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Highlights
- Obviously, AI has enormous potential to contribute to successfully battling major global challenges.
- However, attempting to solve P-type (and some NP) problems is not always about saving the world.
- While we attempt to solve the complex problems, let’s also keep these real headaches in mind: lack of transparency, bias, and data privacy concerns.
- With all these possibilities, the question isn’t just “Can AI solve this problem?” but “Should it?”.
More than a million queries are sent to large language models daily, consuming an average of a billion tokens. And the number of tokens used specifically to say please or thank you within these queries is astonishing. A very large amount of computation power, representing millions if translated into dollar terms, goes toward processing these tokens. Maybe it is a good investment, because we are training the models to be polite, but is it the best investment?
In 2025, AI is everywhere: automating mundane tasks, organizing our lives, and providing recommendations on everything from what to buy from Amazon to how to get better ZZZs. But when it comes to problem solving, how do we decide which issues are truly worth throwing our silicon brains at?
If we categorize problems according to computational theory, they can be classified by the following types, based on the difficulty level of the problem and which technology/technologies can be used to solve it: P, NP, NP Complete, and NP Hard.
Let’s learn a bit more about these types:
- P (Polynomial Time): Problems which can be solved quickly (in polynomial time) by a computer. For example, input income and deductions to calculate the tax and refund amounts.
- NP (Nondeterministic Polynomial Time): Problems for which a solution can be verified quickly but may take some time to find. For example, assigning limited ICU beds in hospitals based on severity, age, and comorbidity. In this example, it may be relatively quick to confirm which patients meet the requirements, but it could take a lot longer to determine the best allocation from there.
- NP Complete: Problems for which solutions are verifiable in polynomial time. Such problems are often solved by heuristics and approximation algorithms. For example, finding the best combination of locations at which to place fire stations to minimize response time.
- NP Hard: In spite of the name, these are not necessarily NP problems, as they are not solvable nor verifiable in polynomial time due to the exponential number of possible solutions. Gerrymandering (the manipulation of electoral boundaries) is a classic example, as the number of possible combinations grows exponentially as variables increase.
While it may be engineering overkill to attempt P-type problems using AI and GenAI, the latter is often very helpful in performing heuristics, scenario modelling, data modelling, and approximation to offer better solutions to many NP and NP-complete problems. And when it comes to NP-hard problems, it is expected that the ones that cannot be solved by GenAI today will be solved by Artificial General Intelligence in the future.
Obviously, AI has enormous potential to contribute to successfully battling major global challenges. For example, AI is addressing the climate crisis by predicting weather patterns and recommending ways to reduce energy consumption, based on comprehensive data analysis. And when it comes to healthcare, AI can be a valuable asset for improving diagnosis and treatment methods. AI is accelerating drug discovery, simulating protein folding, and even helping physicists hunt for new particles. Meanwhile, in agriculture, AI drones are monitoring crops, predicting yields, and scaring off crows—giving farmers a much-needed helping hand.
However, attempting to solve P-type (and some NP) problems is not always about saving the world. Some efforts are just about alleviating the annoyance of picking an unripe avocado. Yes, AI-powered shopping carts now judge your produce choices, ensuring your guacamole dreams never turn brown. AI is also revolutionizing entertainment, with AI stand-up comedians taking the stage and meme generators waging digital meme wars. AI is ensuring the internet remains a chaotic, hilarious battleground.
While we attempt to solve the complex problems, let’s also keep these real headaches in mind: lack of transparency, bias, and data privacy issues. The well-known “black box” problem refers to AI-based decisions which cannot be readily explained, which is a big deal if you’re trusting it with your medical records or asking it for legal advice. Bias is another persistent issue, as feeding biased data into AI models can lead to stereotypical or unfair decisions. For example, people of color are often misidentified by facial recognition systems and applicants with “traditional” names have sometimes been favored over those with less common names when their resumes are screened by AI. Data privacy is another major area of concern, with AI systems requiring vast amounts of personal data to function effectively. The challenge is balancing innovation with protecting individual rights, especially as regulations struggle to keep up with technology.
Given that we know AI can likely solve many of the problems we are facing, the question becomes: “Should it?” Sure, AI can write poetry, but judging by some AI-generated haikus, the jury is still out on that one. Should we let AI decide who gets a loan, who gets hired, or who wins a reality TV show? Ethics aside, maximizing AI resources means focusing on problems that matter: improving lives, solving global challenges, and occasionally ensuring the ripest avocado. The best use cases are those that free up human creativity, increase fairness, make our lives easier, and help us tackle the issues that really keep us up at night. As for the rest? Well, if AI wants to try its hand at stand-up, who are we to judge? Just don’t ask it to explain the joke; it’s still working on that.
About the author
Image of Pallab Datta
Pallab Datta
Pallab is a technology enthusiast, innovator, and seasoned business leader with experience in software consultancy, design, and delivery. Pallab loves to explore uncharted territory and discover new possibilities. Outside of work, he enjoys spending quality time with his family and is also an avid soccer enthusiast.
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