Johns Hopkins University professor Steve Hanke discussed the impact of artificial intelligence on the labor market. The economist believes that new technologies will not lead to mass layoffs. He cites the high cost of implementing neural networks as the main reason. Replacing people with algorithms will cost companies more than regular hiring. Steve Hanke noted that advanced systems will not become free. Artificial intelligence requires huge expenditures on physical capital. The operation of neural networks consumes a large amount of electrical energy. These factors make the exploitation of technology very resource-intensive. The business will take these costs into account when making personnel decisions. Full automation of processes remains economically impractical for many organizations.

The economist also compared neural networks with traditional software. Regular programs do not require fixed costs after completion of development. Artificial intelligence needs continuous funding for its work. Steve Hanke linked the future of the industry to the cost of scarce resources. Infrastructure development requires significant financial investments from technology companies. Individual corporations are already recording negative cash flow due to investments. Industry representatives predict that high costs will remain for decades. SoftBank CEO Masayoshi Son estimated the future costs at trillions of dollars. These funds will be used to create and implement new systems. Technology giants continue to increase computing power for training models.

Steve Hanke's views differ from those of other market participants. Nvidia CEO Jensen Huang expects a gradual reduction in hardware costs. Elon Musk admits the complete replacement of people with algorithms in the future. However, current practice shows different results of technology implementation. Some companies are returning laid-off employees to their previous jobs. The replaced systems showed lower results in real-world tasks. Employers are re-evaluating the increase in labor productivity. The savings from using neural networks turned out to be lower than the initial expectations of the business. This confirms the thesis about the complexity of fully automating work processes. Human labor remains competitive in many sectors of the economy.