6 AI questions you want answered by 2026

(Bloomberg Opinion) — The word of the year for 2025 is “slop,” which refers to low-quality content churned out by artificial intelligence, according to Merriam-Webster.

This aptly reflects the awkward stage we've entered three years after ChatGPT sparked a global AI boom. We were promised tools to treat disease and solve climate change. What we got in 2025 was mainly machine-generated porn, fake bunnies jumping on trampolines, and an internet that felt a little more spammy every day.

AI is now facilitating heated discussions from the boardroom to the classroom. But despite all the hype and tons of money, some of the biggest questions about how this technology revolution will play out remain unanswered. We hope to have a clearer answer in 2026.

What does the training data contain?

Is it an image of child sexual abuse? Thousands of copyrighted creations? Or vast amounts of material that perpetuates an English and Eurocentric perspective?

The answer seems to be “yes” to all of the above. But we don't know for sure because the companies building these systems refuse to say so.

As AI systems enter high-stakes environments such as schools, hospitals, recruitment tools, and government services, secrecy becomes increasingly indefensible. The more we hand over decision-making and agency to machines, the more urgent it becomes to understand what is happening to them.

Instead, companies have treated training data as a trade secret (or, increasingly, a liability). But this battle over transparency will come to a head in the new year. The European Union will require companies to share detailed summaries of their training data by mid-2027. Other jurisdictions should follow suit.

I don't think anyone can credibly declare that artificial general intelligence will be achieved in 2026. But before we debate whether we have achieved it or not, it would be helpful to collectively agree on what it actually is.

Google Deepmind researchers wrote in a paper last year: “If you asked 100 AI experts to define what ‘AGI’ means, you would probably get 100 related but different definitions.” Meanwhile, this nebulous concept has become a north star for an entire global industry, and has been used to justify hundreds of billions of dollars in investments.

The most widely used definition from OpenAI's charter describes it as “a highly autonomous system that outperforms humans at the most economically valuable tasks.” But even that is a bit fuzzy, as CEO Sam Altman has acknowledged over the past year. Automation also becomes a moving target as it invades more parts of the economy.

Internally, OpenAI and Microsoft had a financial goal for AGI to reach $100 billion in gross profits, at least according to this information. But asking consumers to pay for an app that rots their brains seems a long way from a true measure of “intelligence.”

I don't think AGI is a useful term. It fosters a cycle of hype and fear rather than a serious discussion about the social impact of AI and how to regulate it. The industry won't be abandoning the term anytime soon. But the least we can do is agree on an empirical way to measure it.

It's no surprise that Big Tech companies don't want to take on the burden of regulation, and governments don't want to risk falling behind in geopolitical competition.

But it will be difficult for policymakers to ignore growing societal concerns about the impact AI will have on everything from the developing brains of young people to their electricity bills. Outside of Europe, few jurisdictions are seriously attempting to address these threats.

Lawmakers would be wise to pre-empt the backlash before the damage spreads. We can't rely on companies looking to profit from this technology to write all the rules.

What will it take to burst the bubble?

In recent months, many in the industry seem to have accepted that we are in the midst of a bubble of sorts. That doesn't mean AI won't be transformative, but eye-watering valuations and seemingly cyclical investments in unprofitable companies are starting to look like red flags.

Still, this euphoria has been surprisingly resilient over three years, and although it shows some trepidation here and there, it shows no signs of slowing down. The fear of missing out remains strong. Eventually something will test it. Revenue growth could slow, perhaps due to saturation of early adopters or the rise of powerful free and open source models that erode the pricing power of closed systems.

Perhaps no single event will derail the global hype train. But in 2026, I expect more investors to start asking questions about how they can avoid becoming the people dancing when the music stops, and how they can assess risk and return with a clearer eye.

Companies will soon need to start proving that for every dollar spent on AI, there is at least a sustainable path to profitability. Chip makers are already making profits, but it's even more uncertain for model makers.

This will be particularly problematic in China, where competition is fierce and frugal consumers are reluctant to spend on software services. But even in Silicon Valley, where major companies are starting to make real profits, the numbers still seem small given the huge amounts of money being spent on data centers and expansion.

Whether consumers want it or not, we'll likely see more attempts to introduce new revenue streams, such as targeted advertising and TikTok clones. But at some point, investors will demand more than just a promise of AGI's future prospects.

This is the most common question I get when talking about AI in the real world. The anxiety is already there.

We've already seen investments in AI being used as a cover for layoffs in some parts of the technology industry, and we expect to see more of the same in other industries. Policymakers and business leaders will increasingly need to come up with solutions for how to deal with the massive labor market disruption that is just around the corner.

If there's a silver lining to this down year, it's that there is a human hunger for ideas and creativity that machines have yet to fully capture at scale.

I don't think we'll have all the answers in 2026. But the questions we ask, such as power, accountability, money, and meaning, will determine how we let AI reshape our world. In the new year, let's continue to be curious, skeptical, and stubbornly human.