Digital Transformation

Negotiating wicked problems with the power of AI

Cristobal Garcia Herrera — Wed, 22 Jul 2026 20:47:54 +0000

Negotiating wicked problems with the power of AI

Cristobal Garc… Wed, 22/07/2026 - 20:47

23 July 2026

Wicked problems, such as climate breakdown, geopolitical fragmentation and demographic transformation, do not yield to single-sector solutions. Demanding new coalitions, new models, and new levels of ambition, this annual symposium exists to address exactly that.

This year, attendees discovered that while wicked problems are AI-resistant, the technology could still play a part in accelerating their resolution.

The event, organised by Wicked Labs, a collaboration between 51�Թ�� Business School and the Royal College of Art, was made up of five sessions:

Geopolitics, wars and new policy approaches;
The pace towards net-zero society;
Corporate venturing and AI;
The future of education, industries and governments; and
Industrial futures

Through these, attendees were exposed to four key practical insights:

Look at past tech revolutions to envision emerging futures;
Embrace the AI revolution and run continuous experiments in education, companies and governments;
Foster learning ecosystems to leverage collective intelligence; and
Train curious, emphatic and resilient leaders

A unique forum for addressing how organisations can adapt in the face of disruptions, such as AI, the Wicked Symposium has also become a community of people who refuse to accept that the world's hardest problems are unsolvable.

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The sixth Wicked Symposium gathered policymakers, investors, scholars, corporate innovation managers and experts to explore what happens to wicked problems when AI enters the room.

Fergus Burnett/51�Թ�� Business School

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Book launch on Tencent and its rise to being a global tech giant

Lou Lee — Mon, 14 Apr 2025 10:15:24 +0000

Book launch on Tencent and its rise to being a global tech giant

Lou Lee Mon, 14/04/2025 - 10:15

29 July 2026 News

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23 July 2026 News

Negotiating wicked problems with the power of AI

1 July 2026

To mark the publication of their new book, China's 'Everything Online' Company: The Unique Innovation Model of Tencent, Professor George Yip, Emeritus Professor of Marketing & Strategy at 51�Թ�� Business School, and co-author Professor Xiaolan Fu, Professor of Technology and International Development at the University of Oxford, shared insights from their research into one of the world's leading technology companies.

Introduced by Lord Clement-Jones, Liberal Democrat House of Lords spokesperson for Science, Innovation and Technology, the event traced Tencent's evolution from its early instant messaging platform QQ to the creation of WeChat and its vast digital ecosystem spanning social media, gaming, payments, cloud computing and AI.

Drawing on years of research, Professor Yip and Professor Fu explained that Tencent's success stems not only from technological excellence, but also from the way it encourages new ideas to emerge. Their book introduces the company's distinctive OCEAN framework: Openness, Coopetition, Empowerment, Autonomy and Attentiveness as a way of managing stakeholder needs. Together, these principles help Tencent operate more like a connected ecosystem than a traditional corporate hierarchy, encouraging experimentation while allowing new ideas to scale quickly.

“I hope people left with fresh insights into how Tencent combines technology, organisation and ecosystem thinking to create innovation at scale, and why those lessons matter well beyond China.”

Professor George Yip

Emeritus Professor of Marketing & Strategy

Reflecting on the event, Professor Yip said: "The enthusiasm and engagement from the audience showed just how much interest there is in understanding one of the world's most innovative companies. I hope people left with fresh insights into how Tencent combines technology, organisation and ecosystem thinking to create innovation at scale, and why those lessons matter well beyond China, even to the UK's next Prime Minister."

The evening concluded with a lively audience Q&A exploring the future of innovation, digital ecosystems and China's rapidly evolving technology landscape.

Professor Yip and Professor Fu's book offers fresh insights for business leaders, policymakers, researchers and students interested in innovation, digital platforms and the future of technology.

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51�Թ�� Business School welcomed business leaders, academics and alumni for the launch of a new book offering a rare insight into how Chinese technology giant Tencent became one of the world's most innovative companies.

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Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek

Evie Burrows-Taylor — Mon, 03 Feb 2025 09:58:57 +0000

Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek Evie Burrows-Taylor Mon, 03/02/2025 - 09:58

28 January 2025

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ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market

Evie Burrows-Taylor — Mon, 03 Feb 2025 09:49:09 +0000

ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market Evie Burrows-Taylor Mon, 03/02/2025 - 09:49

27 January 2025

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The Times features 51�Թ��’s new Venture Capital Fellowship

Evie Burrows-Taylor — Mon, 27 Jan 2025 12:05:17 +0000

The Times features 51�Թ��’s new Venture Capital Fellowship Evie Burrows-Taylor Mon, 27/01/2025 - 12:05

22 January 2025

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Why making AI too easy to use is a risk for business

Chengwei Liu — Wed, 15 Jan 2025 12:14:02 +0000

Why making AI too easy to use is a risk for business

Chengwei Liu Wed, 15/01/2025 - 12:14

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Leaders should design AI workflows to preserve the human ability to evaluate, challenge and build on AI-generated outputs, new research reveals
Deliberate friction, requiring human input and thinking, can help avoid an AI-driven degradation in critical thinking within organisations.

As the saying goes, “necessity is the mother of invention”: a lack of easy solutions forces us to come up with innovative ways to tackle complex problems. Artificial intelligence (AI) offers an alternative approach, providing instant solutions with little human effort required. While this can certainly increase productivity, it may also reduce “necessity” in the sense of needing to innovate, think creatively and come up with novel approaches to get things done.

Is this a problem? Perhaps not in the short term, as less work means lower costs and more resource capacity for businesses. But in the long term, it may pose an existential threat by atrophying the critical faculties we need to produce new thinking and develop innovative solutions. We call this “the productivity trap”.

Our research looks at this from an organisational perspective. It shows that when “good enough” solutions become free for anyone in the organisation to access, facilitated by AI, the rate of reuse goes up, while the rate of independent exploration and experimentation goes down. Over time, this sees teams increasingly converge on a limited number of approaches.

Avoiding the productivity trap

This creates a significant tension for business leaders: how to balance the cost and efficiency benefits of AI with the erosion of creative thinking among individuals and teams.

One option is to simply limit or slow AI integration, but that comes with clear opportunity costs in short-term savings and long-term efficiency gains. At the same time, ignoring the dangers of the productivity trap could see an organisation lose its USP and cutting edge in the eyes of the market, or be caught off guard by an unexpected challenge.

The solution lies in what we call “strategic friction” – adding a small hurdle to individual use of AI in order to raise the total level of knowledge at the organisational level. This works because when people are required to invest some effort in order to use ready-made findings, they naturally learn and produce new knowledge for others to build on.

As a result, more distinct approaches survive, fewer individuals give up on exploration and experimentation, and the organisation is less at risk of getting stuck with a limited range of solutions. This relies on a mechanism known as “absorptive capacity”, which is the ability to build expertise by stacking new learning onto existing knowledge. This helps people to grow understanding, allowing them to evaluate, adapt and improve ideas rather than just copying them.

"When people are required to invest some effort in order to use ready-made findings, they naturally learn and produce new knowledge for others to build on."

Friction with purpose

The key to using strategic friction is not to create bureaucratic hurdles for the sake of it. Instead, it’s about keeping enough friction in the system so that people need to understand ideas before reusing them, helping them to continue to learn and develop rather than stagnate.

Leaders should calibrate this carefully; too little friction can limit innovation, while too much might slow everything down. Equally, not every task requires friction; routine, simple tasks where speed is the goal will benefit less than those where the organisation needs staff to think, learn and develop.

At the organisational level, this means moving from a model where users ask AI questions and receive simple solutions, to one where users have to contribute their own understanding to receive useful outputs. To achieve this, leaders could consider requiring staff to submit documented independent work before being allowed to use AI for a task.

At the system level, AI platforms can be customised to ask for a user-generated input before providing an output. For example, an analyst asking AI for a risk assessment could first be required by the interface to upload their own hypothesis and list of variables. The AI would then build on this human input in a reciprocal, conversational process that sees the two parties collaborating, rather than the user simply delegating work to the AI.

Getting the right staff mindset in place can help too. Traits to look out for include an unwillingness to accept or rely on AI-generated work at face value. Instead, workers should take the time to see what’s missing, test the assumptions and adapt it to fit the context. This requires the ability to spot errors, omissions and incorrect assumptions in AI outputs, as well as the ability to explain why these things are problems.

Building a workforce with these skills may see a shift from targeting those who can give the right answers to those who can identify the right processes. Leaders should be looking for “builders” rather than “free-riders”, choosing those who instinctively understand the limits of AI capability, question its outputs, and provide their own inputs to ensure the results are useful.

Discover more insights by Dr Chengwei Liu

Read 'Using AI Can Stifle Innovation. But It Doesn’t Have To.'

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AI can boost efficiency at work, but it may also weaken the experimentation and critical thinking that drive innovation. Leaders should introduce strategic friction to help preserve judgement, learning and creativity.

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Science’s biggest AI challenge isn’t discovery – it’s proof

Ryan Cory-Wright — Wed, 15 Jan 2025 12:14:02 +0000

Science’s biggest AI challenge isn’t discovery – it’s proof

Ryan Cory-Wright Wed, 15/01/2025 - 12:14

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AI can now generate scientific hypotheses faster than traditional research systems can test them
Scientific progress still depends on verification: testing whether promising claims hold up against evidence, theory and domain expertise
AI-driven science will deliver on its promise only if we can test ideas as quickly and rigorously as we generate them

Scientific breakthroughs look sudden only in retrospect: a new theory, a medical advance, a technology that changes how we see the world. But before an idea becomes a discovery, it must survive a slower and less glamorous process of verification.

AI-driven science needs scalable verification methods. This is because AI systems can analyse vast amounts of data, identify patterns and generate new hypotheses faster than ever before. But verifying those hypotheses remains slow, costly, and highly domain-specific.

Ideas need proof

Science begins with a hypothesis: a claim precise enough to be tested, challenged and potentially proven wrong. Verification asks whether that hypothesis survives contact with what we already know by testing it against against theory, evidence, experiments, simulations, or formal reasoning. It is this process that separates genuine discoveries from ideas that merely appear plausible.

Scientific advances such as germ theory, thermodynamics and modern agricultural practices transformed society not simply because scientists proposed new ideas, but because those ideas were rigorously tested and repeatedly validated. Verification, then, is the dividing line between a promising idea and a discovery that science can trust.

The age of abundant ideas

For centuries, generating new scientific ideas was one of the most time-consuming parts of research. Over the past decades, major discoveries have become harder to achieve as scientific problems have grown more complex and interconnected.

This is what makes AI so exciting for science: it changes the speed at which researchers can move from data to possible explanations.

Machine learning systems can scan vast datasets for patterns that humans might miss, while large language models (LLMs) can help connect those patterns to possible explanations, making hypothesis generation dramatically faster.

That changes the economics of discovery. When machines can produce candidate explanations faster than researchers can test them, the bottleneck shifts from generating ideas to verifying and filtering them.

Science’s new bottleneck

In many areas of science, the pace of hypothesis generation has historically been constrained by human expertise, data collection and theory-building. AI weakens that constraint: it can propose many more candidates than existing verification systems, such as peer review, can comfortably absorb.

While the number of hypotheses has increased dramatically, verification remains comparatively slow, expensive and often dependent on human expertise. Laboratory experiments, clinical trials and peer review cannot easily be scaled at the same rate as AI-generated ideas. The result is a growing verification bottleneck.

“Generating ideas is no longer the primary constraint. Determining which ideas stand up to scrutiny is becoming the harder problem.”

Without effective verification, scientific progress risks becoming overwhelmed by plausible yet untested results. Some hypotheses may fit existing data while failing to hold up under further scrutiny. Others may appear convincing but remain disconnected from established scientific knowledge.

In other words, generating ideas is no longer the primary constraint. Determining which ideas stand up to scrutiny is becoming the harder problem.

History shows the cost of inadequate verification. Scientific claims that initially appeared convincing have later been overturned because measurements were flawed, assumptions proved incorrect, or results could not be replicated. Without rigorous verification, minor errors can compound, leading to years of wasted effort and resources.

Verification isn’t one-size-fits-all

Verification is not the same in every scientific field. In physics, it often relies on mathematical consistency and reproducible experiments. In biology, it may require laboratory testing and observation. In medicine, verification can involve clinical trials and statistical evidence.

There is no universal framework that can verify every scientific claim. What unites these disciplines is the principle that scientific knowledge advances only when ideas are tested against evidence. Although the methods differ, verification remains fundamentally driven by structured, iterative reasoning.

Trust is the new competitive advantage

AI is changing scientific discovery and may ultimately require us to rethink aspects of the traditional scientific method.

For much of human history, ideas were scarce and difficult to generate. Today, AI is making ideas abundant. The scarce resource is increasingly becoming the ability to determine which discoveries are robust enough to trust, invest in and build upon.

“In an age of abundant AI-generated ideas, trust becomes the scarce resource, and verification will be how science and business earn it.”

As AI continues to accelerate scientific discovery, the greatest opportunities may lie not in generating ever more hypotheses, but in developing better ways to verify them. That means building verification into AI-driven science itself through:

Automated checks wherever possible
Stronger links to established theory
Better benchmarks
Human oversight where scientific judgement matters

For businesses, universities and investors, the main lesson is not that AI makes scientists less important. It is that AI makes scientific judgement more valuable. As machines make it cheaper to generate hypotheses, the competitive advantage will shift to organisations that can test claims quickly, rigorously and carefully before building products, making investments or changing practice. In an age of abundant AI-generated ideas, trust becomes the scarce resource, and verification will be how science and business earn it.

Discover insights by Dr Cory-Wright

Read 'The need for verification in artificial intelligence-driven scientific discovery'.

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AI is making scientific ideas abundant. The hard part is knowing which ones to trust.

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Will European regulators shape Hollywood’s bidding war?

Tommaso Valletti — Wed, 15 Jan 2025 12:14:02 +0000

Will European regulators shape Hollywood’s bidding war?

Tommaso Valletti Wed, 15/01/2025 - 12:14

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The European Commission (EC) competition authority is scrutinising rival bids for Warner Bros. from Netflix and Paramount, an unusual scenario
Both bids raise different competition concerns
A frontrunner could emerge if one bidder gets cleared before the other
The EC prefers light-touch remedies
US politics, not Europe, is likely to decide the winner

A bidding saga across the Atlantic

Europe’s competition regulator is simultaneously reviewing competing bids by major US companies Netflix and Paramount to acquire entertainment company Warner Bros. This is unusual and strategically sensitive, given the current geopolitical climate. The bidding saga has attracted significant attention on both sides of the Atlantic.

Both bids raise competition concerns, but in different ways:

The Paramount deal would reduce rivalry between two major Hollywood studios.
Netflix’s acquisition of Warner Bros. would increase market dominance in streaming across Europe

Professor Tommaso Valletti, Professor of Economics at 51�Թ�� Business School and former Chief Competition Economist at the EC, questions whether the EC will act boldly, given its preference for light-touch remedies and geopolitical considerations. US politics, not Europe’s regulators, may ultimately decide the winner.

What’s at stake

Netflix agreed to buy Warner Bros.’ studios and streaming assets in a transaction valued around $82.7 billion. To challenge this, rival Paramount Skydance launched a hostile takeover bid for the entire company, including news network CNN, worth $108 billion, offering shareholders a higher per-share cash price.

The Warner Bros. board backs Netflix, but competition concerns are significant and more complex than the ones associated with Paramount’s bid.

A lack of clear precedent in the EU merger control raises a key question: how will the EC proceed?

Because all three companies operate across Europe and the Americas, the European Commission must review the market implications under its EU merger control framework. A simultaneous review could shape the race. By clearing one bidder, while subjecting the other to a longer investigation, a frontrunner could emerge.

A lack of clear precedent in EU merger control raises a key question: how will the EC proceed? Historically, the regulator tends to favour light-touch remedies, like small divestitures or access commitments, over prohibitions.

Netflix: from scale to market power

Netflix’s story of scale in Europe has shifted to revenue diversification. Netflix already shows signs of pricing power: in the UK, a standard subscription rose from £6 to £13 per month over the past decade. A deal with Warner Bros. would lead to:

Further market concentration: Netflix already holds over 50 percent of Europe’s subscription video-on-demand (SVOD) market. Combining this with Warner Bros.’ studios, HBO Max and other streaming platforms would increase its dominance.
Monopolisation of streaming content: Netflix could control one of the largest content libraries and production pipelines in the world, cutting off production for cinemas and other traditional content platforms.

Netflix could address these concerns through remedies such as divesting HBO Max. Its likely defence would be that its streaming services compete with a broader digital entertainment market, including social media and video gaming. Regulators may accept this argument to some extent, although previous merger reviews have generally used a narrower, SVOD-focused market definition.

Paramount: traditional Hollywood rivalry

Paramount’s acquisition of Warner Bros. raises more traditional competition concerns. Combining two established major Hollywood studios would:

reduce industry rivalry and lead to fewer big players in Hollywood,
increase bargaining power over distributors, advertisers and creators, and
potentially lead to fewer market players, less innovation, higher costs for consumers.

The deal would also continue the consolidation of Hollywood studios that has been ongoing for 50 years, reducing the “Big Five” studios (Disney, Warner Bros., Universal, Paramount and Sony) to four. Disney would remain number one, possibly facing stronger competition from the combined entities currently ranked number two (Warner Bros.) and number five (Paramount).

While the merger could be easier to clear overall, overlaps between the studios, for example franchises between Paramount and Warner Bros., could still raise competition concerns that may be harder to address.

Why Europe and the US may decide differently

Whether the EC’s decision will impact the deal remains uncertain. In the US, regulators face a different and more political context. Paramount’s bid would include CNN, which Netflix is not acquiring. President Trump, a vocal critic of CNN, has previously argued that the network should be sold or restructured. These dynamics may influence the outcome.

Given the current political climate, a European ban on either deal seems unlikely. While the EC can still influence the pace and outcome of the bidding war, the winner will ultimately be decided in the US.

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A rare regulatory scenario could see Brussels tilt the Netflix - Paramount battle for Warner Bros.

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When winning comes at a price: The business of pay-to-win add-ons

Mushegh Harutyunyan — Wed, 15 Jan 2025 12:14:02 +0000

When winning comes at a price: The business of pay-to-win add-ons

Mushegh Harutyunyan Wed, 15/01/2025 - 12:14

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The video game industry is booming. According to Statista, more than three billion people played video games worldwide in 2024. As per PwC’s Global Entertainment and Media Outlook 2022-26, the gaming industry is tipped to maintain its recent rapid growth, and could be worth $321 billion by 2026. A big slice of that — $82 billion in 2024 — came from in-game purchases such as cosmetic “skins”, virtual currency, map packs, and more. In the UK, a recent Censuswide poll revealed that gamers, on average, spend £540 a year on in-game purchases (£22 a month) and subscriptions (£23 a month).

One of the most debated forms of in-game spending is the pay-to-win add-on, a digital item that gives players a clear advantage in player-versus-player games. A special weapon in Call of Duty: Warzone, a premium camouflage in Escape from Tarkov, or unique cards in Hearthstone can tilt the odds decisively in favor of paying players. For the buyer, these items boost the chance of winning; for everyone else, they create a frustrating disadvantage. One gamer described their game experience bluntly: “It was absurd and halfway to unplayable if you didn’t drain your wallet.”

The developer’s dilemma

At first glance, offering P2W add-ons seems like easy money. They cost little to produce and appeal directly to players’ powerful motivations: the desire to win and the dread of losing. Yet our research shows that the business case is more nuanced.

Unlike traditional add-ons (like hotel WiFi or an extended warranty), the value of P2W items is shaped by network effects. Their appeal grows when few players own them, because they confer an advantage over many others. But as adoption of P2W items spreads, their edge diminishes.

Players who do not spend on P2W may abandon the game entirely as they face a daunting competitive disadvantage against the large number of P2W owners. The fewer non-buyers remain, the smaller is the pool of players against whom P2W will bring an advantage, further eroding the add-on’s value. This negative network effect created by the P2W add-ons calls for a careful assessment of market conditions before introducing them. We find that ironically, when a large share of players is willing to buy, introducing P2W can reduce profits, leaving developers better off without it.

The psychology of competition makes this balance even trickier. Players who are highly motivated by winning make the add-on more lucrative: the thrill of victory increases their willingness to pay. But the story is different with loss sensitivity. If defeats sting only mildly, add-ons hold little attraction. If they sting too much, non-buyers disengage altogether. Developers can influence these sensitivities through design choices — making victories more rewarding while keeping defeats painful but not unbearable.

Design and pricing

One might assume that the stronger the add-on, the better for profits. Yet our findings suggest otherwise. Making the add-on too powerful discourages non-buyers from playing at all, shrinking the competitive field and eroding the add-on’s value. In highly competitive genres, developers may actually benefit from deliberately limiting the power of pay-to-win items, ensuring the game remains engaging for all.

The introduction of P2W items forces developers to lower the base game price to keep P2W-non-buyers in the fold, compensating their disadvantage against P2W-buyers. In some cases, this means shifting to a freemium model, or even — in theory — setting a negative price, paying players to participate because their presence sustains the market for add-ons. And while lower prices might seem to discourage investment in quality, the opposite can happen: when losses feel especially painful for non-buyers, developers are pushed to raise quality to keep them engaged.

Perhaps most surprising is what this means for players themselves. P2W is often accused of undermining fairness and reducing enjoyment. Yet under the right conditions, our analysis shows it can actually increase player surplus. By broadening the player base and motivating higher investment in quality, the system can leave players better off than in a world without P2W.

Keeping the pond healthy

Players are often categorised into "whales" (big spenders), "dolphins" (moderate spenders) and "minnows" (low or non-spenders). For developers, pay-to-win add-ons are a tempting way to extract more profits from the less price-sensitive whales and dolphins. Yet their very success depends on the participation of the more price-sensitive minnows. These players may never spend much, but their presence ensures that competitive advantages remain meaningful for others. If minnows abandon the game, the pond dries up — and the value of P2W erodes with it.

The lesson is clear: introducing P2W is not simply about monetizing the biggest spenders. It is about keeping the entire ecosystem in balance. Developers who want sustainable profits must design their games so that whales, dolphins, and minnows can all swim in the same pond — even if not on equal terms.

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Pay-to-win (P2W) add-ons are controversial in the gaming industry. New research by Mushegh Harutyunyan and Esma Koca reveals when a game developer should introduce a P2W add-on and how to design and price it.

Main image: Milan_Jovic / iStock via Getty Images Plus

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Negotiating wicked problems with the power of AI

Negotiating wicked problems with the power of AI

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The sixth Wicked Symposium gathered policymakers, investors, scholars, corporate innovation managers and experts to explore what happens to wicked problems when AI enters the room.

Book launch on Tencent and its rise to being a global tech giant

Book launch on Tencent and its rise to being a global tech giant

51�Թ��� launches UK’s first master’s degree focused on AI’s economic and policy impact

Could food taxation in India pave the way to better health?

Negotiating wicked problems with the power of AI

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51�Թ��� Business School welcomed business leaders, academics and alumni for the launch of a new book offering a rare insight into how Chinese technology giant Tencent became one of the world's most innovative companies.

Listen: The Standard’s Tech and Science Daily podcast interviews Dr Mark Kennedy about Chinese AI company DeepSeek

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ITV News interviews David Shrier about DeepSeek’s emerging dominance in the technology market

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The Times features 51�Թ���’s new Venture Capital Fellowship

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Why making AI too easy to use is a risk for business

Why making AI too easy to use is a risk for business

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Why making AI too easy to use is a risk for business

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

Article at a glance

Leaders should design AI workflows to preserve the human ability to evaluate, challenge and build on AI-generated outputs, new research reveals

Deliberate friction, requiring human input and thinking, can help avoid an AI-driven degradation in critical thinking within organisations.

Avoiding the productivity trap

Friction with purpose

Discover more insights by Dr Chengwei Liu

Share

AI can boost efficiency at work, but it may also weaken the experimentation and critical thinking that drive innovation. Leaders should introduce strategic friction to help preserve judgement, learning and creativity.

Science’s biggest AI challenge isn’t discovery – it’s proof

Science’s biggest AI challenge isn’t discovery – it’s proof

More blogs

Why making AI too easy to use is a risk for business

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

Article at a glance

AI can now generate scientific hypotheses faster than traditional research systems can test them

Scientific progress still depends on verification: testing whether promising claims hold up against evidence, theory and domain expertise

AI-driven science will deliver on its promise only if we can test ideas as quickly and rigorously as we generate them

Ideas need proof

The age of abundant ideas

Science’s new bottleneck

Verification isn’t one-size-fits-all

Trust is the new competitive advantage

Discover insights by Dr Cory-Wright

Share

AI is making scientific ideas abundant. The hard part is knowing which ones to trust.

Will European regulators shape Hollywood’s bidding war?

Will European regulators shape Hollywood’s bidding war?

More blogs

Why making AI too easy to use is a risk for business

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

Article at a glance

The European Commission (EC) competition authority is scrutinising rival bids for Warner Bros. from Netflix and Paramount, an unusual scenario

Both bids raise different competition concerns

A frontrunner could emerge if one bidder gets cleared before the other

The EC prefers light-touch remedies

US politics, not Europe, is likely to decide the winner

A bidding saga across the Atlantic

What’s at stake

Netflix: from scale to market power

Paramount: traditional Hollywood rivalry

Why Europe and the US may decide differently

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A rare regulatory scenario could see Brussels tilt the Netflix - Paramount battle for Warner Bros.

Top 10 Business School articles that you should read before the year ends

Top 10 Business School articles that you should read before the year ends

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Why making AI too easy to use is a risk for business

Science’s biggest AI challenge isn’t discovery – it’s proof

How to survive as an entrepreneur: insights from Steve Blank

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From sustainable consumption to innovation in the biopharma sector, here are 10 of the most-read IB Knowledge think pieces that shaped our understanding of globally relevant research from 51�Թ��� this year

51�Թ�� launches UK’s first master’s degree focused on AI’s economic and policy impact

51�Թ�� Business School welcomed business leaders, academics and alumni for the launch of a new book offering a rare insight into how Chinese technology giant Tencent became one of the world's most innovative companies.

The Times features 51�Թ��’s new Venture Capital Fellowship

Explore articles by 51�Թ�� Business School's academics

From sustainable consumption to innovation in the biopharma sector, here are 10 of the most-read IB Knowledge think pieces that shaped our understanding of globally relevant research from 51�Թ�� this year