, down from 58% in January and 25% said it is staying the same, relatively unchanged.

Leaders also report more frequent AI use than other employees, a gap that has widened over time. Since Q2 2023, frequent AI use among leaders has risen from 17% to 44%. Over the same period, frequent use among managers has doubled from 15% to 30%, while frequent use among individual contributors has increased from 9% to 23%. Frequent use has risen among all three types of workers since Q3, contributing to the overall climb in Q4.

Implications

Modest gains in frequent AI use were seen in Q4 2025, on par with the growth seen in Q3, but the percentage of employees who say they use AI overall remained flat. Use remains most prevalent in knowledge-based industries and remote-capable roles. These differences in AI adoption may help to explain why overall adoption appears to be slowing, even as AI use continues to deepen within certain segments of the workforce.

Leaders, in particular, report substantially higher and more frequent AI use than other employees, and that separation has grown over time. Gallup research shows that lack of utility is the most common barrier to individual AI use, suggesting that clear AI use cases may be more apparent for leaders than employees in other roles. For organizations integrating AI technology, this underscores the importance of grounding decisions about AI adoption in a clear understanding of how AI may be applied to different roles and functions, not just among those closest to decision-making.

Gallup’s newest indicator tracks workplace AI adoption over time, including usage frequency, employee comfort, manager support, organizational integration and strategic communication. Explore all of their indicators for global data on what matters most in the workplace and to societies at large.

Survey Methods

These results for the quarterly Gallup workforce studies are based on self-administered web surveys conducted with a random sample of adults working full time and part time for organizations in the United States, aged 18 and older, who are members of the Gallup Panel™. Gallup uses probability-based, random sampling methods to recruit its Panel members. Gallup weighted the obtained samples to correct for nonresponse. Nonresponse adjustments were made by adjusting the sample to match the national demographics of gender, age, race, Hispanic ethnicity, education and region. Demographic weighting targets were based on the most recent Current Population Survey figures for the aged 18 and older U.S. population.

For results based on the sample of employed U.S. adults, the margin of sampling error at the 95% confidence level varies for different topics and time frames. Details for the recent quarterly surveys are noted below. In addition to sampling error, question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of public opinion polls.

Survey Method Details

Survey dates, sample size (among employed U.S. adults), margin of error and design effect by quarter for each study.

Survey Dates	Sample Size	Margin of Error (95% confidence level)	Design Effect
Q4 2025, Oct. 30-Nov. 13, 2025	22,368	±1.0 percentage points	2.26
Q3 2025, Aug. 5-19, 2025	23,068	±1.0 percentage points	2.46
Q2 2025, May 7-16, 2025	19,043	±1.1 percentage points	2.29
Q2 2024, May 11-25, 2024	21,543	±1.0 percentage points	2.25
Q2 2023, May 11-25, 2023	18,871	±1.1 percentage points	2.25

This post was originally published on Gallup and is republished here with permission.

Read next: 

• These Are the Best and Worst U.S. Metro Areas for Science, Technology, Engineering, and Mathematics Professionals in 2026

• Twelve Countries Say No to Banning Autonomous Weapons

by External Contributor via Digital Information World

Twelve Countries Say No to Banning Autonomous Weapons

by Anna Fleck, Data Journalist - Edited by Asim BN.

The United States and United Kingdom are among 12 countries opposing a global ban on autonomous weapons, joined by Australia, Belarus, Estonia, India, Israel, Japan, North Korea, Poland, Russia and South Korea. Data compiled by Automated Decision Research, the monitoring and research team of Stop Killer Robots, finds that another 53 nations have yet to take a clear stance, while 127 countries, including most of Africa and Latin America, support the ban. These positions have been listed following discussions at UN General Assembly and Certain Conventional Weapons meetings.

At present, there is no single law or legally-binding treaty that bans the use of lethal autonomous weapons (LAWS), which have been used in conflict zones like Ukraine and Libya. The International Committee of the Red Cross (ICRC) is calling for new international rules, citing humanitarian, legal and ethical concerns over the loss of human control in warfare. LAWS pose risks to both civilians and combatants and could escalate conflicts.

Though LAWS may use AI, it is not a requirement. However, the broader debate around military AI also remains obscure. Over the past few years, several initiatives have emerged to address military AI, but none are yet legally binding. In 2024, the UN GA resolution A/79/408 saw 166 countries supporting restrictions on LAWS, while Belarus, Korea, and Russia opposed, and 15, including Ukraine, abstained. Meanwhile, two landmark intergovernmental frameworks worth mentioning include The Political Declaration on Responsible Military Use of AI and Autonomy, an initiative launched by the U.S. and supported by over 60 nations, as well as the Responsible AI in the Military Domain (REAIM) Call for Action, endorsed by more than 50 countries. Both focus on ethical guidelines but are non-binding.

The UN Office for Disarmament Affairs has condemned LAWS as "politically unacceptable and morally repugnant," and UN Secretary-General António Guterres has called for their prohibition under international law.

This article was originally published on Statista ‘Chart of the Day’ and is made available under the Creative Commons License CC BY‑ND 3.0.

Read next: Foreign Accents Receive Higher Hypothetical Investment in Business Experiment Only With Strong Reputation, URI Study Finds
by External Contributor via Digital Information World

Everyone Is Sick of Customer Service Bots. Here is How to Bypass Them

Edited by Asim BN.

Are you one of the many people yelling "Representative!" on your phone lately? If so, you are not alone. There is a new wave happening in the digital world of consumers: patience is wearing thin for automated customer support.

New data shows that we are approaching a peak in acceptance of cost-cutting automation. According to Google Trends, interest in searching for "live person customer service" is at an all-time high, as is interest in searches such as "talk to a real person" and "human customer support." What is clear is that the digital consumer is voting, and the vote is decidedly against the digital strategies corporations are adopting.

At the same time that businesses are racing to deploy generative AI to lower costs, consumers are actively seeking an exit from the automated experience.

The LiveOps 2025 Holiday AI and Customer Service report points out a widening gap between the strategy of businesses and the feelings of users: Only 17% of consumers wish to see an increase in AI use by companies over the next year, while almost a third (32%) of consumers wish to see a decrease.

The Efficiency Paradox

This creates a paradox for digital entrepreneurs and business owners. On one hand, automation is a requirement for scale. On the other hand, relying too heavily on Interactive Voice Response (IVR) technology has created a "customer trap."

Jason Long, founder of SupportMy.website says that even though IVR technology was developed to be efficient, it acts as a barrier to access for many consumers. However, Long adds that these digital walls are not insurmountable. Most enterprise-level IVR systems include "exit strategies," which are logical pathways that allow high-value or high-risk calls to bypass the IVR and connect the caller directly to a human decision maker.

Understanding how IVR technology works will enable consumers to bypass the bot and reach a human decision-maker.

Bypassing AI Technology with Three Different Methods

There are three methods consumers can use to bypass IVR and reach a human decision-maker. Each method is a direct result of the financial and accessibility logic embedded in the IVR system's software.

Method #1 – Triggering the Churn Risk Protocol

IVR support systems are tiered. Calls that ask for "help" will typically be directed to the lowest cost tier, which is the automated bot. However, IVR systems are designed to protect the company's revenue at all times.

To determine a call's priority, IVR systems consider the potential for revenue loss.

Therefore, if a consumer asks for a cancellation or retention instead of saying "support", they can potentially trigger a "retention protocol" that will bypass the general support queue and send them to a Retention Specialist, which is typically a human agent who is responsible for resolving issues quickly to prevent the company from losing a customer.

Method #2 – The Sales Trojan Horse

Most companies have segregated their inbound calls into two categories: cost centers (Support) and revenue generators (Sales). While cost centers are automated to reduce costs, revenue-generating lines are always manned by humans to generate revenue.

According to Long, the "Sales Trojan Horse" is a method that bypasses the support line completely and dials the line for "new customers" or "sales". These lines are usually answered immediately by a human. Once connected, the caller can tell the sales representative that they are having difficulty reaching the support team. Since sales representatives can internally transfer the call to the support representative's extension, the consumer can bypass the automated IVR system and go to the head of the line.

Method #3 – Using the Accessibility Defaults (the Mumble Method)

To comply with accessibility regulations, modern IVR systems must assist consumers with disabilities, those with poor accents, or those with poor internet connectivity.

If a consumer remains silent or mumbles when responding to the bot, the IVR system recognizes a "recognition error." Once a predetermined number of recognition errors (typically three) occurs, the IVR system will automatically switch to a human operator to prevent discrimination against a disabled consumer or a consumer with a legitimate connection issue.

The Bottom Line For Businesses

These are survival techniques for consumers; however, they are a red flag for business owners. As Long notes, "Just as fast as a bot can find what you are looking for, sometimes what you really need is a human - knowing how to get to a human who can make real decisions regarding your account is critical."

If your customers have to resort to cheat codes to talk to your employees, it may be time to review your automated customer service strategy.

Author bio

Jason Long is the founder and CEO of SupportMy.Website. He is a serial problem solver and entrepreneur with 25 years of experience in business building. Jason’s ventures range from agriculture to healthcare with a focus on web-based technology. He has extensive experience in software development and has operated as a developer, UX designer, graphic designer, project manager, director, executive coach, and CEO.‍ Jason is also an experienced world traveler who regularly visits destinations worldwide and is passionate about community growth, social issues, fitness, and family. ‍

Read next: These Are the Best and Worst U.S. Metro Areas for Science, Technology, Engineering, and Mathematics Professionals in 2026
by Guest Contributor via Digital Information World

These Are the Best and Worst U.S. Metro Areas for Science, Technology, Engineering, and Mathematics Professionals in 2026

A 2026 study by personal finance website WalletHub ranks the best and worst U.S. metropolitan areas for science, technology, engineering, and mathematics (STEM) professionals.

The analysis, published Jan. 21, 2026, compared the 100 most populous U.S. metropolitan statistical areas using 21 metrics grouped into three categories: Professional Opportunities, STEM-Friendliness, and Quality of Life. Data were drawn from publicly available sources including the U.S. Census Bureau, Bureau of Labor Statistics, National Science Foundation, U.S. Patent and Trademark Office, and other national and private datasets, with figures collected as of Dec. 19, 2025.

According to the study, Boston ranked first overall, followed by Atlanta, Seattle, Pittsburgh, and Austin. 

At the lower end of the rankings, Cape Coral, Florida; Jackson, Mississippi; North Port, Florida; Memphis, Tennessee; and Little Rock, Arkansas ranked among the least favorable metro areas for STEM professionals based on the same criteria.

The study also highlights variation across metros in STEM employment concentration and growth. San Jose, California had the highest share of workers employed in STEM fields, while Providence, Rhode Island recorded the highest recent STEM employment growth. Conversely, several metros ranked low due to smaller STEM workforces or slower growth.

WalletHub explained that where city-level data were missing, state-level information was used to represent metro areas in the rankings.

Overall Rank	Metro Area*	Total Score	Professional Opportunities Rank	STEM-Friendliness Rank	Quality of Life Rank
1	Boston, MA	69.43	3	1	67
2	Atlanta, GA	66.70	7	12	9
3	Seattle, WA	65.42	4	7	35
4	Pittsburgh, PA	65.07	24	13	5
5	Austin, TX	64.78	6	20	10
6	San Francisco, CA	64.26	5	3	61
7	Cincinnati, OH	62.05	15	33	6
8	Salt Lake City, UT	60.77	2	37	23
9	Minneapolis, MN	59.69	22	24	14
10	Orlando, FL	59.61	19	31	11
11	Worcester, MA	58.73	40	5	51
12	Sacramento, CA	58.59	55	10	25
13	San Jose, CA	58.19	10	6	79
14	Washington, DC	58.13	1	35	52
15	Portland, OR	57.48	34	43	7
16	Madison, WI	57.18	29	26	29
17	Hartford, CT	57.14	8	27	16
18	Tampa, FL	56.95	25	32	26
19	San Diego, CA	56.76	35	9	56
20	Chicago, IL	56.73	62	17	17
21	St. Louis, MO	56.35	17	45	24
22	Raleigh, NC	56.16	13	14	62
23	Denver, CO	55.88	9	23	57
24	Columbus, OH	55.53	54	15	30
25	Springfield, MA	54.94	98	2	4
26	Albany, NY	54.32	11	40	13
27	Boise, ID	53.58	23	76	12
28	Los Angeles, CA	52.93	76	4	70
29	Houston, TX	52.54	51	22	42
30	Providence, RI	51.22	42	29	40
31	Baltimore, MD	50.85	16	11	87
32	Dallas, TX	50.71	21	25	71
33	Cleveland, OH	50.68	45	30	53
34	Albuquerque, NM	50.60	39	74	20
35	Spokane, WA	50.41	64	41	28
36	Rochester, NY	50.29	49	36	36
37	New York, NY	49.99	48	8	85
38	Harrisburg, PA	49.91	12	57	15
39	Dayton, OH	49.87	20	66	1
40	Nashville, TN	49.31	30	19	80
41	Greenville, SC	48.68	18	54	18
42	Richmond, VA	48.65	14	56	60
43	Des Moines, IA	48.58	27	87	22
44	Tucson, AZ	48.54	68	59	27
45	Buffalo, NY	48.47	56	39	55
46	Columbia, SC	47.65	32	55	46
47	Omaha, NE	47.49	58	85	21
48	Knoxville, TN	47.11	61	50	33
49	Philadelphia, PA	47.10	65	21	72
50	Charleston, SC	46.31	26	91	34
51	New Haven, CT	46.16	79	18	68
52	Phoenix, AZ	45.72	59	72	39
53	San Antonio, TX	45.54	53	48	63
54	Syracuse, NY	45.50	47	62	3
55	Colorado Springs, CO	45.04	33	90	49
56	Milwaukee, WI	44.72	66	61	54
57	Grand Rapids, MI	44.50	46	75	45
58	El Paso, TX	44.50	71	67	41
59	Kansas City, MO	44.20	38	95	44
60	Allentown, PA	43.76	82	28	48
61	Charlotte, NC	43.64	28	69	74
62	Oklahoma City, OK	43.56	60	93	38
63	Virginia Beach, VA	42.93	52	71	66
64	Honolulu, HI	42.58	74	94	31
65	Jacksonville, FL	41.87	57	52	78
66	Miami, FL	41.71	41	53	82
67	Indianapolis, IN	41.53	37	46	90
68	Akron, OH	41.26	69	64	65
69	Bakersfield, CA	41.05	90	44	69
70	Ogden, UT	40.89	44	73	50
71	Provo, UT	40.85	50	70	43
72	Augusta, GA	40.54	36	83	76
73	Tulsa, OK	40.48	77	98	32
74	Las Vegas, NV	40.34	72	96	47
75	Riverside, CA	39.88	99	16	88
76	Birmingham, AL	39.69	43	92	73
77	Wichita, KS	38.36	85	51	81
78	Youngstown, OH	38.11	97	60	2
79	Palm Bay, FL	38.02	31	99	37
80	Toledo, OH	37.90	86	63	77
81	Louisville, KY	37.73	84	88	64
82	Scranton, PA	37.73	89	68	8
83	Detroit, MI	37.66	63	78	84
84	Baton Rouge, LA	37.29	67	81	83
85	Chattanooga, TN	37.21	70	80	75
86	New Orleans, LA	36.67	95	89	59
87	Bridgeport, CT	36.49	75	47	93
88	Lakeland, FL	36.16	87	58	58
89	Fresno, CA	35.74	93	42	92
90	Stockton, CA	35.51	100	38	89
91	Oxnard, CA	35.08	88	34	99
92	Greensboro, NC	34.91	80	65	91
93	Winston-Salem, NC	34.90	81	77	86
94	McAllen, TX	34.45	94	79	19
95	Deltona, FL	34.08	92	49	94
96	Little Rock, AR	29.43	73	97	95
97	Memphis, TN	29.27	83	84	97
98	North Port, FL	28.01	91	82	100
99	Jackson, MS	27.55	78	100	96
100	Cape Coral, FL	26.49	96	86	98

* Metro Area refers to a Metropolitan Statistical Area (MSA). Except for “Total Score,” all columns show relative ranks, where 1 indicates the best conditions in that category.

The study presents a comparative snapshot of STEM job markets. To provide additional context, WalletHub analyst Chip Lupo responded to follow-up questions from DIW.

Q: What's the #1 mistake STEM professionals make when evaluating metros - and what critical factors do they typically overlook?

The biggest mistakes STEM professionals make when evaluating metro areas are zeroing in on a specific salary threshold and chasing a city’s tech reputation. High pay doesn’t always translate into better outcomes if housing is expensive, wage growth is slow, or job options are limited. 

People also tend to overlook what really drives long-term opportunity, like how many STEM jobs are available, how fast the sector is growing, and whether wages keep up with the cost of living. Just as important are factors such as R&D investment, strong engineering schools, and an active innovation ecosystem. 

With many STEM jobs allowing remote work, the smartest move often isn’t choosing the flashiest tech hub, but finding a place that balances opportunity, affordability, and quality of life.

Q: Your expert commentary mentioned AI weakening entry-level STEM hiring. Which specific metros or STEM fields are most/least affected by this shift, and how should recent grads adjust their strategies?

STEM jobs are still in high demand and pay well, but AI is changing the entry-level landscape by automating routine tasks, making some junior roles less plentiful. Graduates should focus on areas with high job growth and plenty of STEM openings, like Boston, Seattle, and Atlanta, while upskilling to take on tasks that are harder to automate. Smaller markets like North Port, FL and McAllen, TX offer fewer opportunities, so location and adaptability are key for early-career success.

Q: Federal defunding of basic science research was flagged as a major concern. Beyond research roles, how might this impact STEM professionals in digital tech, software engineering, and other private sector fields? Should they factor this into their metro decisions?

Federal research funding fuels innovation that private-sector STEM jobs rely on. Cuts or shifts in R&D could slow new tech development, limit collaboration with schools and labs, and affect high-tech job growth. Metros with strong R&D funding and innovation tend to have more STEM jobs, higher pay, and better long-term growth opportunities. Even if a job can be done remotely, being near these innovation hubs can make a real difference in career prospects. That said, it’s difficult to predict which areas will get the most funding years into the future. 

Q: For Gen Z STEM grads entering the market in 2026-2027, would you recommend a different top 5 metros than for experienced professionals? What changes based on career stage?

Gen Z STEM grads entering the job market should weigh entry-level opportunities more heavily than experienced professionals. While big cities such as Boston, Seattle, Austin, Atlanta, and Pittsburgh rank highly overall, new grads may benefit from areas with more job openings per capita and high starting pay. For example, Greenville, SC has the most per-capita STEM job openings (18.4 times more than North Port, FL), and San Jose, CA offers the highest average monthly earnings for new STEM employees (nearly four times higher than Lakeland, FL). Atlanta also ranks in the top 10 for median STEM earnings adjusted for the cost of living, at more than $110,000.

Career stage matters because early-career STEM workers prioritize getting their foot in the door, along with a strong starting salary and growth potential, whereas experienced professionals can weigh factors like STEM density, R&D intensity, quality-of-life rankings, and executive pay. For new grads, focusing on metro areas that maximize early opportunity can help launch a career even if overall rankings differ slightly.

Q: Looking at your 2026 data trends, which metros show the strongest momentum for growth - and what skills should STEM professionals prioritize to position themselves in these emerging markets?

Boston, Atlanta, Seattle, Pittsburgh, and Austin show the strongest momentum for STEM growth. They combine abundant job openings, high wages, strong STEM education, and active innovation ecosystems. 

With STEM roles making up a large share of employment and salaries well above the national median, targeting these metro areas can boost career growth and earning potential while also offering quality-of-life advantages

Note: AI tools assisted with drafting and polishing this post. All content was human-reviewed and approved.

Read next:

• Reversible Words Affect Persuasiveness: Boost Confidence in Agreeing Readers, Reduce It in Disagreeing Ones, Study Finds

by Asim BN via Digital Information World

ChatGPT Health promises to personalise health information. It comes with many risks

Julie Ayre, University of Sydney; Adam Dunn, University of Sydney, and Kirsten McCaffery, University of Sydney

Image: Berke Citak / Unsplash

Many of us already use generative artificial intelligence (AI) tools such as ChatGPT for health advice. They give quick, confident and personalised answers, and the experience can feel more private than speaking to a human.

Now, several AI companies have unveiled dedicated “health and wellness” tools. The most prominent is ChatGPT Health, launched by OpenAI earlier this month.

ChatGPT Health promises to generate more personalised answers, by allowing users to link medical records and wellness apps, upload diagnostic imaging and interpret test results.

But how does it really work? And is it safe?

Most of what we know about this new tool comes from the company that launched it, and questions remain about how ChatGPT Health would work in Australia. Currently, users in Australia can sign up for a waitlist to request access.

Let’s take a look.

AI health advice is booming

Data from 2024 shows 46% of Australians had recently used an AI tool.

Health queries are popular. According to OpenAI, one in four regular ChatGPT users worldwide submit a health-related prompt each week.

Our 2024 study estimated almost one in ten Australians had asked ChatGPT a health query in the previous six months.

This was more common for groups that face challenges finding accessible health information, including:

• people born in a non-English speaking country
• those who spoke another language at home
• people with limited health literacy.

Among those who hadn’t recently used ChatGPT for health, 39% were considering using it soon.

How accurate is the advice?

Independent research consistently shows generative AI tools do sometimes give unsafe health advice, even when they have access to a medical record.

There are several high-profile examples of AI tools giving unsafe health advice, including when ChatGPT allegedly encouraged suicidal thoughts.

Recently, Google removed several AI Overviews on health topics – summaries which appear at the top of search results – after a Guardian investigation found the advice about blood tests results was dangerous and misleading.

This was just one health prompt they studied. There could be much more advice the AI is getting wrong we don’t know about yet.

So, what’s new about ChatGPT Health?

The AI tool has several new features aimed to personalise its answers.

According to OpenAI, users will be able to connect their ChatGPT Health account with medical records and smartphone apps such as MyFitnessPal. This would allow the tool to use personal data about diagnoses, blood tests, and monitoring, as well as relevant context from the user’s general ChatGPT conversations.

OpenAI emphasises information doesn’t flow the other way: conversations in ChatGPT Health are kept separate from general ChatGPT, with stronger security and privacy. The company also says ChatGPT Health data won’t be used to train foundation models.

OpenAI says it has worked with more than 260 clinicians in 60 countries (including Australia), to give feedback on and improve the quality of ChatGPT Health outputs.

In theory, all of this means ChatGPT Health could give more personalised answers compared to general ChatGPT, with greater privacy.

But are there still risks?

Yes. OpenAI openly states ChatGPT Health is not designed to replace medical care and is not intended for diagnosis or treatment.

It can still make mistakes. Even if ChatGPT Health has access to your health data, there is very little information about how accurate and safe the tool is, and how well it has summarised the sources it has used.

The tool has not been independently tested. It’s also unclear whether ChatGPT Health would be considered a medical device and regulated as one in Australia.

The tool’s responses may not reflect Australian clinical guidelines, our health systems and services, and may not meet the needs of our priority populations. These include First Nations people, those from culturally and linguistically diverse backgrounds, people with disability and chronic conditions, and older adults.

We don’t know yet if ChatGPT Health will meet data privacy and security standards we typically expect for medical records in Australia.

Currently, many Australians’ medical records are incomplete due to patchy uptake of MyHealthRecord, meaning even if you upload your medical record, the AI may not have the full picture of your medical history.

For now, OpenAI says medical record and some app integrations are only available in the United States.

So, what’s the best way to use ChatGPT for health questions?

In our research, we have worked with community members to create short educational materials that help people think about the risks that come with relying on AI for health advice, and to consider other options.

Higher risk

Health questions that would usually require clinical expertise to answer carry more risk of serious consequences. This could include:

• finding out what symptoms mean
• asking for advice about treatment
• interpreting test results.

AI responses can often seem sensible – and increasingly personalised – but that doesn’t necessarily mean they are correct or safe. So, for these higher-risk questions, the best option is always to speak with a health professional.

Lower risk

Other health questions are less risky. These tend to be more general, such as:

• learning about a health condition or treatment option
• understanding medical terms
• brainstorming what questions to ask during a medical appointment.

Ideally, AI is just one of the information sources you use.

Where else can I get free advice?

In Australia we have a free 24/7 national phone service, where anyone can speak with a registered nurse about their symptoms: 1800 MEDICARE (1800 633 422).

Symptom Checker, operated by healthdirect, is another publicly funded, evidence-based tool that will help you understand your next steps and connect you with local services.

AI tools are here to stay

For now, we need clear, reliable, independent, and publicly available information about how well the current tools work and the limits of what they can do. This information must be kept up-to-date as the tools evolve.

Purpose-built AI health tools could transform how people gain knowledge, skills and confidence to manage their health. But these need to be designed with communities and clinicians, and prioritise accuracy, equity and transparency.

It is also essential to equip our diverse communities with the knowledge and skills to navigate this new technology safely.

Julie Ayre, Post Doctoral Research Fellow, Sydney Health Literacy Lab, University of Sydney; Adam Dunn, Professor of Biomedical Informatics, University of Sydney, and Kirsten McCaffery, NHMRC Principal Research Fellow, Sydney School of Health, University of Sydney

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

by External Contributor via Digital Information World

Most Americans Don’t Know About Web Hosting, But Know They Want It Cheap

Written by Derick Migliacci. Edited by Asim BN. Reviewed by Ayaz Khan.

Web hosting knowledge in America

Web hosting is one of the most important aspects of websites on the internet. All sites need a hosting provider to keep their pages running and users online. Web hosting is currently supporting billions of websites and is also a multi-billion dollar industry with exponential growth in the future.

As a foundational piece of the internet, you would assume that a majority of internet-dependent Americans would know a lot about this concept. But according to cybersecurity website, All About Cookies, that may not be the case.

A 2025 All About Cookies survey found that only 40% of Americans have a general idea of the concept, while 24% reported they don't know the meaning of web hosting.

Additionally, the All About Cookies survey found that around one-fourth (25%) of respondents couldn’t correctly identify the essential functions and tasks of what a web host does. While most correctly identified that a web host stores website files and data, manages domain names, and makes a website visible to online users, some confuse web hosting responsibilities with tasks that align more with web design.

As evidenced by the graphic above, 27% of respondents incorrectly believed that web hosting consists of design elements, which is typically handled by a dedicated web or UX designer. Other users incorrectly believed cybersecurity measures like protecting sites from viruses or installing antiviruses as tasks a web host would handle. Alongside the incorrect answers, 6% of survey participants admitted they were not fully sure about what web hosting entailed.

Price is the #1 factor for people when considering a web host

Many individuals build websites for personal brands, their own interests, or a variety of other reasons and require a host to get these websites live.

Survey respondents put affordability at the forefront of their minds when making web hosting decisions, as evidenced by the graphic below.

Ease of setup was the second most important prioritization at 46%, followed by security and backups (39%).

Nearly one-third of Americans have tried building a website

With affordability and ease of setup at the top of user’s lists, it’s no surprise that many turn to popular web building providers such as Squarespace or Wix. These sites provide users with ease of setup without having to spend the money or resources to outsource the project. According to the All About Cookies survey, 74% of respondents who tried building a website on their own relied on these tools.

With the combination of ease of use and heavy marketing tactics, web builders are at the top of the list when getting their site up and running. With that ease of use and affordability, these users are trading the full scale controllability that web hosting would provide them for a cheaper and more simplistic avenue than web hosting would.

Control over web hosting versus website builders is understood by only 40% of respondents with general web hosting knowledge. According to data pulled from the All About Cookies survey, 32% of Americans have actually tried building a website at some point in time by attempting to code it from scratch.

Web Building For Small Businesses

Web building is a huge part of helping to build a small business, with the current age of online shopping and digital marketing it’s almost impossible to not employ someone or attempt to host a website yourself for your business.

With many small businesses having limited funds when starting out, a majority build and manage their own site in-house. 65% of small business owners don’t outsource their websites and opt to build one themselves due to factors such as lack of funds or resources.

When it comes to how much small business owners pay for web building, they’re willing to spend $250 annually, on average. Of the small business owners surveyed, 68% reported spending between $50 and $250 annually.

Final Thoughts

Web hosting and building is something that users need in today’s digital age, and these findings prove that small business owners and individuals alike recognize that. The disconnect comes with people’s lack of education on the topic, but their desire to host and build with affordability as their main priority regardless of the reason for why they want to host or build their site.

Having a full knowledge of the web hosting and building landscape could benefit small business owners as well as individuals regardless of if users choose to build/host/code or employ someone to do so. Users who can combine their knowledge of web hosting with their wants and needs can make more informed web hosting decisions that could prove to give them a leg up from their less experienced peers.

About author:
Derick Migliacci is a Digital PR Strategist for AllAboutCookies.org. He brings 3 years of experience in the PR world as well as a passion for digital trends, cybersecurity, and technology.

Read next: 

• Gen Z Financial Struggles: 72% Social Life Impact, 67% Mental Health Hit, 47% Have One Hour or Less Free Daily

• Many Americans Unaware AI Powers Everyday Phone Features Like Weather Alerts and Call Screening
by Guest Contributor via Digital Information World

Gen Z Financial Struggles: 72% Social Life Impact, 67% Mental Health Hit, 47% Have One Hour or Less Free Daily

Edited by Asim BN

Financial challenges have a wide-reaching impact, and in a recent study, Gen Z reported their social lives (72%), mental health (67%) and physical health (62%) have suffered due to money constraints in the last year.

Image: Karolina Grabowska kaboompics.com / pexels

The survey of 2,000 Gen Z hourly workers also found that more than a third (36%) are working multiple jobs, and just about half (47%) have an hour or less of free time each day.

Despite their grind, more than two-thirds of Gen Z workers (68%) doubt they’ll ever be able to fully retire.

And those who are confident they’ll be able to retire are working two jobs, on average, while those who are uncertain about retiring work just one.

This poses the question: Will Gen Z actually need to work two jobs to have enough money to retire?

The survey was conducted by Talker Research on behalf of DailyPay to investigate Gen Z’s financial health and the ways money difficulties have impacted their overall well-being, work and retirement plans.

According to the findings, most Gen Z (77%) think they’ll need to work past the typical retirement age to make ends meet: Half (49%) believe they’ll need to work full-time, and 29% anticipate they’ll need to work at least part-time.

And while, holistically, 67% of Gen Z hourly workers are still proactively saving for retirement, only a small group of those who don’t think they’ll retire are still saving for it just in case (44%), putting this significant group of people in a precarious financial position.

Looking at respondents’ work/life balance, the majority of Gen Z respondents (56%) went so far as to say they don’t feel like they have lives outside of their jobs.

Respondents also said they eat mostly home-cooked meals (44%), shop at discount stores (38%), opt to do free activities for fun (36%) and even cut their own hair (26%) to limit their spending.

Interestingly, Gen Z also reported that they’re financially responsible for one other person, on average, along with themselves.

Considering this, some of their more intense money-saving habits make a bit more sense in context. These include keeping the thermostat very low in the winter and high in the summer (18%), taking short or cold showers (15%) and air-drying their clothes instead of using a dryer (13%).

2025 was an incredibly difficult financial year for many, if not most, and when Gen Zers were asked about the most extreme things they did in the last year to save money, the responses put things into perspective.

Some respondents said they cut back on showering to reduce their water bills, while others turned off their hot water or electricity, did laundry in the bathtub and stopped buying necessities like groceries and toilet paper.

“Gen Z is facing a financial crisis that is actively undermining their health, their work performance and their hope for retirement,” said Andrew Brandman, chief operating officer at DailyPay. “The outdated pay cycle is misaligned with the younger generation’s modern financial needs and, for many, is negatively impacting their stability and well-being.”

In the study, the majority of Gen Z hourly workers (63%) reported their work performance has taken a hit in the last year because of their money worries.

More than a third (35%) also admitted they accepted their current jobs because they were desperate for work and many (31%) ended up in their current positions because they were attracted to how frequently they’d be paid (e.g. daily, weekly), instead of an attraction to the role itself.

“On-Demand Pay is no longer a niche perk; for many, it’s an essential benefit that restores control over pay and provides financial security to the employee,” said Brandman. “Empowering workers with real-time access to the pay they’ve already earned can be one of the most effective ways to help Gen Z stabilize their finances and thrive.”

GEN Z’S TOP MONEY SAVING HACKS

• Eating mostly home-cooked meals (44%)
• Shopping at discount stores (38%)
• Using coupon apps, cashback sites and waiting for sales (36%)
• Doing free activities for fun (36%)
• Meal prepping (31%)
• Buying in bulk (30%)
• Cutting my own hair (26%)
• Buying secondhand things (25%)
• Buying generic brands only (24%)
• DIY home repairs (21%)
• DIY car maintenance (19%)
• Keeping the thermostat very low in the cold months and higher during the warm months (18%)
• Using public transit (17%)
• Carpooling with others when possible (16%)
• Biking or walking instead of driving (15%)
• Taking shorter or cold showers (15%)
• Air-drying clothes instead of using a dryer (13%)

Note: This article was originally published on TalkerResearch and republishing here as per their guidelines.

Read next: Feeling unprepared for the AI boom? You’re not alone
by External Contributor via Digital Information World

Feeling unprepared for the AI boom? You’re not alone

Patrick Barry, University of Michigan

Image: DIW-Aigen

Journalist Ira Glass, who hosts the NPR show “This American Life,” is not a computer scientist. He doesn’t work at Google, Apple or Nvidia. But he does have a great ear for useful phrases, and in 2024 he organized an entire episode around one that might resonate with anyone who feels blindsided by the pace of AI development: “Unprepared for what has already happened.”

Coined by science journalist Alex Steffen, the phrase captures the unsettling feeling that “the experience and expertise you’ve built up” may now be obsolete – or, at least, a lot less valuable than it once was.

Whenever I lead workshops in law firms, government agencies or nonprofit organizations, I hear that same concern. Highly educated, accomplished professionals worry whether there will be a place for them in an economy where generative AI can quickly – and relativity cheaply – complete a growing list of tasks that an extremely large number of people currently get paid to do.

Seeing a future that doesn’t include you

In technology reporter Cade Metz’s 2022 book, “Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World,” he describes the panic that washed over a veteran researcher at Microsoft named Chris Brockett when Brockett first encountered an artificial intelligence program that could essentially perform everything he’d spent decades learning how to master.

Overcome by the thought that a piece of software had now made his entire skill set and knowledge base irrelevant, Brockett was actually rushed to the hospital because he thought he was having a heart attack.

“My 52-year-old body had one of those moments when I saw a future where I wasn’t involved,” he later told Metz.

In his 2018 book, “Life 3.0: Being Human in the Age of Artificial Intelligence,” MIT physicist Max Tegmark expresses a similar anxiety.

“As technology keeps improving, will the rise of AI eventually eclipse those abilities that provide my current sense of self-worth and value on the job market?”

The answer to that question, unnervingly, can often feel outside of our individual control.

“We’re seeing more AI-related products and advancements in a single day than we saw in a single year a decade ago,” a Silicon Valley product manager told a reporter for Vanity Fair back in 2023. Things have only accelerated since then.

Even Dario Amodei – the co-founder and CEO of Anthropic, the company that created the popular chatbot Claude – has been shaken by the increasing power of AI tools. “I think of all the times when I wrote code,” he said in an interview on the tech podcast “Hard Fork.” “It’s like a part of my identity that I’m good at this. And then I’m like, oh, my god, there’s going to be these (AI) systems that [can perform a lot better than I can].”

The irony that these fears live inside the brain of someone who leads one of the most important AI companies in the world is not lost on Amodei.

“Even as the one who’s building these systems,” he added, “even as one of the ones who benefits most from (them), there’s still something a bit threatening about (them).”

Autor and agency

Yet as the labor economist David Autor has argued, we all have more agency over the future than we might think.

In 2024, Autor was interviewed by Bloomberg News soon after publishing a research paper titled Applying AI to Rebuild Middle-Class Jobs. The paper explores the idea that AI, if managed well, might be able to help a larger set of people perform the kind of higher-value – and higher-paying – “decision-making tasks currently arrogated to elite experts like doctors, lawyers, coders and educators.”

This shift, Autor suggests, “would improve the quality of jobs for workers without college degrees, moderate earnings inequality, and – akin to what the Industrial Revolution did for consumer goods – lower the cost of key services such as healthcare, education and legal expertise.”

It’s an interesting, hopeful argument, and Autor, who has spent decades studying the effects of automation and computerization on the workforce, has the intellectual heft to explain it without coming across as Pollyannish.

But what I found most heartening about the interview was Autor’s response to a question about a type of “AI doomerism” that believes that widespread economic displacement is inevitable and there’s nothing we can do to stop it.

“The future should not be treated as a forecasting or prediction exercise,” he said. “It should be treated as a design problem – because the future is not (something) where we just wait and see what happens. … We have enormous control over the future in which we live, and [the quality of that future] depends on the investments and structures that we create today.”

At the starting line

I try to emphasize Autor’s point about the future being more of a “design problem” than a “prediction exercise” in all the AI courses and workshops I teach to law students and lawyers, many of whom fret over their own job prospects.

The nice thing about the current AI moment, I tell them, is that there is still time for deliberate action. Although the first scientific paper on neural networks was published all the way back in 1943, we’re still very much in the early stages of so-called “generative AI.”

No student or employee is hopelessly behind. Nor is anyone commandingly ahead.

Instead, each of us is in an enviable spot: right at the starting line.

Patrick Barry, Clinical Assistant Professor of Law and Director of Digital Academic Initiatives, University of Michigan

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

Read next: AI-Assisted Coding Reaches 29% of New US Software Code

by External Contributor via Digital Information World