The biggest AI problems for nonprofits (and solutions)

I’ve been talking with many nonprofit professionals about AI in the last few months. There are some issues that come up over and over in these conversations (environmental impact, copyright infringements, poor results, etc.). Some people don’t want to use AI at all because of these issues.

I agree that these issues exist, but I also think some potential problems have been exaggerated or can be reduced a lot if we use AI “properly”.

It’s not black or white. AI is clearly not perfect and we should not use it for everything, but some AI uses could help nonprofits a lot and have more positive than negative effects overall. I think taking extreme positions like “everything regarding AI is awful, I won’t use it at all” could be doing more harm than good to your nonprofit and your positive impact on the world.

Also, AI won’t probably steal your job anytime soon, but refusing to use any AI could make you unemployable in a few years. There is a high chance that we will reach the point where it will be like saying you refuse to use Internet, Google or social media for your work, because those technologies have some negative effects on society. Many organizations will probably find it too extreme (and negative for their results & impact), so they will hire someone else.

Let’s discuss the main issues about AI that I’ve heard* :

Poor results

This is probably the most important point. If AI gives bad results, then everything else doesn’t really matter, nobody should use AI (even if it had no other issues).

But many studies are showing great results using AI (and most of them were using old AI models, now we have much better ones):

• Workers using AI tripled their productivity (doing in 30 minutes tasks that would have taken them about 90 minutes on average without AI).
• Using generative AI improves employee productivity by 66% on average.
• Top-tier consultants (BCG) using AI finished 12.2% more tasks on average and produced 40% higher quality results than those without.

Another data point: ChatGPT has +400 million active users. So we can assume that millions of people are achieving good results, at least for some tasks (if they only saw poor results, they would not use it anymore).

Also, many big companies are investing billions in AI, much more than they did in anything else before (not even the Internet), so unless we think they are just dumb or want to lose billions for no reason, we should assume they are seeing a huge potential to increase results, productivity, innovation, etc.

There are certainly some tasks where AI doesn’t deliver good results (yet). But also many tasks where AI is already better than humans (or at least much faster + similar results). It’s not easy to predict the “winners” beforehand, because AI doesn’t think or behave like us. We have to try different tasks, tools and prompts. Some experts call this the “jagged frontier” of AI.

Interestingly, many people who report poor AI results seem to have tried very few tools and tasks. “ChatGPT for writing texts” is the most common response I’ve heard (which is not one of the tasks that ChatGPT does great, especially if you don’t give examples and context, it’s usually better at giving ideas, summarizing info, detecting issues, translating or modifying texts, generating code and many other tasks).

There are thousands of AI tools available now and there are also thousands of possible uses of AI. So some people are basically saying “I’ve tried less than 1% of AI and I’ve concluded that AI is not useful for anything. No need to try the other 99%, let’s forget about AI forever.”.

Also, between 40% and 69% of nonprofit organizations have not received formal AI training at all. So probably many people reporting bad results don’t know how to use AI tools properly (which is normal, they are pretty new and there is a lot to learn). They are probably not using “prompt engineering” techniques, advanced features and settings, specialized tools instead of ChatGPT for everything, etc. So it’s not just a tool problem, but a people/training problem.

For example, some people complain because ChatGPT responses are not always reliable (and that’s true, it sometimes invents/hallucinates info), but that doesn’t make AI useless. You just have to know how to use each AI tool (use AI search tools like Perplexity or Deep Research when you need reliable facts and sources, don’t use them for important stuff without human supervision, learn about dozens of use cases focused on creativity and innovation, etc.)

So now that we know that AI can give good results (at least for some tasks), we have to figure out if the positive effects of AI results can be bigger than the negative effects generated by AI issues (and how to decrease those negative effects if possible). So let’s focus now on those issues…

Environmental impact

The AI industry is in hyper-growth mode and that has a big environmental impact. No doubt about it. Big data centers with thousands of powerful chips consume a lot of energy (not 100% renewable in most cases), water (although this might change in the future) and materials.

But making a responsible use of AI is probably not as bad for the environment as many people think.

There are 2 main steps regarding AI models: Training and running them (also called “inference”).

1) Training new AI models (especially frontier LLMs) consumes a lot of energy, but for popular models that training “cost” will be divided between millions of users. And a trained model can in theory be used forever (especially true for open source models). So the impact per use might not be very big in the long term for some of the current AI models.

Also, AI companies are probably going to keep training new models (at least for a few years) even if a lot of people refuse to use them. Among other things, because they are focused on winning the AI/ASI race, not so much on earning money right now. There are even AI companies with billions in funding that don’t even plan to launch any public product anytime soon, because they are focused on training a future ASI (Artificial Super Intelligence), not on current AI uses or models.

So some might argue that AI training costs are not directly linked with our individual use. If companies will train those models anyway (or have trained them already), we might as well use them. I’m not sure if I support this reasoning, but I guess it’s food for thought.

2) Running AI models can have a big energy cost (if we are doing complex tasks like creating long videos) or quite small (if we are doing simple things like asking short questions to small text models).

According to MIT, the energy needed for simple AI chatbot tasks can be around 114 joules per response. That’s very little energy. Your laptop probably consumes around 3,000 joules per minute (26x more). Watching 1 hour of Netflix might consume more than 200,000 joules (1,700x more). Your household might consume about 100,000,000 joules per day (800,000x more). So asking a few simple things to AI models makes almost no difference in your overall energy consumption (it’s probably less than 1% of your energy use). Instead of refusing to use AI, it may be more impactful to use Netflix or other video streaming apps less frequently, for example. Or change other things in your life that may have 100x more environmental impact.

Using AI may even save energy in some cases. For example, if you get a good result from an AI chatbot in a few seconds instead of using your laptop for 20 minutes to get the same thing “manually” (without AI).

Also, some experts argue that AI might actually help us reduce our emissions in the next decade (by accelerating innovation, optimizing complex systems in key industries, etc.). So the overall impact of AI on the environment might become very positive, even if we consume significant resources to run AI systems. But for now that’s just based on long-term projections, not guaranteed at all.

It’s difficult to calculate the exact environmental impact of our individual AI use. There is not a lot of reliable data and dozens of variables to take into account (which prompt and model you are using, if you are using cloud AI or a model installed on your own computer, if you include the energy cost of AI training in your individual use or not, what is the non-AI solution that you would use instead for those tasks, if the results of your work may help the environment, etc.) .

But I think we can extract 3 main learnings:

1) We should consider the environmental impact of different AI uses and try to use AI only when the positive impact (on society, our organizations and/or ourselves) is bigger than the energy cost. For example, we should probably not create AI videos for jokes or other irrelevant stuff (big energy cost and zero positive impact). But if we can find simple AI tasks that save a lot of time or increase our results, I think we should go for it (small energy cost and positive impact on society). If you still feel bad about your AI use, maybe you can donate to an environmental nonprofit or do carbon offsetting to compensate for it.

2) We should learn more about AI, so we can use it more efficiently (learn “prompt engineering” to avoid spending time & energy to get mediocre results, avoid using big AI models for easy tasks, share learnings and wins with our colleagues instead of doing all the trial and error by ourselves, etc.).

3) If you want to reduce your environmental impact to the minimum, you can install open-source AI models on your own computer (especially if you have one of these new laptops that are at the same time energy-efficient and powerful enough for AI workloads: Macbook M3-M4, Copilot+ PCs…). Open-source AI models are good enough for many tasks and you can even save energy by using them in some cases (compared to not using AI and spending many hours doing manual work on your computer). As an bonus, they can have some privacy and security benefits (since you are not sharing anything with third-party tools or cloud servers).

Copyright issues

AI models (especially LLMs) are trained using massive amounts of data & content. Most AI companies are not very transparent about which content they use exactly, but apparently, we can assume that a significant part of it is copyrighted content.

There is a big debate around this (including many courtroom cases). Some say it’s a huge copyright infringement. Others argue that training an AI model with copyrighted content is “fair use”, since it’s closer to learning and taking inspiration from other creators (like human artists and creators do all the time) than to copying specific content directly.

Anyway, I think most of us understand why many creators are mad at AI companies using their content and giving nothing in return.

There are some solutions for creators who don’t want their content to be included in AI training (blocking AI scrapers with robots.txt and other tools, using protective tools like Glaze on their artwork…). But even if those solutions worked perfectly, getting zero compensation is not a great solution for many artists and creators anyway (some are earning much less than before the “AI era”).

As users, we can prioritize the use of ethically-trained AI models (using public domain and/or licensed content). There are a few of them (especially for image and audio generation), such as:

• Shutterstock AI: AI Image generator that ensures fair compensation for artist contributions to the training model.
• Blunge.ai: AI Image Generator that prevents AI-generated art theft.
• Models certified by the nonprofit organization Fairly Trained.

As far as I know, none of the big AI tools (ChatGPT, Google Gemini, Claude, etc.) can be considered ethically-trained right now.

If you want to use those AI tools but still support creators, you could do some sort of “compensation” by buying something from creators each month (art, digital products, memberships, services…) or donate to individual artists or associations. It would be something similar to carbon offsets but for creators impacted by AI.

I hope the AI industry comes up with a better solution (maybe forced by laws or courtroom decisions), but in the meantime…

Privacy & security issues

Not a lot of people mentioned this to me, but I think it’s an important one. Some organizations are uploading a lot of files (even their whole drives) to AI tools without being careful about it.

Most AI tools are probably pretty secure, but almost anything that is online can get hacked. There are also other possible privacy and security issues related to AI use (people having access to certain messages or files via AI that they should not have, data exfiltration via agents, etc.).

Also, some AI tools reserve the right to train their models with your messages/info, but they usually have an option to deactivate this and/or they don’t do that at all on paid accounts and API usage.

I think all organizations should give training to their staff to mitigate these risks.

Just telling people to not use AI at all doesn’t work. Around 35% of people hide their AI use to their organizations/employers. So even if your organization forbids any AI use, the risks will still be there (or maybe even increase because they can’t share their concerns, security tips, etc.)

A lot of these risks can be avoided if we just don’t give sensitive information to AI (there are plenty of AI uses that don’t require that info).

If you need to use sensitive information, maybe use open-source AI models installed on your computer (e.g. Ollama or LM Studio) instead of the cloud services (e.g. ChatGPT). Or at least erase the sensitive files and conversations from those cloud AI services immediately after finishing them.

Impact on society (jobs, creativity, critical thinking, misinformation, bias…)

I think this is a key topic to discuss, but not sure if it’s more of a problem or an opportunity.

AI is already having an impact on many jobs and this trend will probably accelerate in the next few years. If we achieve Artificial Super Intelligence (an AI that is better than humans at most tasks), it will probably lead to the most profound transformation in the labor market in history.

Whether we like it or not, I think this is going to happen sooner or later, it’s not in our hands. The potential benefits are too high, so all the big companies and governments will continue pushing for it. Even if social pressure manages to stop this in one country, the rest will continue the race.

So instead of trying to fight any use of AI, I think we should focus on how we can use AI to generate more positive effects (increase our creativity, productivity, social impact, etc.) and reduce the risks and negative effects (reduce bias and misinformation with AI help, train people for this new world, approve laws to avoid “high-risk AI”, etc.),

There are plenty of scientific studies that show both positive and negative impacts of AI in different organizations and society in general. It depends on how we use this powerful tool. For example, AI can make students lazier, but it can also improve their learning performance and higher-order thinking. It can also open opportunities that were impossible without AI (such as having unlimited tutors to guide each student individually, anywhere in the world and almost for free).

I think the same applies to nonprofit organizations. AI will change how we work, but I think this is an opportunity to increase our social impact and also our personal satisfaction (focusing more on creative and strategic initiatives instead of repetitive tasks and grunt work).

Most nonprofit organizations don’t really want to replace humans with AI bots, but they are very interested in having more productive and happy workers (with the help of AI or any other tool they can use).

It’s normal to be afraid of change. And there will be probably some negative effects in this “AI revolution” (like it happened with every new powerful technology). But I think the overall impact can be very positive if we embrace it and plan our future with responsible AI use, instead of fighting any AI use and clinging to the past.

Next steps