One might think, then, that an obvious place for the nascent railways to reached out to would have been the historic university town of Tübingen, about 20 miles from Stuttgart. No so, Tilman Wörtz of the university’s communications department informed me, on a recent visit. In fact, explained Wörtz, an accomplished journalist, the story goes that the academic grandees of the era resisted a connection with the emerging financial and industrial powerhouse, perhaps regarding it as somewhat uncouth and vulgar to distract from deep cultural and scientific considerations to engage with the forces of commerce. So for a long time, the proposed railroad hit the buffers.

Fortunately today, thanks to the efforts of university leaders, the institution strives to connect, both with industry and the wider community. There is now a railway station, and I was thrilled to speak with a number of academics about relaying their research and knowledge to non-academic readers. Indeed, this fascinating read on rapper Haftbefehl, who is the subject of a Netflix documentary gripping Germany has already come out of the sessions, and do stay tuned in the coming weeks and months for more from the University of Tübingen, which was founded in 1477 and is now the first German member institution of The Conversation.

Fast forward a week, and I found myself in the eye of what some cast as a fourth or even fifth industrial revolution, in Dubai, incorporating AI, nanobiology, and bioengineering. The city is pitching itself as being at the heart of, and a driving force in, this new era of change, which sees civic government enabling human and technological collaboration tackling societal issues and powering growth.

For more than a decade, what is now called Protoypes For Humanity has been an exhibition at the heart of this city’s dash for development, powering projects that bring the prospect of solutions to challenges in the environment, energy, health, technology and other spheres.

When I attended Prototypes a year ago, it was still largely a showcase for PhD candidates’ projects from some of the world’s leading universities, many of which are members of The Conversation. In the last 12 months, however, a new element has been developed, under the guidance of Naren Barfield, former Provost and Deputy Vice Chancellor of the UK’s Royal College of Art. This sees senior academics come to the city to deliver papers drawing on key aspects of their research.

Full transparency, I served on the selection panel Professor Barfield designed to finalise the programme and The Conversation was a media partner for the 2025 Prototypes event.

The themes for the year were as follows:

• Wellbeing and health futures
• Sustainable and resilient infrastructure
• Artificial intelligence and augmented intelligence
• Environmental sustainability and climate action
• Socio-economic empowerment and innovation
• Open and speculative categories

Following short paper presentations in the Socio-Economic Empowerment category, Barfield explained the thinking behind the new element of Prototypes and the opportunity for researchers:

We are bringing together some of the world’s sharpest minds and most innovative researchers to tackle challenges faced in different parts of the planet. Dubai and this initiative provide a unique chance to generate ideas across a range of academic disciplines that might not otherwise collaborate in such an impactful way.

The Prototypes for Humanity initiative and the relatively new Professors’ Programme has a proven track record of connecting academia with policymakers, industry, and the public in a way often described elsewhere as aspirational. Here, it is actually happening.

The reference to industry struck a chord, perhaps given that I’d so recently heard that story of detachment from 19th century Stuttgart, but also because it’s a grumble I regularly encounter across the world when it comes to academia and its engagement (or lack of) beyond the university sector today.

At the conference venue, in the Emirates Towers of Dubai International Financial District, Tadeu Baldani Caravieri, director of Prototypes for Humanity, discussed the thinking behind the project and potential routes forward.

At Protoypes we’ve seen how researchers can directly drive innovation in partnership with industry and, in the case of Dubai, with the city government as a facilitator.

This has been possible thanks to some of the advantages of this state and region. But these are solutions that can, and do present wider benefits – in some cases globally relevant solutions solutions.

He later added:

This edition [of Prototypes] helped to confirm fundamental assumptions for the space we operate in, i.e. creating bridges between academic ingenuity and real-world needs. The main one is that, although there is sometimes a disconnect between university innovation capabilities and industry needs, there is genuine interest, across all of the parts in this equation, to overcome obstacles and do more. We have enabled and witnessed very promising and results-oriented conversations between academia and potential partners, from PhDs and private sector discussing pilots in applied robotics, to professors supporting a humanitarian agency to rethink aid allocation systems, to multinationals looking to fuel their R&D roadmaps.

Dubai is an excellent incubator for these bridges we are building but, in keeping with the city’s outlook and spirit, we want to enable impact across the world – so it’s just natural that, in the future, we hope to open structured avenues for multi-city collaborations, where local ecosystems complement each other’s strengths.

Prototypes’ community brings in research talent from more than 800 universities around the world, including many academics who have also engaged with The Conversation. For instance, Jeremy Howick, of the University of Leicester, presented on empathy in healthcare in the age of AI, and has written this account. Further articles based on projects that exhibited and on the professors’ papers will be published on The Conversation and will be accessible via this link.

Stay tuned to read more on critical and diverse research relating to subjects such as monitoring and diagnosing pre-eclampsia (Patricia Maguire, University College Dublin, using seaweed to create a sustainable packaging alternative (Austeja Platukyte, Vilnius Academy of Arts ) and the emergent Internet of Beings (Francesco Grillo, Bocconi University, Milan).

Across state legislatures and in Congress, debates are intensifying about the value of funding certain college degree programs – and higher education, more broadly.

The growing popularity of professional graduate degrees over the past several decades – including programs in business administration and engineering management – has reshaped the economics of higher education. Unlike traditional academic graduate programs, which are often centered on research and scholarship, these professionally oriented degrees are designed primarily for workforce advancement and typically charge much higher tuition.

These programs are often expensive for students and are sometimes described as cash-cow degrees for colleges and universities, because the tuition revenue far exceeds the instructional costs.

Some universities and colleges also leverage their brands to offer online, executive or certificate-based versions of these programs, attracting many students from the U.S. and abroad who pay the full tuition. This steady revenue helps universities subsidize tuition for other students who cannot pay the full rate, among other things.

Yet a quiet tension underlies this evolution in higher education – the widening divide between practical, technical training and a comprehensive education that perhaps is more likely to encourage students to inquire, reflect and innovate as they learn.

An overlooked factor

Some states, including Texas, track salary data for graduates of every program to measure worth through short-term earnings. This approach may strike many students and their families as useful, but I believe it overlooks a part of what makes higher education valuable.

A healthy higher education system depends not only on producing employable graduates but also on cultivating citizens and leaders who can interpret uncertainty, question assumptions and connect ideas across disciplines.

When assessing disciplines such as English, philosophy, history and world languages, I think that we should acknowledge their contributions to critical thought, communication and ethical reasoning.

These academic disciplines encourage students to synthesize ideas, construct arguments and engage in meaningful debate. Some law schools often draw their strongest students from these backgrounds because they nurture analytical and rhetorical skills essential for navigating complex civic and legal issues.

Historically, poets and writers have often been among the first to be silenced by authoritarian regimes. It’s a reminder of the societal power of inquiry and expression that I believe higher education should protect.

Why students stay on narrow professional paths

Students entering college today face significant pressure to choose what they might see as safe majors that will result in a well-paying career. For aspiring physicians and engineers, the path is often scripted early by steering them toward physical and biosciences. High test scores, internships and other stepping stones are treated as nonnegotiable. Parents and peers can reinforce this mindset.

Most colleges and universities do not reward a future medical student who wants to major in comparative literature, or an engineering student who is spending time on philosophy.

Students’ majors also typically place course requirements on them, in addition to a school’s general course requirements. This often does not leave a lot of room for students to experiment with different classes, especially if they are pursuing vocationally focused majors, such as engineering.

As a result, I’ve seen many students trade curiosity for credentialing, believing that professional identity must come before intellectual exploration.

As someone who began my education in psychology and later transitioned into engineering, I have seen how different intellectual traditions approach the same human questions. Psychology teaches people to observe behavior and design experiments. Engineering trains students to model systems and optimize performance.

When combined, they help reveal how humans interact with technology and how technological solutions reshape human behavior.

In my view, these are questions neither field can answer alone.

Initiative is the missing ingredient

One of the most important and often overlooked ingredients in thriving high tech, medical and business environments is initiative. I believe students in the humanities routinely practice taking initiative by framing questions, interpreting incomplete information and proposing original arguments. These skills are crucial for scientific or business innovation, but they are often not emphasized in structured science, technology, engineering and mathematics – or STEM – coursework.

Initiative involves the willingness to move first and to see around corners, defining the next what-if, rallying others and building something meaningful even when the path is uncertain.

To help my engineering students practice taking initiative, I often give them deliberately vague instructions – something they rarely experience in their coursework. Many students, even highly capable ones, hesitate to take initiative because their schooling experience has largely rewarded caution and compliance over exploration. They wait for clarity or for permission – not because they lack ability, but because they are afraid to be wrong.

Yet in business, research labs, design studios, hospitals and engineering firms, initiative is the quality employers most urgently need and cannot easily teach. Broader educational approaches help cultivate this confidence by encouraging students to interpret ambiguity rather than avoid it.

How teaching can evolve

Helping all students develop a sense of initiative and innovation requires university leaders to rethink what success looks like.

Universities can begin with achievable steps, such as rewarding cross-disciplinary teaching and joint appointments in promotion and tenure criteria.

At the University of Iowa’s Driving Safety Research Institute, where our teams blend engineering, medicine, public health and psychology, students quickly learn that a safe automated vehicle is not just a technical system but also a behavioral one. Understanding how human drivers respond to automation is as important as the algorithms that govern the vehicle.

Other institutions are modeling this approach of integrating social, behavioral and physical sciences.

Olin College of Engineering, a school in Needham, Massachusetts, builds every project around both technical feasibility and human context. Courses are often co-taught by humanities and engineering professors, and projects require students to articulate not only what they built but why it matters.

Still, integrating liberal and technical education is difficult in practice. Professional curricula often overflow with accreditation requirements. Faculty incentives reward specialization more than collaboration. Students and parents, anxious about debt and job security, hesitate to spend credits outside of a student’s major.

Rethinking what success means

I believe that higher education’s purpose is not to produce uniform workers but adaptable thinkers.

It might not be productive to center conversations about defending the liberal arts or glorifying STEM. Rather, I think that people’s focus should be on recognizing that each field is incomplete without the other.

Education for a complex world must cultivate depth, initiative and perspective. When students connect disciplines, question assumptions and act with purpose, they are prepared not only for their first job but for a lifetime of learning and leadership.

Daniel V. McGehee does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Over the past few years, we have repeatedly been told artificial intelligence (AI) is coming for our jobs.

In May last year, the International Monetary Fund warned the technology was hitting labour markets like a “tsunami”. Many of the tech giants behind the technology have been making their own confident predictions about which jobs will be wiped out first.

Yet inside many Australian firms, the reality is much less dramatic.

Last week, the Reserve Bank of Australia released the findings from a 2025 survey of 100 medium and large-sized firms, which sought to understand how technology investments have been affecting the way businesses run. It found enterprise-wide AI transformation was the exception rather than the norm.

This presents a strange mismatch: a loud global story about an AI “jobpocalypse”, and a much quieter story inside firms about experiments, pilots and a lot of waiting around for real productivity gains to show up. The Reserve Bank’s report helps explain why.

Laying the groundwork

Australian firms haven’t been ignoring AI and technology. The report notes that over the past decade, total IT investment has grown by almost 80%, faster than other types of capital investment.

Much of that has gone into software, cybersecurity, cloud and upgrading internal systems such as customer relationship management and enterprise resource planning platforms.

While these investments are mostly about keeping the lights on and do not deliver big productivity pay offs on their own, they are essential groundwork to make firms’ systems and processes ready for AI.

‘Minimal’ use of AI

AI is now starting to move up the priority list. About two-thirds of firms surveyed said they have adopted AI “in some form”.

But for the largest group – representing nearly 40% of all respondents – this use was still “minimal”.

The most common use cases were tasks such as summarising emails or drafting text using off-the-shelf-products like Microsoft Copilot or ChatGPT.

Just over 20% of all firms reported “moderate” adoption, using AI to assist with tasks such as demand forecasting or inventory management.

And a small frontier group – less than 10% of all firms – said they had embedded AI into more advanced processes such as fraud detection.

Impact on jobs

Asked about jobs and the future, companies said AI and automation would likely save some labour, particularly on traditionally time-consuming tasks. But they didn’t expect it to cause catastrophic job losses.

Around half said they expected AI and machine learning would lead to a modest reduction in headcount over the next three years.

Firms planning to reduce their headcount expected to do so through natural attrition (employees resigning or retiring), lower intake of new staff, and redundancies.

Routine finance, administration and contact centre work were seen as most at risk of being automated. But firms also said AI would likely create demand for new roles related to the technology – such as in cybersecurity and redesigning processes.

Why are Australian firms so slow?

The Reserve Bank’s findings align with other reports showing Australia as a cautious adopter of AI when compared, for example, to the United States.

Global report cards on AI adoption and innovation more broadly consistently place Australia behind many other advanced economies.

A few key themes stand out from the Reserve Bank’s survey and other international evidence.

First, much of Australia’s recent technology investment has gone into cybersecurity, compliance, legacy system upgrades, data quality improvements and cloud migration. This is a necessary first step before AI investments.

Second, many firms have been struggling to hire the skills needed to drive AI transformation, such as data engineers and data scientists.

And third, Australia’s business culture is cautious, with low trust and high levels of concern about AI. Adding to this, individuals’ “shadow” use of AI tools (without telling their bosses) can mask the true extent of the technology’s adoption.

Do Australian firms need more support?

If Australian businesses want to move beyond “ChatGPT for emails”, evidence points to several practical steps they and governments can make.

Better engagement of company boards is crucial for moving beyond shallow pilots of digital technology, especially when workers distrust AI and directors see it as a risk. Providing evidence of more successful use cases is an important part of this shift.

Australia also needs to invest in lifting AI skills across the workforce, with a particular focus on reskilling workers and preparing for the likely decline in entry-level jobs for young people.

The report indicates firms see the uncertain regulatory environment as a major barrier. The goal should be to have clear, risk-based rules that make safe experimentation easier.

Without these, firms are effectively told two things at once: “move fast to stay competitive” and “don’t you dare breach privacy, copyright or ethics”.

Read more:
AI ‘workslop’ is creating unnecessary extra work. Here’s how we can stop it

Brace for a bumpy ride

It appears we may not yet be racing toward a world without workers, but rather a messy and uneven transition.

The number of AI firms in Australia has grown substantially, and there is clear momentum and optimism.

For now, however, many businesses are still working out how to make AI useful, embed it into workflows and manage its risks. Their biggest complaints are about ambiguous regulation and skills shortages – not having too many humans.

Stan Karanasios does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

US president Donald Trump’s 15% baseline tariffs on EU imports may read like a throwback to old-school protectionism, designed to safeguard American jobs and manufacturing. But in today’s globalised and digitally driven economy, the risk isn’t just to steel or car factories, it’s to innovation itself.

The world’s most advanced technologies rely on complex, deeply integrated supply chains. Evidence from 2023 shows that even temporary US tariff shocks disrupted relationships between firms. And these tariffs won’t just hit the EU. They will disrupt the high-value tech ecosystems of partners like the UK – especially firms contributing to artificial intelligence (AI), semiconductor design and cybersecurity.

These industries underpin national resilience, data security and the competitiveness of advanced economies. For the UK, which often positions itself as a global innovation hub post-Brexit, the fallout could be significant.

Take ARM Holdings, the Cambridge-based semiconductor giant whose chip designs power 99% of the world’s smartphones and an increasing share of AI infrastructure.

ARM doesn’t manufacture chips itself. Instead, it licenses its architecture to firms like Apple, Nvidia and Qualcomm. That makes it a prime example of the UK’s value in the global innovation chain: high intellectual property (IP), low carbon footprint, huge reach.

ARM’s position as a vital link in the supply chain underlines another point. Trade policy aimed at traditional manufacturing sectors can inadvertently destabilise tech-intensive, IP-led sectors like semiconductors and software. This is echoed in research examining global tariff spillovers on tech competitiveness.

If tariffs are applied to components or design work linked to traded goods that cross EU or UK borders en route to US manufacturers, it introduces a layer of risk and cost to innovative firms and their global partners.

Even if a company’s work isn’t directly taxed, the uncertainty and red tape may make US firms think twice about sourcing from outside US jurisdictions. While Trump might present that as a victory for American manufacturing, in reality it could raise costs for US producers, damage innovation and make US firms less competitive in the industries he aims to protect.

It’s not just the giants at risk. In the UK, Cambridge’s wider tech cluster, sometimes called “Silicon Fen”, is home to dozens of ambitious AI firms. With operations spanning the UK, EU and US, companies like this depend on fast, flexible and trusted international partnerships to develop, deploy and refine their products. Tariff-related disruptions make collaboration harder at a time when speed is a competitive advantage.

This is not hypothetical. Tariffs reduce access to large markets – and when markets shrink, firms reduce investment in research and innovation.

What Trump gets wrong

Trump’s broader narrative suggests tariffs can bring back jobs and restore industrial power to the US. But innovation doesn’t work like that. A semiconductor isn’t made in one place. A cybersecurity system isn’t built by a single team. These are networked, iterative processes, involving researchers, suppliers, data centres and talent pools across continents. Disrupt that flow and you slow progress.

The UK is especially exposed because of its unique post-Brexit positioning. It trades independently from the EU but is still tightly intertwined with it, particularly in tech sectors.

Many UK firms use EU distribution centres to reach the US market or collaborate with EU partners on joint projects involving data, hardware or software This reflects the fact that the UK remains tightly integrated into European supply and value chains – exporting £358 billion of goods and services to the EU in 2024 alone. Tariffs targeting the EU could easily catch UK-originated components or design work as collateral damage.

Modelling has shown that Trump’s proposed tariffs could reduce EU-US trade volumes across multiple sectors, particularly in tech, where integrated production routes are standard.

Small and medium-sized enterprises and startups may find themselves most vulnerable. These firms typically can’t absorb sudden cost increases or legal complexities. Nor can they easily switch suppliers or reroute through different customs zones.

If you’re an early-stage AI company relying on a specific chip from Germany and a US cloud partner to train your model, a 15% tariff adds months of delays and thousands of pounds in costs, just to maintain the status quo.

From a policy perspective, the impact goes deeper. The UK government has championed sectors like AI, fintech and clean tech as pillars of economic growth. But these industries are only as strong as the networks that sustain them. If global fragmentation accelerates, the UK risks losing its role as a bridge between the US and the EU.

Meanwhile, countries like China continue to invest heavily in consolidating their innovation supply chains, from chip manufacturing to AI research, particularly in efforts to secure domestic control over advanced technologies and semiconductors. This is something that the US and EU have only recently begun to coordinate on.

In the short term, Trump’s tariff strategy may boost US customs revenue, which is up US$50 billion (£38 billion) a month by some estimates.

But this is not “free money”. These revenues are largely absorbed by businesses and ultimately passed on to consumers through higher prices, or to smaller suppliers through squeezed profit margins.

More fundamentally, it represents a belief that economic strength comes from protection rather than connection. But innovation has never worked that way. It thrives on collaboration, trust and scale. Tariffs may be politically effective, but economically they are the equivalent of building firewalls between teams that are supposed to be co-writing the future.

As the UK charts its post-Brexit global role, aligning itself with open, innovation-driven economies should be a priority. That means standing up for the integrity of global tech supply chains and recognising that disruption to one part of the system can reverberate far beyond its intended target.

Matthew Allen is affiliated with The Conservative Party as a party member. I am not a councillor or an MP. I am also not active in any campaigning.

Technological innovations can seem relentless. In computing, some have proclaimed that “a year in machine learning is a century in any other field.” But how do you know whether those advancements are hype or reality?

Failures quickly multiply when there’s a deluge of new technology, especially when these developments haven’t been properly tested or fully understood. Even technological innovations from trusted labs and organizations sometimes result in spectacular failures. Think of IBM Watson, an AI program the company hailed as a revolutionary tool for cancer treatment in 2011. However, rather than evaluating the tool based on patient outcomes, IBM used less relevant measures – possibly even irrelevant ones, such as expert ratings rather than patient outcomes. As a result, IBM Watson not only failed to offer doctors reliable and innovative treatment recommendations, it also suggested harmful ones.

When ChatGPT was released in November 2022, interest in AI expanded rapidly across industry and in science alongside ballooning claims of its efficacy. But as the vast majority of companies are seeing their attempts at incorporating generative AI fail, questions about whether the technology does what developers promised are coming to the fore.

In a world of rapid technological change, a pressing question arises: How can people determine whether a new technological marvel genuinely works and is safe to use?

Borrowing from the language of science, this question is really about validity – that is, the soundness, trustworthiness and dependability of a claim. Validity is the ultimate verdict of whether a scientific claim accurately reflects reality. Think of it as quality control for science: It helps researchers know whether a medication really cures a disease, a health-tracking app truly improves fitness, or a model of a black hole genuinely describes how it behaves in space.

How to evaluate validity for new technologies and innovations has been unclear, in part because science has mostly focused on validating claims about the natural world.

In our work as researchers who study how to evaluate science across disciplines, we developed a framework to assess the validity of any design, be it a new technology or policy. We believe setting clear and consistent standards for validity and learning how to assess it can empower people to make informed decisions about technology – and determine whether a new technology will truly deliver on its promise.

Validity is the bedrock of knowledge

Historically, validity was primarily concerned with ensuring the precision of scientific measurements, such as whether a thermometer correctly measures temperature or a psychological test accurately assesses anxiety. Over time, it became clear that there is more than just one kind of validity.

Different scientific fields have their own ways of evaluating validity. Engineers test new designs against safety and performance standards. Medical researchers use controlled experiments to verify treatments are more effective than existing options.

Researchers across fields use different types of validity, depending on the kind of claim they’re making.

Internal validity asks whether the relationship between two variables is truly causal. A medical researcher, for instance, might run a randomized controlled trial to be sure that a new drug led patients to recover rather than some other factor such as the placebo effect.

External validity is about generalization – whether those results would still hold outside the lab or in a broader or different population. An example of low external validity is how many early studies that work in mice don’t always translate to people.

Construct validity, on the other hand, is about meaning. Psychologists and social scientists rely on it when they ask whether a test or survey really captures the idea it’s supposed to measure. Does a grit scale actually reflect perseverance or just stubbornness?

Finally, ecological validity asks whether something works in the real world rather than just under ideal lab conditions. A behavioral model or AI system might perform brilliantly in simulation but fail once human behavior, noisy data or institutional complexity enter the picture.

Across all these types of validity, the goal is the same: ensuring that scientific tools – from lab experiments to algorithms – connect faithfully to the reality they aim to explain.

Evaluating technology claims

We developed a method to help researchers across disciplines clearly test the reliability and effectiveness of their inventions and theories. The design science validity framework identifies three critical kinds of claims researchers usually make about the utility of a technology, innovation, theory, model or method.

First, a criterion claim asserts that a discovery delivers beneficial outcomes, typically by outperforming current standards. These claims justify the technology’s utility by showing clear advantages over existing alternatives.

For example, developers of generative AI models such as ChatGPT may see higher engagement with the technology the more it flatters and agrees with the user. As a result, they may program the technology to be more affirming – a feature called sycophancy – in order to increase user retention. The AI models meet the criterion claim of users considering them more flattering than talking to people. However, this does little to improve the technology’s efficacy in tasks such as helping resolve mental health issues or relationship problems.

Second, a causal claim addresses how specific components or features of a technology directly contribute to its success or failure. In other words, it is a claim that shows researchers know what makes a technology effective and exactly why it works.

Looking at AI models and excessive flattery, researchers found that interacting with more sycophantic models reduced users’ willingness to repair interpersonal conflict and increased their conviction of being in the right. The causal claim here is that the AI feature of sycophancy reduces a user’s desire to repair conflict.

Third, a context claim specifies where and under what conditions a technology is expected to function effectively. These claims explore whether the benefits of a technology or system generalize beyond the lab and can reach other populations and settings.

In the same study, researchers examined how excessive flattery affected user actions in other datasets, including the “Am I the Asshole” community on Reddit. They found that AI models were more affirming of user decisions than people were, even when the user was describing manipulative or harmful behavior. This supports the context claim that sycophantic behavior from an AI model applies across different conversational contexts and populations.

Measuring validity as a consumer

Understanding the validity of scientific innovations and consumer technologies is critical for scientists and the general public. For scientists, it’s a road map to ensure their inventions are rigorously evaluated. And for the public, it means knowing that the tools and systems they depend on – such as health apps, medications and financial platforms – are truly safe, effective and beneficial.

Here’s how you can use validity to understand the scientific and technological innovations happening around you.

Because it is difficult to compare every feature of two technologies against each other, focus on which features you value most from a technology or model. For example, do you prefer a chatbot to be accurate or better for privacy? Examine claims for it in that area, and check that it is as good as claimed.

Consider not only the types of claims made for a technology but also which claims are not made. For example, does a chatbot company address bias in its model? It’s your key to knowing whether you see untested and potentially unsafe hype or a genuine advancement.

By understanding validity, organizations and consumers can cut through the hype and get to the truth behind the latest technologies.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

What makes some countries rich and others poor? Is there any action a country can take to improve living standards for its citizens? Economists have wondered about this for centuries. If the answer to the second question is yes, then the impact on people’s lives could be staggering.

This year’s Sveriges Riksbank Prize in Economic Sciences (commonly known as the Nobel prize for economics) has gone to three researchers who have provided answers to these questions: Philippe Aghion, Peter Howitt and Joel Mokyr.

For most of human history, economic stagnation has been the norm – modern economic growth is very recent from a historical point of view. This year’s winners have been honoured for their contributions towards explaining how to achieve sustained economic growth.

At the beginning of the 1980s, theories around economic growth were largely dominated by the works of American economist Robert Solow. An important conclusion emerged: in the long-run, per-capita income growth is determined by technological progress.

Solow’s framework, however, did not explain how technology accumulates over time, nor the role of institutions and policies in boosting it. As such, the theory can neither explain why countries grow differently for sustained periods nor what kind of policies could help a country improve its long-run growth performance.

It’s possible to argue that technological innovation comes from the work of scientists, who are motivated less by money than the rest of society might be. As such, there would be little that countries could do to intervene – technological innovations would be the result of the scientists’ own interests and motivations.

But that thinking changed with the emergence of endogenous growth theory, which aims to explain which forces drive innovation. This includes the works of Paul Romer, Nobel prizewinner in 2018, as well as this year’s winners Aghion and Howitt.

These three authors advocate for theories in which technological progress ultimately derives from firms trying to create new products (Romer) or improve the quality of existing products (Aghion and Howitt). For firms to try to break new ground, they need to have the right incentives.

Creative destruction

While Romer recognises the importance of intellectual property rights to reward firms financially for creating new products, the framework of Aghion and Howitt outlines the importance of something known as “creative destruction”.

This is where innovation results from a battle between firms trying to get the best-quality products to meet consumer needs. In their framework, a new innovation means the displacement of an existing one.

In their basic model, protecting intellectual property is important in order to reward firms for innovating. But at the same time, innovations do not come from leaders but from new entrants to the industry. Incumbents do not have the same incentive to innovate because it will not improve their position in the sector. Consequently, too much protection generates barriers to entry and may slow growth.

But what is less explored in their work is the idea that each innovation brings winners (consumers and innovative firms) and losers (firms and workers under the old, displaced technology). These tensions could shape a country’s destiny in terms of growth – as other works have pointed out, the owners of the old technology may try to block innovation.

This is where Mokyr complements these works perfectly by providing a historical context. Mokyr’s work focuses on the origins of the Industrial Revolution and also the history of technological progress from ancient times until today.

Mokyr noted that while scientific discoveries were behind technological progress, a scientific discovery was not a guarantee of technological advances.

It was only when the modern world started to apply the knowledge discovered by scientists to problems that would improve people’s lives that humans saw sustained growth. In Mokyr’s book The Gifts of Athena, he argues that the Enlightenment was behind the change in scientists’ motivations.

In Mokyr’s works, for growth to be sustained it is vital that knowledge flows and accumulates. This was the spirit embedded in the Industrial Revolution and it’s what fostered the creation of the institution I am working in – the University of Sheffield, which enjoyed financial support from the steel industry in the 19th century.

Mokyr’s later works emphasise the key role of a culture of knowledge in order for growth to improve living standards. As such, openness to new ideas becomes crucial.

Similarly, Aghion and Howitt’s framework has become a standard tool in economics. It has been used to explore many important questions for human wellbeing: the relationship between competition and innovation, unemployment and growth, growth and income inequality, and globalisation, among many other topics.

Analysis using their framework still has an impact on our lives today. It is present in policy debates around big data, artificial intelligence and green innovation. And Mokyr’s analysis of how knowledge accumulates poses a central question around what countries can do to encourage an innovation ecosystem and improve the lives of their citizens.

But this year’s prize is also a warning about the consequences of damaging the engines of growth. Scientists collaborating with firms to advance living standards is the ultimate elixir for growth. Undermining science, globalisation and competition might not be the right recipe.

Antonio Navas does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Three economists working in the area of “innovation-driven economic growth” have won this year’s Nobel Memorial Prize in Economic Sciences.

Half of the 11 million Swedish kronor (about A$1.8 million) prize was awarded to Joel Mokyr, a Dutch-born economic historian at Northwestern University.

The other half was jointly awarded to Philippe Aghion, a French economist at Collège de France and INSEAD, and Peter Howitt, a Canadian economist at Brown University.

Collectively, the trio’s work has examined the importance of innovation in driving sustainable economic growth. It has also highlighted that in dynamic economies, old firms die as new firms are being born.

Innovation drives sustainable growth

As noted by the Royal Swedish Academy of Sciences, economic growth has lifted billions of people out of poverty over the past two centuries. While we take this as normal, it is actually very unusual in the broad sweep of history.

The period since around 1800 is the first in human history when there has been sustained economic growth. This warns us we should not be complacent. Poor policy could see economies stagnate again.

One of the Nobel judges gave the example that in Sweden and the United Kingdom there was little improvement in living standards in the four centuries between 1300 and 1700.

Mokyr’s work showed that prior to the Industrial Revolution, innovations were more a matter of trial and error than being based on scientific understanding. He has argued that sustained economic growth would not emerge in:

a world of engineering without mechanics, iron-making without metallurgy, farming without soil science, mining without geology, water-power without hydraulics, dyemaking without organic chemistry, and medical practice without microbiology and immunology.

Mokyr gives the example of sterilising surgical instruments. This had been advocated in the 1840s or earlier. But surgeons were offended by the suggestion they might be transmitting diseases. It was only after the work of Louis Pasteur and Joseph Lister in the 1860s that the role of germs was understood and sterilisation became common.

Mokyr emphasised the importance of society being open to new ideas. As the Nobel committee put it:

practitioners, ready to engage with science, along with a societal climate embracing change, were, according to Mokyr, key reasons why the Industrial Revolution started in Britain.

Winners and losers

This year’s other two laureates, Aghion and Howitt, recognised that innovations create both winning and losing firms. In the US, about 10% of firms enter and 10% leave the market each year. Promoting economic growth requires an understanding of both processes.

Their 1992 article built on earlier work on the concept of “endogenous growth” – the idea that economic growth is
generated by factors inside an economic system, not the result of forces that impinge from outside. This earned a Nobel prize for Paul Romer in 2018.

It also drew on earlier work on “creative destruction” by Joseph Schumpeter.

The model created by Aghion and Howitt implies governments need to be careful how they design subsidies to encourage innovation.

If companies think that any innovation they invest in is just going to be overtaken (meaning they would lose their advantage), they won’t invest as much in innovation.

Their work also supports the idea governments have a role in supporting and retraining those workers who lose their jobs in firms that are displaced by more innovative competitors.

This will build political support for policies that encourage economic growth, as well.

‘Dark clouds’ on the horizon?

The three laureates all favour economic growth, in contrast to growing concerns about the impact of endless growth on the planet.

In an interview after the announcement, however, Aghion called for carbon pricing to make economic growth consistent with reducing greenhouse gas emissions.

He also warned about the gathering “dark clouds” of tariffs; that creating barriers to trade could reduce economic growth.

And he said we need to ensure today’s innovators do not stifle future innovators through anti-competitive practices.

The newest Nobel prize

The economics prize was not one of the five originally nominated in Swedish chemist Alfred Nobel’s will in 1895. It is formally called the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. It was first awarded in 1969.

The awards to Mokyr and Howitt continue the pattern of the economics prize being dominated by researchers working at US universities.

It also continues the pattern of over-representation of men. Only three of the 99 economics laureates have been women.

Arguably, economics professor Rachel Griffith, rather than Mokyr, could have shared the prize with Aghion and Howitt this year. She co-authored the book Competition and Growth with Aghion, and co-wrote an article on competition with both of them.

John Hawkins does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Governments of all stripes provide support to small businesses in the form of tax concessions, lighter-touch regulation or government grants. They’re called the “engine room” of the economy. But is small really best?

In recent research, my co-authors and I explored this question by looking at the contributions that firms of different ages and size make to the economy.

We found new and young businesses, rather than small, old businesses, are the drivers of economic growth. This matters, as the economic dynamism these young firms drive boosts productivity – the major determinant of incomes in the long run. But government policy is focused on size, which may be holding us back.

Using de-identified data from the Australian Bureau of Statistics that tracks all businesses in Australia, we analysed the economic performance of each individual business in the market sector from 2003 onward – from pubs and cafes to manufacturing.

This includes all business types and sizes, from the corner store to the major corporates. We analysed how many people they employed, their economic value-add (think of it as their contribution to the economy), and their labour productivity (how much stuff they produce for a given amount of workers and hours).

Australia has some 2.7 million small businesses, with 440,000 new businesses started in 2024-25. But our study finds it’s young firms (those aged five years or less) that punch above their weight and have an outsized positive contribution to the economy, while small, old firms (aged over five, and with fewer than 15 employees) have a net negative impact.

Engines of job creation

Our research found young businesses contribute six percentage points to overall annual headcount growth. This compares to small, old firms, which actually reduce overall annual headcount growth by 4.5 percentage points, due to these firms stagnating, shrinking and closing down.

This difference is underlined when we look separately at job creation and job destruction. Young firms contribute 59% of new jobs, while small old firms account for just 16%.

This is even more stark when comparing job losses: small old businesses account for 41% of all job destruction. Large old businesses – often the focus of announced corporate layoffs – account for 18% of job destruction.

So is young best then? As economists like to say – it depends.

We analysed the growth trajectories of young firms and found significant differences.

Of firms that survive to age five, high-performing young firms employ twice the number of workers than the average firm of the same age, and are over 40% more productive.

But the typical new business (in its first year of activity) is relatively small, employing only around two people. And it stops growing relatively quickly – on average new firms plateau after two years of operation. This highlights the vast differences in firm types among young firms.

This might not be surprising to some readers; not all new businesses are started with the goal of being the next Atlassian or Canva.

People start businesses for a range of reasons: whether you’re a lawyer who’d rather be your own boss than work for a large corporation; an IT worker who recently had a child and values control over the flexibility of your time; or a tradie who benefits from the tax implications of running your own business.

Smarter ways to support all businesses

This highlights the importance of policymakers being clear on what they’re trying to achieve when providing subsidies and support to businesses.

Our analysis suggests if the policy goal is to spur economic growth and employment, then targeting assistance to small businesses is poor policy. But this doesn’t necessarily mean we should take that assistance and give it to young firms instead.

Since a small number of high-performing young firms drive economic growth, we won’t always know which young firms these will be. Policy that subsidises young firms would potentially still be ineffective. And we know government has a chequered history with picking winners – see the more than A$30 billion provided to the car manufacturing sector.

So, what should government do?

One often overlooked and potentially counterintuitive finding from our research is the role of firm “exits” – businesses closing down or moving onto new ventures. Firms that exit are 20% less productive than the average firm in their industry five years before they close down, and their productivity declines further as they approach closure.

But the rate of business closures in Australia has been declining over time. Policies that remove impediments from orderly business closure, including supporting affected workers, would help workers and capital to be re-allocated to more productive and innovative firms.

Specific business assistance and targeting is always fraught with difficulty. Policymakers can instead focus on broader policy settings that are conducive to growth, and that apply to all firms rather than just a subset.

These efforts, such as streamlining regulation and ensuring it is fit for purpose for all businesses, would be in line with some of the principles and reform directions agreed at Treasurer Jim Chalmers’ economic reform roundtable earlier this year.

The author thanks Rachel Lee and Ewan Rankin, researchers at the e61 Institute, for their contribution to this article.

Lachlan Vass is affiliated with the e61 Institute.

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to CuriousKidsUS@theconversation.com.

Who invented the light bulb? – Preben, age 5, New York City

When people name the most important inventions in history, light bulbs are usually on the list. They were much safer than earlier light sources, and they made more activities, for both work and play, possible after the Sun went down.

More than a century after its invention, illustrators still use a lit bulb to symbolize a great idea. Credit typically goes to inventor and entrepreneur Thomas Edison, who created the first commercial light and power system in the United States.

But as a historian and author of a book about how electric lighting changed the U.S., I know that the actual story is more complicated and interesting. It shows that complex inventions are not created by a single genius, no matter how talented he or she may be, but by many creative minds and hands working on the same problem.

Making light − and delivering it

In the 1870s, Edison raced against other inventors to find a way of producing light from electric current. Americans were keen to give up their gas and kerosene lamps for something that promised to be cleaner and safer. Candles offered little light and posed a fire hazard. Some customers in cities had brighter gas lamps, but they were expensive, hard to operate and polluted the air.

When Edison began working on the challenge, he learned from many other inventors’ ideas and failed experiments. They all were trying to figure out how to send a current through a thin carbon thread encased in glass, making it hot enough to glow without burning out.

In England, for example, chemist Joseph Swan patented an incandescent bulb and lit his own house in 1878. Then in 1881, at a great exhibition on electricity in Paris, Edison and several other inventors demonstrated their light bulbs.

Edison’s version proved to be the brightest and longest-lasting. In 1882 he connected it to a full working system that lit up dozens of homes and offices in downtown Manhattan.

But Edison’s bulb was just one piece of a much more complicated system that included an efficient dynamo – the powerful machine that generated electricity – plus a network of underground wires and new types of lamps. Edison also created the meter, a device that measured how much electricity each household used, so that he could tell how much to charge his customers.

Edison’s invention wasn’t just a science experiment – it was a commercial product that many people proved eager to buy.

Inventing an invention factory

As I show in my book, Edison did not solve these many technical challenges on his own.

At his farmhouse laboratory in Menlo Park, New Jersey, Edison hired a team of skilled technicians and trained scientists, and he filled his lab with every possible tool and material. He liked to boast that he had only a fourth grade education, but he knew enough to recruit men who had the skills he lacked. Edison also convinced banker J.P. Morgan and other investors to provide financial backing to pay for his experiments and bring them to market.

Historians often say that Edison’s greatest invention was this collaborative workshop, which he called an “invention factory.” It was capable of launching amazing new machines on a regular basis. Edison set the agenda for its work – a role that earned him the nickname “the wizard of Menlo Park.”

Here was the beginning of what we now call “research and development” – the network of universities and laboratories that produce technological breakthroughs today, ranging from lifesaving vaccines to the internet, as well as many improvements in the electric lights we use now.

Sparking an electric revolution

Many people found creative ways to use Edison’s light bulb. Factory owners and office managers installed electric light to extend the workday past sunset. Others used it for fun purposes, such as movie marquees, amusement parks, store windows, Christmas trees and evening baseball games.

Theater directors and photographers adapted the light to their arts. Doctors used small bulbs to peer inside the body during surgery. Architects and city planners, sign-makers and deep-sea explorers adapted the new light for all kinds of specialized uses. Through their actions, humanity’s relationship to day and night was reinvented – often in ways that Edison never could have anticipated.

Today people take for granted that they can have all the light they need at the flick of a switch. But that luxury requires a network of power stations, transmission lines and utility poles, managed by teams of trained engineers and electricians. To deliver it, electric power companies grew into an industry monitored by insurance companies and public utility regulators.

Edison’s first fragile light bulbs were just one early step in the electric revolution that has helped create today’s richly illuminated world.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

Ernest Freeberg does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

In May 2025, the White House proposed reducing the budget of the National Institutes of Health by roughly 40% – from about US$48 billion to $27 billion. Such a move would return NIH funding to levels last seen in 2007. Since NIH budget records began in 1938, NIH has seen only one previous double-digit cut: a 12% reduction in 1952.

Congress is now tasked with finalizing the budget ahead of the new fiscal year, which begins Oct. 1. In July, the Senate rejected the White House’s proposed cuts and instead advanced a modest increase. And in early September, the House of Representatives also supported a budget that maintains the agency’s current funding levels.

However, talk of cutting NIH funding is not a new development. Such proposals tend to resurface from time to time, and the ongoing discussion has created uncertainty about the stability of research overall and prompted concern among scientists about the future of their work.

As researchers studying complex health policy systems – and specifically, science funding policy – we see the NIH as one node in an interconnected system that supports the discovery of new knowledge, trains the biomedical workforce and makes possible medical and public health advances across the U.S.

Our research shows that while cutting NIH funding may appear to save money in the short term, it can trigger a chain of effects that increase long-term health care costs and slow the development of new treatments and public health solutions over time.

Seeing the bigger picture of NIH funding

NIH funding does not just support the work of individual researchers and laboratories. It shapes the foundation of American science and health care by training scientists, supporting preventive health research and creating the knowledge that biomedical companies can later build into new products.

To understand how funding cuts may affect scientific progress, the training of new researchers and the availability of new treatments, we took a broad look at existing evidence. We reviewed studies and data that connect NIH funding, or biomedical research more generally, to outcomes such as innovation, workforce development and public health.

In a study published in July 2025, we built a simple framework to show how changes in one part of the system – research grants, for example – can lead to changes in others, like fewer training opportunities or slower development of new therapies.

Eroding the basic research foundation

The NIH funds early-stage research that lacks immediate commercial value but provides the building blocks for future innovations. This includes projects that map disease pathways, develop new laboratory methods or collect large datasets that researchers use for decades.

For example, NIH-supported research in the 1950s identified cholesterol and its role in disease pathways for heart disease, helping to lay the groundwork for the later discovery of statins used by millions of people to lower cholesterol levels. Cancer biology research in the 1960s led to the discovery of cisplatin, a chemotherapy prescribed to 10% to 20% of cancer patients. Basic research in the 1980s on how the kidneys handle sugar helped pave the way for a new class of drugs for Type 2 diabetes, some of which are also used for weight management. Diabetes affects about 38 million Americans, and obesity affects more than 40% of the adults in the U.S.

Without this kind of public, taxpayer-funded investment, many foundational projects would never begin, because private firms rarely take on work with long timelines or unclear profits. Our study did not estimate dollar amounts, but the evidence we reviewed shows that when public research slows, downstream innovation and economic benefits are also delayed. That can mean fewer new treatments, slower adoption of cost-saving technologies and reduced growth in industries that depend on scientific advances.

Reducing the scientific workforce

By providing grants that support students, postdoctoral researchers and early-career investigators, along with the labs and facilities where they train, the NIH also plays a central role in preparing up-and-coming scientists.

When funding is cut, fewer positions are available and some labs face closure. This can discourage young researchers from entering or staying in the field. The effect extends beyond academic research. Some NIH-trained scientists later move into biotechnology, medical device companies and data science roles. A weaker training system today means fewer skilled professionals across the broader economy tomorrow.

For example, NIH programs have produced not only academic researchers but also engineers and analysts who now work on immune therapies, brain-computer interfaces, diagnostics and AI-driven tools, as well as other technologies in startups and in more established biotech and pharmaceutical companies.

If those training opportunities shrink, biotech and pharmaceutical industries may have less access to talent. A weakened NIH-supported workforce may also risk eroding U.S. global competitiveness, even in the private sector.

Innovation shifts toward narrow markets

Public and private investment serve different purposes. NIH funding often reduces scientific risk by advancing projects to a stage where companies can invest with greater confidence. Past examples include support for imaging physics that led to MRI and PET scans and early materials science research that enabled modern prosthetics.

Our research highlights the fact that when public investment recedes, companies tend to focus on products with clearer near-term returns. That may tilt innovation toward specialty drugs or technologies with high launch prices and away from improvements that serve broader needs, such as more effective use of existing therapies or widely accessible diagnostics.

Some cancer drugs, for instance, relied heavily on NIH-supported basic science discoveries in cell biology and clinical trial design. Independent studies have documented that without this early publicly supported work, development timelines lengthen and costs increase, which can translate into higher prices for patients and health systems. When public funding shrinks and companies shift toward expensive products instead of lower-cost improvements, overall health spending can rise.

What looks like a budget saving in the near term can therefore have the opposite effect, with government programs such as Medicare and Medicaid ultimately shouldering higher costs.

Prevention and public health are sidelined

NIH is also a major funder of research aimed at promoting health and preventing disease. This includes studies on nutrition, chronic diseases, maternal health and environmental exposures such as lead or air pollution.

These projects often improve health long before disease becomes severe, but they rarely attract private investment because their benefits unfold gradually and do not translate into direct profits.

Delaying or canceling prevention research can result in higher costs later, as more people require intensive treatment for conditions that could have been avoided or managed earlier. For example, decades of observation in the Framingham Heart Study shaped treatment guidelines for risk factors such as high blood pressure and heart rhythm disorders. Now this cornerstone of prevention helps to avert heart attacks and strokes, which are far more risky and costly to treat.

A broader shift in direction?

Beyond these specific areas, the larger issue is how the U.S. will choose to support science and medical research going forward. For decades, public investment has enabled researchers to take on difficult questions and conduct decades-long studies. This support has contributed to advances ranging from psychosocial therapies for depression to surgical methods for liver transplants that do not fit neatly into market priorities, unlike drugs or devices.

If government support weakens, medical and health research may become more dependent on commercial markets and philanthropic donors. That can narrow the kinds of problems studied and limit flexibility to respond to urgent needs such as emerging infections or climate-related health risks.

Countries that sustain public investment may also gain an edge by attracting top researchers and setting global standards for new technologies.

On the other hand, once opportunities are lost and talent is dispersed, rebuilding takes far more time and resources.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.