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The Nobel Prize in Economics goes to prosperity researchers

Darren Acemoglu, Simon Johnson and James A. Robinson received this year’s Nobel Prize in Economic Sciences for their contributions to proving the importance of public institutions to a country’s prosperity.

KEY FACTS

  • The prestigious prize, officially known as the Sveriges Riksbank Prize for Economic Sciences in Memory of Alfred Nobel, is the last prize awarded this year and is worth SEK 11 million ($1.1 million).
  • This year’s laureates showed that one of the explanations for differences in countries’ prosperity is the social institutions introduced during European colonization. Inclusive institutions were often introduced in countries that were poor at the time of colonization, which over time led to general prosperity for the population. This is an important reason why former colonies that were once rich are now poor and vice versa.
  • Introducing inclusive institutions would create long-term benefits for everyone, but extractive institutions provide short-term gains for those in power. As long as the political system ensures they retain their control, no one will trust their promises of future economic reforms. According to the laureates, this is the reason why there is no improvement.
  • “Reducing the huge income gaps between countries is one of the greatest challenges of our time. The laureates have demonstrated the importance of public institutions in achieving this,” said Jakob Svensson, Chairman of the Economic Sciences Prize Committee.
  • “Societies with poor rule of law and institutions that exploit the population do not generate growth or change for the better,” the prize’s organizers add on their website.

TANGENT

Darren Acemoglu and Simon Johnson work at MIT, while James Robinson is at the University of Chicago.

Acemoglu and Johnson recently collaborated on a book researching technology through the ages that demonstrates how some technological advances are better at creating jobs and spreading wealth than others.

KEY STORY

The Economics Prize is not one of the original science, literature and peace prizes created by the will of dynamite inventor and businessman Alfred Nobel and first awarded in 1901, but is a later additional prize established and funded by the Central Bank of Sweden in 1968.

Past recipients of the award include a number of influential thinkers such as Milton Friedman, and John Nash – played by actor Russell Crowe in the 2001 film A Beautiful Mind, and former US Federal Reserve Chairman Ben Bernanke.

Last year, Harvard economic historian Claudia Goldin won a prize for her work highlighting the causes of pay and labor market inequality between men and women.

Google’s Gemini Has A Branding Problem As AI Apps Grow More Complicated

Google’s latest Gemini update highlights a broader problem in consumer AI: companies are increasingly turning internal tools and capabilities into separate products that users must learn to navigate.

In its announcement of new Gemini Live voice features, Google said users should not have to determine whether a task requires Spark, Daily Brief or a simple inbox search. Yet those are precisely the distinctions the Gemini app currently asks users to make.

Too Many Features, Too Many Names

Gemini users can switch between Chat, Spark and Daily Brief, each with its own icon and place in the app. Rather than simplifying the experience, the growing list of branded features risks making the underlying technology more visible than it needs to be.

Daily Brief illustrates the problem. Google describes it as a source of personalised, proactive updates based on information from services such as Gmail and Calendar. In practice, however, some of its suggestions can feel less like useful assistance and more like unsolicited reminders about previous searches or unfinished research.

Spark has almost the opposite problem. The feature can act as an AI agent capable of completing tasks on a user’s behalf, but packaging that capability under a separate brand forces users to understand when and where they should use it.

A simpler approach would be to let users describe what they need and allow Gemini to determine whether a standard response, an agent or another capability is appropriate.

Gemini Is Not Alone

Google’s approach reflects a wider trend across the AI industry, where companies increasingly expose the architecture of their products through separate modes and branded features.

Anthropic, for example, asks users to distinguish between Claude’s standard chat experience and Cowork. ChatGPT similarly separates Chat and Work. For consumers, these distinctions can turn what should be a simple interaction into a question about which product or mode to use.

That approach is largely driven by how AI systems are built, rather than by how people naturally think about using them.

Apple Takes A Different Approach

Apple’s strategy for Siri offers a contrasting model. Rather than requiring users to learn a new AI interface, the company is integrating AI capabilities into tools people already use, including Spotlight, Photos, the camera and voice requests.

That approach could prove more effective as AI becomes a mainstream consumer technology. Users do not necessarily need to understand which model, agent or feature is handling a request; they simply need the system to complete the task.

Text-Based AI Offers A Simpler Model

The popularity of text-based AI assistants points in the same direction. Services such as Poke, Ollie, Lindy, Orchid, Lucas, Folk, Tomo and Instinct largely reduce the interaction to a familiar interface: send a message and let the assistant determine what needs to happen next.

That simplicity removes an additional layer of decision-making. Users do not need to choose between Chat, an agent or a specialised feature before asking for help.

As a16z investment partner Justine Moore recently argued, consumers increasingly want an AI assistant to feel like a contact they can message rather than another application they must learn.

For Google and its competitors, the challenge may therefore be less about adding capabilities and more about hiding the complexity behind them. The AI that wins mainstream adoption may ultimately be the one that asks users to understand the least.

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