With the recent stock market performance, the question of whether AI is overhyped and will crash has been discussed by many analysts. To understand if Artificial Intelligence (AI) will crash or enter an AI Winter, we must define what an AI Winter is, as well as when and why it happened. There are presently significant risks to AI that can cause another winter. However, AI has good reasons to be hopeful about the future and avoid another AI winter.

The Gartner Hype Cycle

According to the Gartner hype cycle, an AI winter occurs when the technology fails to meet expectations and passes through the “peak of inflated expectations.” At this point, the technology enters the “trough of disillusionment,” where governments and companies re-evaluate their current funding levels and either reduce or eliminate the funding for those projects. Hopefully, the technology can survive this challenging time and later achieve value, eventually reaching the “plateau of productivity.”

Previous AI Winters

There were two previous AI winters: 1974 to 1980 and 1987 to 2000. Funding was the principal reason in both AI winters. There are two sources of AI funding: public and private sectors. The AI public sector funding was reduced or eliminated in both cases due to stagnation in the 1970’s and the 1987 market crash where public funding was significantly cut. Private funding was in its infancy and companies chose to focus on improving collaboration through distributed computing and networking technologies.

Is Another AI Winter Possible?

Current risks that could create a new AI winter include regulatory impacts, AI trust/ethics, security/privacy issues, chip/compute limitations and environmental concerns like power consumption and cooling. Regulatory presents the biggest risk. Governments that are too restrictive in their regulations can stifle competition, creativity, and increase operating costs. AI trust/ethics is another risk where governments, companies, and individuals can severely impact AI by unethical acts. Security/privacy concerns are a major challenge. AI must protect the individual privacy and ensure the necessary level of security to protect their information.

My Conclusion

The current AI accelerators, I believe, are going to keep AI from going into another winter. These include the democratization of AI, adoption rates, private sector funding, chip development and quantum computing, AI Software platforms/re-usability, and Cloud availability.

While another AI winter is possible, I do not foresee it happening. AI has been around since the 1940s and has clearly demonstrated its benefits to the public. We are at a tipping point where all the adjacent technologies like compute, storage, networking, cloud, software, and security are further enabling AI adoption by governments, business sectors, and individuals. Even though an AI winter may not happen, there will still be hurdles to overcome in further AI adoptions.