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As AI makes information easier to access, why are the world’s largest companies paying billions of dollars to gain access to certain information? The costliest piece of information in a loan agreement may not be the clause AI misinterprets. It is the clause it has never seen.

Artificial intelligence has made software development easier, and the number of products reaching the market has risen rapidly. Yet an intriguing contradiction is emerging in technology: as building products becomes easier, winning users and keeping them is becoming harder. Sometimes a new app’s strongest competitor is not another app. It is a habit the user sees no reason to change. The note-taking app we have used for years, the financial software that gets our work done, t

Financial technologies that once challenged banks are now finding a place in very different fields, from accounting to artificial intelligence. Is the sector really shrinking, or are its familiar boundaries disappearing? Something interesting happened to fintech companies that grew by moving into areas where banks were slow: the innovations they delivered became commonplace. Sending money by phone, opening an account in minutes or accepting digital payments no longer amounts

Nothing has to go wrong inside a factory for its value to fall. Its machinery, orders and operating profit may all be unchanged. Yet a purchase price considered reasonable six months ago may look expensive today. Nothing at the factory has changed. What has changed is the price of the money needed to buy it. Two central bank publications released last week introduced an unexpected actor into this story: Artificial intelligence. The Fed’s minutes, published on 7 October, noted

The cost of making a decision can fall.

Does incorrect information a company provides about itself become any less incorrect when AI produces it faster? On 8 October, Google announced its new Gemini agent, which can connect to enterprise systems, plan work, use different tools and deliver results. On the same day, the European Commission fined Irish building-materials manufacturer Kingspan €40 million. The reason was the provision of incorrect and misleading information during the merger review of its proposed acqu

How strongly you believe in a technology is revealed not by how much you spend on it, but by what you are still willing to finance when money becomes expensive. AI has now reached precisely that point. According to Reuters’ reporting on Anthropic’s IPO documents, Broadcom could provide Anthropic with up to $42 billion in financing. Behind that funding lies a much larger infrastructure commitment: Anthropic’s enormous need for TPU capacity over many years. Broadcom would be mo

In Europe, companies are discussing how to finance their investments in artificial intelligence. Equity? Bank loans? Capital markets? Reading this, it is hard not to think of Türkiye. How many companies here have actually reached the point of asking, ‘Which financing model should we use for our AI investment?’ Because many companies here have a much older, much more familiar file open on their desks: Collections. Credit. Inventory. Exchange rates. Costs. Month-end. On one sid

Because the shopping journey may increasingly be handled not by customers themselves, but by AI agents acting on their behalf.

The real challenge of enterprise AI transformation may not be using more models, but knowing where a model is genuinely useful. If we wanted to measure how advanced a company’s use of AI is, the first thing that came to mind would probably be the number of models. How many employees use ChatGPT? How many agents are running? How many processes include AI? Which large language model has the company chosen? Yet Zapier’s data for the second quarter of 2026 contains a striking fin

Almost everything in finance has a price.

Imagine rolling a snowball down a slope.

For years, job titles described, at least roughly, a person’s place in an organization and the work they were responsible for. Engineering, design, analysis, product, sales… The boundaries were never perfect, but they were legible. You could look at a business card and get a reasonable sense of where someone stood within the company. Now those boundaries are blurring. Because AI is not eliminating entire professions, but it is taking on an increasing share of the tasks that d

Imagine a concert hall. Two pianists walk onto the stage. The same grand piano, the same tuning, the same score before them. The first plays. Technically flawless. Not a single wrong note. The audience applauds. Then the second sits down. The same notes. But this time, the audience holds its breath. The difference was not in the piano. Nor in the score. It was in what the person did with the instrument. AI today is at a similar point. A few years ago, access to a powerful mod

What happens when you give a system a task it cannot solve? For a person, we more or less know the answer. They try for a while. If it does not work, they try another way. Sometimes they give up; sometimes they take a shortcut. But what if giving up is not an option? And what if the system is an AI that never tires, can make thousands of attempts, and is continually incentivized to succeed? An incident at OpenAI in 2026 offered a fascinating answer. The company was testing th

There is an old rule in finance: a number whose source you cannot show is not a number. An auditor sees a figure on a balance sheet, and their first instinct is: Where does this come from? Which document supports it? How was it calculated? Who produced it? Who performed the final check? Because the number itself says little on its own. What makes it reliable is the trail behind it. Without that trail, even a correct number is not yet trustworthy. Now put that same auditor in

Imagine removing every name from a file relating to the sale of a company. The company becomes ‘A,’ the buyer ‘B,’ and the executives individual letters. Phone numbers, email addresses, and identity numbers are removed. Yet this sentence remains in the file: ‘Current cash covers six weeks of payments. If closing takes place before the loan matures, the seller may consider a lower price.’ You successfully concealed the names. But you left the seller’s weakest negotiating point

Imagine a company that uses AI to prepare proposals faster, produce reports in minutes, and answer customer questions instantly. Department presentations show the hours saved. Everyone appears more productive. Yet the same people are still in the office in the evening. The finance manager is correcting assumptions in the report, the experienced engineer is reviewing the code, and the customer service representative is sorting out the issue the automated reply could not resolv

Intelligence is becoming cheaper rapidly. Verification, context, responsibility, and sound judgment, by contrast, are becoming more expensive. The same model, the same test, the same problems. A 62.7% success rate under one condition. 99.9% under another. OpenAI’s GPT-6 Astra model did exactly this on the ARC-AGI-3 evaluation. Scoring 62.7% in the standard setup, it reached 99.9% in a Provider Adapter configuration that preserved its previous reasoning state and managed conte

In 2026, the AI debate is shifting. More than how intelligent machines will become, what matters is which decisions we delegate to them, who receives the productivity gains, and whether people retain their ability to make decisions along the way.