AI – Richard Brown

A storm coming?

Sir Sadiq Khan was right to highlight the potentially “colossal” impact of artificial intelligence (AI) on London and its economy in his Mansion House speech last week, but “controlling” this still-emerging technology may be a tall order.

As the Mayor argued, the dominance of knowledge economy sectors in the city puts London “at the sharpest edge of change”. Three such sectors – information and communication, finance and insurance, and professional, scientific and technical services – account for 31 per cent of jobs in the capital, almost twice as many as across the UK.

These are the roles that are most exposed to AI. Human-centric, a report written by me and published by University of London in October, argued that generative AI’s ability to “precis, to research, to generate ‘ideas’, to structure arguments and data, and to produce text and images make it a close fit for tasks that are core to knowledge economy roles”. AI boosters and think tanks alike predict that these sectors may be as dramatically shaken up as agriculture and manufacturing were during previous spates of technological change.

However, the impact is hard to discern at the moment. There may have been a fall in graduate recruitment, but the evidence is contested and the impact of AI hard to disentangle from other factors. And corporate adoption of generative AI has been slow. This is partly about accuracy and accountability, but also reflects the ways in which generative AI use is spreading: individual workers are using chatbots on mobile phones and desktops rather than technology introduced through complex, top-down corporate roll-outs.

But it is still early days, only three years since ChatGPT 3.5 was launched, even if it seems longer. And so, while things may feel calm for the moment, there could be a storm coming, with London’s economy directly in its path. The impact on employment could, as the Mayor said, be dramatic.

It bears repeating that generative AI does not simply “take jobs”. The technology can be used to support particular tasks (“augmentation”) or to fully automate those tasks (“substitution”). If this saves time and money, it boosts productivity: more output for the same input.

Productivity gains may be realised by redeploying workers to build more products or serve more customers, or to develop new products and services. Such gains can also be shared with workers in the forms of reduced hours or higher pay. But they can also be cashed in, to return money to shareholders or taxpayers, through “efficiency savings” – that is to say, job losses.

There are ways to slow this down, but they are not necessarily desirable. The US think tank Brookings has proposed a “robot tax” on automation to tip the balance in favour of keeping humans in work (and to create revenues that could support workers who lose out).

Regulations could also be used to slow AI adoption. But keeping humans working on tasks that could be done more efficiently by AI makes productivity gains much harder to achieve and poses a particular threat to cities like London, which export and compete globally. We can throttle back AI adoption here, but will Singapore, New York and Dubai follow suit?

London is actually well-placed to seize the opportunities that come with AI: the city is a world leader for AI investment and innovation, with a highly educated, cosmopolitan population and a bedrock of world-class universities. The city is also a centre for innovation, for creating new products and services, and for the highly personalised and specialised professional services that may be most resistant to automation.

Even if AI adoption in London leads to job losses, history suggests that technological change leads to the creation of as many jobs as it destroys. We have done this before: automation of London stock market transactions – part of the Big Bang of 1986 – took work away from hordes of back-office clerical staff, who previously had to reconcile every trade on paper. These jobs went, but new jobs – in IT, as analysts, in compliance – led to a net growth in financial services employment.

That said, there is a time lag, and the net gain in jobs can obscure the traumatic impact on those people who lose out. The Mayor’s commitment to offer AI training to all Londoners will be valuable. Human-centric argues that universities should play a part too, helping workers to develop the skills they will need to thrive in and shape the new world– part of the much vaunted shift towards lifelong learning.

Universities can also ensure that the next generation of graduates has the resilience and skills to thrive. This is partly a matter of technical skills, but also about knowing how to use a deceptively “easy” resource critically and ethically, putting this in the context of a wider understanding of citizens and society, and nurturing the human skills – of judgement, understanding, collaboration – that employers still see as paramount.

The Mayor’s Mansion House speech touched on a bigger issue too: the “unprecedented concentration of wealth and power” that could result from AI adoption. The risk is that productivity gains from the use of AI flow mainly to big tech companies and their shareholders, rather than to workers and the public at large. This is beyond the reach of city or even national governments, but will become increasingly urgent if AI adoption does create a boom. What good is growth if its fruits flow to a few people in Silicon Valley?

There are ideas out there – from a levy based on how many hours of computational time individuals and firms use, to an endowment that takes a proportion of the value of AI companies launching on the stock markets and uses it endow a “universal basic income” that enables everyone to share in the benefits of AI.

However, adopting any of them will require a level of international cooperation that seems almost impossibly remote in today’s fractured geopolitical climate. Perhaps this is an issue where cities can take the lead, hoping that their national governments will catch up over time.

First published by OnLondon

Remote control – AI and hybrid working

This decade is likely to see the biggest transformation of the workplace since the widespread adoption of the personal computer. Hybrid and remote working patterns adopted during the pandemic appear to be sticking, and a wave of disruption from artificial intelligence (AI) and large language models (LLMs) is following rapidly behind.

London is at the epicentre of these twin “workquakes”. The capital has persistently had the highest levels of home-working in the UK, with two thirds of Londoners saying they worked at home at least one day a week last summer. This reflects hybrid working’s dominance among professional and managerial staff, who make up 63 per cent of London’s resident workers, compared to 50 per cent of all England’s.

These people enjoy the flexibility, work-life balance and personal productivity that working from home can offer, though the impact on organisational or inter-organisational productivity is more contested. Nonetheless, speakers at a London Assembly meeting last week said that the era of “five days a week in the office” had gone for good, and that the task was to adapt central London to new ways of living, working and playing.

The accelerating pace of AI adoption looks likely to add turbulence. A recent UK government report found that workers in London were twice as exposed to AI as the UK average. This was not because of LLMs’ appetite for the diversity and vitality of the capital, but (like the prevalence of home-working) is largely a result of London’s occupational make-up. Unlike previous waves of automation, which affected manufacturing and routine clerical work, AI is coming for the professionals.

The report suggests that the most affected occupations include management consultants, financial managers, psychologists, economists, lawyers, project managers, market researchers, public relations professionals, authors and, perhaps surprisingly, clergy. The “safest” are jobs are those of such as sports professionals, roofers, plasterers, gardeners and car valets. The former occupations are over-represented in London, the latter are not.

However, before soft-handed metropolitan knowledge workers like me rush to retrain, ignoring our lack of aptitude, there are some caveats. The first is that the government report’s projections make no distinction between jobs that are augmented (those where workers can deploy AI to dramatically enhance their productivity), and those that are likely to be substituted (replaced, sooner or later, by new technology).

The second is that the analysis takes no account of the new jobs that will be created. We can see those that are at risk, but it is harder to identify the opportunities that will arise. A year ago, few people had any idea what a “prompt engineer” was. Today, demand for them is booming. And we can be re-assured by historical experience: the majority of jobs that Americans do today did not exist in 1940.

In any case, most professional jobs involve more than one activity, which is where the interaction between working from home and AI gets interesting. A management consultant, for example, may spend time meeting clients, preparing pitches, interviewing workers, analysing data, workshopping ideas and writing reports. A PR professional may write press releases, manage staff, research markets, pitch to clients and journalists, develop concepts, devise guest lists, plan and host events.

Some of these tasks are intrinsically social and best undertaken face-to-face. Others are more easily undertaken remotely, away from distraction and other people. Those in the latter group are also those that can be most easily supported by AI.

From this perspective, AI adoption and hybrid working will complement each other. Hybrid working has already accustomed us to working remotely with less social interaction; AI can provide a sounding board for ideas and be an orchestrator of collaboration, without the hassle and cost of a commute. Similarly, intelligent use of AI can boost productivity, improve co-ordination and reduce the “digital overload” of online meetings, emails and collaboration spaces that built up during lockdown.

But there may be a sting in the tail. Over time, people working remotely with AI support may find themselves edged out by their machine collaborators. Cost-conscious employers are already exploring whether some jobs undertaken remotely might be outsourced internationally. A task that can be completed in Leamington Spa rather than London can also be exported to Lisbon or Kuala Lumpur. Over time, it may also be undertaken by an AI.

Oxford University professors Michael Osborne and Carl-Benedikt Frey, who published a highly influential analysis of the potential impact of automation on the workforce in 2013, recently wrote a (very readable) update reflecting on the explosive growth in AI and how it may affect their original projections.

In 2013, they argued that tasks requiring social intelligence were unlikely to be automated. Now, they write, AI has challenged that “bottleneck” to automation: “If a task can be done remotely, it can also be potentially automated.” However, for sensitive tasks and relationships, face-to-face would retain primacy:

“The simple reason is that in-person interactions remain valuable, and such interactions cannot be readily substituted for: LLMs don’t have bodies. Indeed, in a world where AI excels in the virtual space, the art of performing in-person will be a particularly valuable skill across a host of managerial, professional and customer-facing occupations. People who can make their presence felt in a room, that have the capacity to forge relationships, to motivate, and to convince, are the people that will thrive in the age of AI. If AI writes your love letters, just like everybody else’s, you better do well when you meet on the first date.”

What does this all mean for cities like London? To start with, while we do not know precisely what new jobs will be created by the AI revolution, London is already one of a handful of hotspots for AI start-ups, so it is likely to be the location for many of the new jobs too. The capital is already home to Google Deepmind and many other high growth AI firms, and OpenAI have announced plans for their first international outpost in London.

The combination of AI and hybrid working may ironically strengthen London’s role as one of a few genuine global centres for face-to-face interaction. If remote work is increasingly dispersed or automated and in-person workers with social skills remain in demand, then diverse, globally-accessible, sociable cities such as London will provide the ideal setting for their relationships and collaborations.

There is a bigger picture too. A recent paper by Richard Florida and others talked of the rise of “metacities” based on long-distance networks of collaboration and intermittent commuting. This identified London and New York as the world’s two leading “superstar” hubs, sitting at the heart of networks of talent and interaction. London’s network, as measured by talent flows, includes Manchester, Birmingham, Edinburgh and Bristol, but also Dublin, Paris, Lagos and Bengaluru.

Florida and colleagues argue that the constellation of satellite cities will shift over time, but the importance of superstar cities will persist. This suggests that in coming years London will need to plan for growth in housing, in offices and in new forms of collaborative and social spaces.

The city will also need to be open and welcoming to global talent while helping local workers adapt to change, and to work more closely with its satellite cities to ensure that economic transformation can deliver prosperity and economic growth across the UK.

This is likely to be a turbulent decade for London’s economy, but it could also be one in which the capital’s national and global profile increase.

First published by OnLondon.

Talkin’ about AI Generation

A year after OpenAI’s ChatGPT was launched, we are starting to see the outlines of generative artificial intelligence’s potential impact on our lives.

While the recent Bletchley Park Summit focused on existential risk and misuse by extremists, debate in universities has focused both on technocratic issues (such as automated marking and plagiarism) and more recently on the potential of AI to transform and enhance learning and research – see, for example, the recently published Russell Group principles on the use of generative AI in education.

But there’s a more fundamental question for universities too. How will AI change our economy, and what will this mean for the role played by universities in readying the workforce of the future? The AI Generation: How universities can prepare students for the changing world, a new report for Demos and University of London, draws together what we know about how universities support students’ employability today, and speculates about how this might change as technology advances.

Within our GRASP

The good news is that there is a reasonably strong consensus about the critical employability skills needed today. As computer use and the internet have transformed the knowledge economy, it is not specific technical skills that are most prized. More important are the broader skills of listening to and persuading clients and colleagues, analysing and communicating information to solve problems, and having the ability to manage your own workload, your career and your professional development – the GRASP (general, relational, analytical, social and personal) skills.

Most of these skills are expected to rise in importance in coming years and will remain important in working with AI. Even if generative AI can produce text and images, workers will need critical and ethical judgement to assess what it produces and what it is asked to produce, as well as the relational and social skills to intermediate between technology and humans. So, it appears that the GRASP skillset, with adjustments, will still be relevant.

The less good news is that there is not much evidence on how well universities help students acquire these skills even now, how well these translate into good employability outcomes, or even precisely how they should be defined. Based on a review of academic and policy literature, The AI generation finds that generic “employability” content is unpopular and largely ineffective for students, but material tailored to subject matter and likely career paths much more valued. Active learning approaches – project assignments, collaborative work, peer assessment – seem to work better in developing most employability skills as well as leading to better outcomes, than traditional lecture-based learning.

But it is what happens outside the classroom – work experience, placements, membership of clubs and societies, studying overseas – that has the most impact, whether based on students’ self-assessments, or longitudinal studies looking at graduate employment rates and types. The complication is that these activities are not available to or taken up by all students equally. Students from poorer backgrounds are less likely to participate – partly a matter of cost, but also a matter of feeling, or being made to feel, that you don’t belong. In this way, there is a risk that the very activities that best boost employability are least accessible to those who need them most.

Murky paths

Growing use of AI may intensify some of these risks. At the moment, a degree still acts as a minimum entry requirement for many personally and financially fulfilling careers. Early studies suggest that AI can particularly help less competent workers achieve better outcomes in standardised professional services tests, suggesting that degree holders may need to do even more to stand out from the crowd than they do at the moment, and that lower-level cognitive work may be automated first and fastest.

This also makes the pathway into graduate careers less clear. Currently students who want to pursue careers in most professions have a clear path ahead of them, with employers keen to diversify traineeship schemes. But AI may quickly automate some of the basic trainee tasks – preparing pitch decks and presentations, summarising arguments, working up architectural details, researching legal precedent – calling into question our whole model of professional development.

These are big societal issues, and universities cannot solve them alone. The AI generation recommends research and analysis of what works best in developing employability skills and a more systematic and fair approach to career-boosting activity such as work placements. But universities can also take the lead as civic institutions, convening government, employers, professional bodies and civil society organisations, to consider what AI will mean for our working lives, and what education and training future generations will need in order to thrive.

First published by WonkHE.