The desire to foresee the future is as old as humanity itself. Today, that desire is being reshaped by artificial intelligence and its immense data-processing power. From agriculture to financial markets, this new technology holds the potential to transform decision-making—but it also raises profound questions: Can AI truly know the future, and if so, will that knowledge be accessible to all?

Since the dawn of humanity, people have longed to predict the future. A look at Egyptian history reveals how influential seers were in governance and decision-making. Pharaoh King Djoser once had a dream and sought its interpretation from the seers of his time. However, they failed to provide a satisfactory explanation. This indecision significantly affected Egypt’s administration. At the time, Joseph (Prophet Yusuf), who was imprisoned, interpreted the dream for the king. According to Joseph, Egypt would experience seven years of abundance followed by seven years of famine. As the story goes, Egypt survived the famine by stockpiling wheat during the years of abundance.

Today, many people still attempt to predict the future through fortune-telling, dream interpretation, or by observing the positions of stars. Economists and statisticians, on the other hand, rely on historical data and certain assumptions to make economic forecasts. Economists constantly compete to make the most accurate and impactful predictions. George Soros, often referred to as the wizard of money, emphasizes the importance of accurate forecasting in his book The New Paradigm for Financial Markets. Mustafa Suleyman, CEO of Microsoft AI, compellingly explains in his book The Coming Wave how artificial intelligence is poised to transform our world and humanity.

If we were to ask you this question—Can artificial intelligence predict wheat prices?—what would your answer be? You might say “yes” or “no.” At the very least, we all wish to have access to reliable AI-driven predictions related to our own line of work. Suppose you’re a farmer. Each year, you probably wonder what prices different crops might fetch. Or perhaps you’re a trader or processor of agricultural commodities, or even someone tracking stocks in the market. Wouldn’t you want to predict price fluctuations, buy at the right time, and sell at the most profitable moment? While we currently rely on experience, wouldn’t you prefer to access realistic insights derived from the analysis of millions of data points? We all would—and surely, we all will.

Whether you call it artificial intelligence, deep learning, or machine intelligence, we have entered an era that will profoundly affect all aspects of our economic, social, and cultural lives. While discussions rage on about what we’ll be able to achieve with machine intelligence in the near future, the real question is: can AI algorithms accurately predict the future? For instance, can they predict agricultural commodity prices? What are the conditions for making accurate forecasts? Can these algorithms provide access to information that could make individuals or companies rich—or bankrupt—overnight? These are questions worth exploring.

So, what exactly will AI do in this field? This may get technical, but it is expected to assess indicators such as Exponential Moving Average (EMA), Relative Strength Index (RSI), Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator, and Average Directional Index more precisely than current statistical methods. In addition, the forecasting industry is likely to evolve dramatically. For example, based on climate forecasts for the coming year, AI could estimate the likelihood of a fluctuation in wheat yields. It could more accurately assess how a contraction in cotton supply and demand or a price shift might affect corn production and global corn prices. A machine brain that can measure such fine variables has a high probability of accurately forecasting product prices.

You might wonder how likely it is that AI could predict the result of a process shaped by hundreds of natural variables (climate, soil, living organisms) and influenced by the will and production culture of millions of people. In other words, can AI analyze climate predictions, evaluate their effect on productivity for specific crops, and simultaneously interpret the tendencies of millions of producers and stakeholders? Who among us can argue that a machine brain—equipped with cloud technologies and 6G internet infrastructure, capable of processing data faster and more effectively than humans—cannot yield more accurate results?

But when discussing price formation in agricultural commodities, aren’t we missing a crucial concept? The mechanism we call the “market” inevitably includes some degree of manipulation. Without manipulation, what exactly drives price volatility in a free market economy? Will AI be able to detect manipulations? Or will it become a tool for creating them? At this point, I can’t foresee the answer. The problem is: if one person can know the future, then everyone can. But if everyone knows what one person knows, it contradicts the very nature of economics and price formation. So, will everyone benefit equally from AI? If not, will those who do grow richer while the larger crowd becomes poorer? These questions remain unanswered.

If AI knows everything and can predict the future, will it level the playing field between geniuses and average minds, between the educated and the uneducated? And if we become equal, will we be happier? Will speaking and writing lose their meaning? I do not know.

As I write this article in 2025, we are witnessing revolutions in artificial intelligence. China’s new AI, DeepSeek, caused American tech stocks to lose billions overnight. The crash in tech stocks also negatively impacted Japanese chip firms. Social media is now flooded with links offering AI-driven “buy-sell signals” for the stock market. It seems everything is evolving faster than we anticipated and becoming increasingly uncertain.

In my view, it will indeed be possible to forecast agricultural commodity prices—including wheat—using artificial intelligence. However, such highly valuable data will likely remain in the hands of those who develop closed-system AI. For now, this vast world of software is inviting us in with dazzling promises as it enters the data-collection phase. Will the artificial Josephs of the 21st century bring us famine or abundance? Will they provoke wars or establish peace? Will they pave the way for unjust wealth accumulation or for economic justice? Will they drive companies and nations to bankruptcy? Are we about to surrender our future to the dominance and mercy of a vast software empire created by the prodigies of ruthless capitalism? We shall wait and see. Surely, natural human intelligence is beginning to see—and debate—the dangers and ethical collapses that machine intelligence may bring.