Many years ago, long before the internet or artificial intelligence, an American engineer called Vannevar Bush was trying to solve a problem. He could see how difficult it had become for professionals to research anything, and saw the potential for a better way.

This was in the 1940s, when anyone looking for articles, books or other scientific records had to go to a library and search through an index. This meant drawers upon drawers filled with index cards, typically sorted by author, title or subject.

When you had found what you were looking for, creating copies or excerpts was a tedious, manual task. You would have to be very organised in keeping your own records. And woe betide anyone who was working across more than one discipline. Since every book could physically only be in one place, they all had to be filed solely under a primary subject. So an article on cave art couldn’t be in both art and archaeology, and researchers would often waste extra time trying to find the right location.

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This had always been a challenge, but an explosion in research publications in that era had made it far worse than before. As Bush wrote in an influential essay, As We May Think, in The Atlantic in July 1945:

There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialisation extends. The investigator is staggered by the findings and conclusions of thousands of other workers – conclusions which he cannot find time to grasp, much less to remember, as they appear.

Bush was dean of the school of engineering at MIT (the Massachusetts Institute of Technology) and president of the Carnegie Institute. During the second world war, he had been the director of the Office of Scientific Research and Development, coordinating the activities of some 6,000 scientists working relentlessly to give their country a technological advantage. He could see that science was being drastically slowed down by the research process, and proposed a solution that he called the “memex”.

The memex was to be a personal device built into a desk that required little physical space. It would rely heavily on microfilm for data storage, a new technology at the time. The memex would use this to store large numbers of documents in a greatly compressed format that could be projected onto translucent screens.

Most importantly, Bush’s memex was to include a form of associative indexing for tying two items together. The user would be able to use a keyboard to click on a code number alongside a document to jump to an associated document or view them simultaneously – without needing to sift through an index.

Bush acknowledged in his essay that this kind of keyboard click-through wasn’t yet technologically feasible. Yet he believed it would be soon, pointing to existing systems for handling data such as punched cards as potential forerunners.

He envisaged that a user would create the connections between items as they developed their personal research library, creating chains of microfilm frames in which the same document or extract could be part of multiple trails at the same time.

New additions could be inserted either by photographing them on to microfilm or by purchasing a microfilm of an existing document. Indeed, a user would be able to augment their memex with vast reference texts. “New forms of encyclopedias will appear,” said Bush, “ready-made with a mesh of associative trails running through them, ready to be dropped into the memex”. Fascinatingly, this isn’t far from today’s Wikipedia.

Where it led

Bush thought the memex would help researchers to think in a more natural, associative way that would be reflected in their records. He is thought to have inspired the American inventors Ted Nelson and Douglas Engelbart, who in the 1960s independently developed hypertext systems, in which documents contained hyperlinks that could directly access other documents. These became the foundation of the world wide web as we know it.

Beyond the practicalities of having easy access to so much information, Bush believed that the added value in the memex lay in making it easier for users to manipulate ideas and spark new ones. His essay drew a distinction between repetitive and creative thought, and foresaw that there would soon be new “powerful mechanical aids” to help with the repetitive variety.

He was perhaps mostly thinking about mathematics, but he left the door open to other thought processes. And 80 years later, with AI in our pockets, we’re automating far more thinking than was ever possible with a calculator.

If this sounds like a happy ending, Bush did not sound overly optimistic when he revisited his own vision in his 1970 book Pieces of the Action. In the intervening 25 years, he had witnessed technological advances in areas like computing that were bringing the memex closer to reality.

Yet Bush felt that the technology had largely missed the philosophical intent of his vision – to enhance human reasoning and creativity:

In 1945, I dreamed of machines that would think with us. Now, I see machines that think for us – or worse, control us.

Bush would die just four years later at the age of 84, but these concerns still feel strikingly relevant today. While it’s great that we do not need to search for a book by flipping through index cards in chests of drawers, we might feel more uneasy about machines doing most of the thinking for us.

Is this technology enhancing and sharpening our skills, or is it making us lazy? No doubt everyone is different, but the danger is that whatever skills we leave to the machines, we eventually lose, and younger generations may not even get the opportunity to learn them in the first place.

The lesson from As We May Think is that a purely technical solution like the memex is not enough. Technology still needs to be human-centred, underpinned by a philosophical vision. As we contemplate a great automation in human thinking in the years ahead, the challenge is to somehow protect our creativity and reasoning at the same time.

BEIJING (Reuters) -Chinese artificial intelligence startup Moonshot AI released a new open-source AI model on Friday, joining a wave of similar releases from local rivals, as it seeks to reclaim its position in the competitive domestic market.

The model, called Kimi K2, features enhanced coding capabilities and excels at general agent tasks and tool integration, allowing it to break down complex tasks more effectively, the company said in a statement.

Moonshot claimed the model outperforms mainstream open-source models in some areas, including DeepSeek’s V3, and rival capabilities of leading U.S. models such as those from Anthropic in certain functions such as coding.

The release follows a trend among Chinese companies toward open-sourcing AI models, contrasting with many U.S. tech giants like OpenAI and Google that keep their most advanced AI models proprietary. Some American firms, including Meta Platforms, have also released open-source models.

Open-sourcing allows developers to showcase their technological capabilities and expand developer communities as well as their global influence, a strategy likely to help China counter U.S. efforts to limit Beijing’s tech progress.

Other Chinese companies that have released open-source models include DeepSeek, Alibaba, Tencent and Baidu.

Founded in 2023 by Tsinghua University graduate Yang Zhilin, Moonshot is among China’s prominent AI startups and is backed by internet giants including Alibaba.

The company gained prominence in 2024 when users flocked to its platform for its long-text analysis capabilities and AI search functions.

However, its standing has declined this year following DeepSeek’s release of low-cost models, including the R1 model launched in January that disrupted the global AI industry.

Moonshot’s Kimi application ranked third in monthly active users last August but dropped to seventh place by June, according to aicpb.com, a Chinese website that tracks AI products.

(Reporting by Liam Mo and Brenda Goh, Editing by Louise Heavens)

Despite its traditionally risk-averse nature, the insurance industry is being fundamentally reshaped by AI.

AI has already become vital for the insurance industry, touching everything from complex risk calculations to the way insurers talk to their customers. However, while nearly eight out of ten companies are dipping their toes in the AI water, a similar number admit it hasn’t actually made them any more money.

Such figures reveal a simple truth: just buying the fancy new tech isn’t enough. The real winners will be the ones who figure out how to weave it into the very fabric of who they are and everything they do.

You can see the most dramatic changes right at the heart of the business: handling claims. That mountain of paperwork and endless phone calls, a process that could drag on for weeks, is finally being bulldozed by AI.

A deployment by New York-based insurer Lemonade back in 2021 resulted in settling over a third of its claims in just three seconds, with no human input. Or look at a major US travel insurer that handles 400,000 claims a year; it went from a completely manual system to one that was 57% automated, cutting down processing times from weeks to just minutes.

However, this isn’t just about moving faster; it’s about getting it right. AI can slash the kind of costly human errors that lead to claims leakage in the insurance industry by as much as 30%. The knock-on effect is a huge productivity leap, with adjusters able to handle 40-50% more cases. This frees up the real experts to stop being paper-pushers and start focusing on the tricky cases where a human touch and genuine empathy make all the difference.

It’s a similar story for the underwriters, the people who calculate the risks. AI is giving them superpowers, letting them analyse colossal amounts of data from all sorts of places – like telematics or credit scores – that a person could never sift through alone. It can even draft an initial risk report with incredible accuracy by looking at past data and policies in the blink of an eye.

In practice, this helps create pricing that is fairer and more accurately reflects a person’s unique situation. Zurich, for example, used a modern platform to build a risk management tool that made their assessments 90% more accurate.

Suddenly, underwriting isn’t about looking in the rearview mirror anymore—it’s a living, breathing process that can adapt on the fly to new, complex threats like cyberattacks or the effects of climate change.

But this isn’t just about back-office wizardry. When deployed in the insurance industry, AI is completely changing the conversation between insurers and the people they serve. It’s allowing a move away from simply reacting to problems to proactively helping customers.

AI chatbots can offer 24/7 support, getting smarter with every question they answer. This lets the human team focus on the more difficult conversations. The real game-changer, though, is making things personal. 

By understanding a customer’s policy and behaviour, AI can gently nudge them with a renewal reminder or suggest a product that actually fits their life, like usage-based car insurance. It’s about showing customers you actually get them, which builds the kind of loyalty that’s been so hard to come by in an industry where over 30% of claimants feel dissatisfied, and 60% blame slow settlements.

This protective instinct also helps the whole system. AI is a brilliant fraud detective for the insurance industry and beyond, spotting weird patterns in data that a person would miss, and has the potential to cut fraud-related losses by up to 40%. It keeps everyone honest and protects the business and its customers.

What’s pouring fuel on this fire of change? A new breed of low-code platforms. They are the accelerators, letting insurers build and launch new apps and services much faster than before. In a world where customer tastes and rules can change overnight, that kind of speed is everything.

The best part of such tools is they democratise access and put the power to innovate into more hands. They allow regular business users – or ‘citizen developers’ – to build the tools they need without having to be coding geniuses. These platforms often come with strong security and controls, meaning this newfound speed doesn’t have to mean sacrificing safety or compliance, which is non-negotiable for an industry like insurance.

When you step back and look at the big picture, it’s clear that getting on board with AI isn’t just a tech project; it’s a make-or-break business strategy. Those who jumped in early are already pulling away from the pack, seeing things like a 14% jump in customer retention and a 48% rise in Net Promoter Scores. 

The market for this technology is set to explode to over $14 billion dollars by 2034, and some believe AI could add $1.1 trillion in value to the industry every year. But the biggest roadblocks aren’t about the technology itself; they’re about people and old habits.

Data, especially in an industry like insurance, is often stuck in old systems which stops AI from seeing the whole picture. To get past this, you need more than clever software. You need leaders with a clear vision, a willingness to change the company culture, and a commitment to training their people.

The winners in this new era won’t be the ones tinkering with AI in a corner—they’ll be the ones who lead from the top, with a clear plan to make it a part of their DNA. This will require an understanding that it’s not just about doing old things better, but about finding entirely new ways to bring value and build trust.

Learn more about how AI is rewriting the rules of the insurance industry at the upcoming webinar “From Complexity to Clarity: AI + Agility Layer for Intelligent Insurance” on July 16, 2025, at 7PM BST / 2PM ET. Industry experts from Appian and EXL will share real-world examples and practical insights into how leading carriers are implementing these technologies. Registration is available at the webinar link.

Featured speakers include:

• Vikram Machado, Senior Vice President & Practice Leader – Life, Annuities, Retirements & Group Insurance, EXL
• Vikrant Saraswat, Vice President – AI Consulting, EXL
• Jack Moroney, Enterprise Account Executive – Insurance & Financial Services, Appian
• Andrew Kearns, Insurance Industry Lead, Appian
• Michaela Morari, Senior Solution Consultant – Insurance & Financial Services, Appian

See also: UK and Singapore form alliance to guide AI in finance