Why AI doesn't work without humans

It was coming for our jobs! And to some extent it has. But it needs us. More than we need it. Here's why.

The one-word summary: Inbreeding.

Inbreeding does not actually create genetic mutations, but it drastically increases the chances that harmful ones will be be expressed. Everyone carries hidden, recessive gene mutations that are usually masked by a healthy dominant gene from an unrelated parent; however, because close relatives share much of the same DNA, offspring of inbred pairs are far more likely to inherit two copies of the exact same damaged gene, causing hidden genetic disorders and health defects to manifest.

This is what will happen with AI models as they eventually feed off less and less human input and more and more information generated by other AIs.

To use another analogy, it's like making a photocopy of a photocopy of a photocopy of a photocopy. All the flaws, so tiny at first, become magnified, along with new flaws that are created with each pass through the copy machine. Soon the noise overwhelms the signal.

The vacuum effect

The remarkable success of AI so early out of the gate can be attributed—as with most tech hype—to it being tested on simplistic models. Those models lived in a vacuum. Real life is messier, in ways we still haven't leaned to model. People can create genuinely new ideas and adapt when reality refuses to cooperate. But so far AI can only recombine information that already exists. Ironically, AI's early success came from operating in a world clean enough for it to master. Real life isn't that world. AI might do well to become acquainted with Chaos Theory, where a hurricane can arise from a butterfly's flight. But even then it would model Chaos Theory too simplistically, because AI is inside it, and you cannot fully model something with complete accuracy when you are confined within it. Philosophers and mathematicians call this Gödel's Incompleteness Theorem, and it's a powerful, probably insurmountable limitation. But I'm fairly confident "geniuses" like Elon Musk and Sam Altman have never heard of it.

Maybe someday this will be proven, as Einstein proved Newton's theories, to be incomplete, and a way "around" Gödel's IT will be found. I'll even go so far as to say AI itself will find it. But until then—and even in many cases after then—this limitation exists.

"Aha!"

AI can weave tapestries of astonishing intricacy, and it can do it far faster than any human. But the cloth—and the fibers that make up that cloth—are made by humans. AI can discover combinations and patterns that would take people much, much longer to recognize. But it cannot have an "Aha!" moment when its entire pool of knowledge is confined to the internet's universe of experience. It can't think outside the box like an Einstein, a Beethoven, or a Leonardo. Its known world is the box, and it can't even really comprehend that there is an "outside."

How do humans do this? That's the ephemeral thing we call creativity, genius, or inspiration—something we still cannot really define. It's like Justice Potter Stewart's definition of pornography, of all things: "I know it when I see it."

Recognizing brilliance...It's not always easy

Mistaking complexity for genius is a pretty common thing.

About 15 to 20 years ago I started hearing about this astonishing new genius who was supposedly the next Einstein. So I started following the words and thoughts of Elon Musk. I wondered almost immediately why everyone thought he was so brilliant. I was told by commenters—and I really don't think they were all bots—that I was stupid for not seeing it. It's obvious, they said. When I pushed for examples, I heard things along the lines of, "How much are you worth?" or "He's founded all these billion-dollar companies. What have you done?"

Well, in truth, he didn't found those companies. He was arguably the least important prong of PayPal, and from there he pushed his way onto multiple other companies. He was not the intellectual fount of any of them. From what I can see, he simply cracks the profitability whip harder than most others while making wild, completely unsubstantiated claims that keep him in the headlines. Almost all of those claims have turned out to be ridiculous, dead wrong, or both. Yet for a very long time he was able—and in many circles is still able—to convince enough people that he is the genius they already wanted him to be.

The human advantage

And I fear AI output itself will be able to do much the same thing—dazzling people with elaborate, seemingly profound ideas that persuade those who assume genius must be complicated and incomprehensible. We won't really be able to judge AI's eureka moments until long after the dust has settled.

The supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience. —Albert Einstein, Herbert Spencer Lecture, Rhodes House, Oxford, December 1933

I am fairly confident that, because AI is fundamentally a simulation of human intelligence and cognitive processes, it will never be able to produce truly original ideas without mining human thought. When those human inputs begin to disappear, AI will gradually churn into nonsense, only it won't know the difference.

But we will.

AI purveyors already know this, of course. You may have heard that they're trying to stuff every book ever written into AI's brains for it to scrape content and knowledge. As Techradar reports in this infuriating article, AI companies much prefer books published before 2022 to train their dragons, because those works predate the widespread use of AI-generated content.

It's a cleaner source, in other words. So the AI bros trying to rub out the very content that they know is superior quality and replace it with their slop. The difference is that the "slop" is, for them, profitable. (At least it's profitable in theory; no one's actually made any money off a business model hedge funds are investing billions into yet. And we used to be told Wall Street guys were smart.)

If you ever needed evidence the whole thing is headed for a giant implosion one day, that's it. We just have no way of predicting how soon. Or how later.

Of course this is cold comfort for people who are seeing their jobs and their livelihoods disappearing now. And it's not mean to be comfort exactly. But it is meant to point out that the sands shift, and the changes going on in grant writing and philanthropy in general are not absolute. AI advocates will say it is, but again, they cite models of the future that exists in a vacuum. Just google Irving Fisher, a top Yale economist who sported all sort of expert and authoritative bling, to see how predicting the future is a bad idea. Always.

Doing philanthropy proud

With so many billionaires hoarding their bananas these days while spending every waking moment trying to accumulate more, it's refreshing to see one of the richest women in the world doing good—and Snap founder Evan Spiegel is paying attention. This article is from the Times of India.

timesofindia.indiatimes.com

Jeff Bezos ex-wife MacKenzie Scott who sold half of her Amazon stake helped write a 'donation playbook' that Snap founder Evan Spiegel is following

TOI Tech Desk

Snap co-founder Evan Spiegel and his wife, supermodel Miranda Kerr, recently made a multimillion-dollar donation that will wipe out $550 million in medical debt for over 261,000 Californians.

Interestingly, this sophisticated philanthropic model was pioneered by MacKenzie Scott, Jeff Bezos ex-wife, who has donated more than $26 billion to over 2,700 non-profit organisations through her platform, Yield Giving. By leveraging a high-impact “donation playbook” to effective nonprofits, Spiegel is emulating the strategy that transformed Scott into the most prolific philanthropist of her generation.

Snap founder Evan Spiegel is following the Scott model. The donation was made to Undue Medical Debt, a nonprofit organisation that gained national prominence largely through early, substantial investments from MacKenzie Scott. The massive debt relief effort will target vulnerable residents across the state, with San Diego and Los Angeles counties receiving the largest shares of assistance. Scott donated a combined $80 million to the organization between 2020 and 2022, helping the once-scrappy nonprofit abolish over $40 billion in medical debt across all 50 states.

Spiegel’s strategy mimics Scott’s signature approach: identify scalable organisations, provide massive, unrestricted capital, and leverage unique market opportunities for maximum impact. “The scale of this gift to Californians is truly astonishing, unburdening over a quarter million families of over half a billion dollars of un-payable medical debt,” said Allison Sesso, president and CEO of Undue Medical Debt. The unique way of abolishing medical debt. The mechanism that enables this exponential impact lies at the intersection of distressed debt markets and American healthcare dysfunction. Hospitals routinely sell uncollectible patient debt to collectors in bulk portfolios for pennies on the dollar. Undue Medical Debt enters this market as a bulk buyer, acquiring these portfolios at the same steep discounts. However, instead of trying to collect the money, the organisation permanently abolishes the debt. Starting in mid-July, eligible Californians began receiving letters informing them that their medical debt has been erased. The relief is targeted toward residents whose medical debt threatens access to essential care and economic stability.

Read the full article here.

The Metric System

If there's one thing everyone is obsessed with today it's data. Big data. Small data. Data with a side of fries. This both helps grantees and limits them at the same time. Here's why.

The Age of Measurement

Spend any amount of time writing grant proposals today and you'll quickly notice a pattern. Nearly every application asks the same questions:

• How many people will you serve?

• What measurable outcomes will you achieve?

• How will you evaluate success?

• What data will you collect?

• What percentage improvement do you expect?

  
  

These aren't unreasonable questions. Foundations have a responsibility to ensure that charitable dollars are spent wisely. Donors deserve accountability. Nonprofits should be able to demonstrate that their programs are making a difference.

But somewhere over the past two decades, something subtle has happened. Measurement has evolved from a useful management tool into an end in itself.

Increasingly, organizations are expected to prove their worth not simply through stories of transformed lives, but through spreadsheets, dashboards, and statistically significant outcomes.

In the process, philanthropy may be undervaluing some of the very things it was created to support.

Not Everything That Matters Can Be Counted

It's easy to measure meals served.

It's harder to measure dignity restored.

A food pantry can report that it distributed 250,000 pounds of food this year. That's valuable information. But suppose volunteers also learned every client's name, treated them with warmth, and created an atmosphere where people felt respected instead of ashamed. How do you assign a numerical value to that?

Likewise, a mentoring program can count the number of mentor-mentee meetings. But what metric captures the moment a lonely teenager finally realizes that one trustworthy adult genuinely believes in them?

The numbers tell part of the story. Often they don't tell the most important part.

The Pressure to Quantify Everything

Many grantmakers now ask applicants to develop logic models, theories of change, SMART objectives, outcome matrices, and evaluation plans before funding is even considered.

These tools can be extremely helpful when used appropriately. They encourage organizations to think carefully about what they're trying to accomplish and how they'll know if they're succeeding.

Problems arise when these frameworks become rigid expectations rather than flexible guides.

Some nonprofit work simply resists precise quantification.

A symphony orchestra doesn't merely produce concerts. It inspires wonder.

A museum doesn't simply record attendance. It preserves civilization's memory.

A community theater doesn't just entertain audiences. It creates belonging, confidence, and imagination.

An after-school art program may never produce dramatic test-score gains. Yet twenty years later, one participant may become a teacher, another an architect, another simply an adult who believes life contains beauty worth preserving.

Which spreadsheet captures that?

The Long Horizon of Human Change

Funders understandably like outcomes that can be measured within a grant period—often twelve months.

Human development rarely follows such convenient timelines.

A child introduced to reading today may not discover a lifelong passion until college.

A neighborhood beautification project may slowly reduce crime, increase civic pride, and encourage local investment over many years.

A counseling program may plant seeds that don't fully bloom until someone faces a personal crisis years later.

The most meaningful changes in human lives often unfold gradually, unpredictably, and invisibly.

Yet grant reporting cycles tend to reward what can be counted immediately.

The Risk of Funding What's Easy to Measure

An old management saying warns:

"What gets measured gets managed."

An unfortunate corollary may also be true:

What gets measured gets funded.

Organizations whose work naturally produces numerical outputs often enjoy an advantage. Medical screenings. Job placements. Meals served. Vaccinations administered. Housing units built.

It goes without saying these are all vitally important, and generate readily reportable statistics.

Meanwhile, organizations devoted to strengthening community, nurturing creativity, preserving history, fostering empathy, or enriching civic life often struggle to describe their impact using the same language.

The irony is that these less measurable efforts frequently help create healthier, more resilient communities in ways that become obvious only over time.

So many pundits are wondering why, while being relatively well-off by historical metrics, we are so unhappy and feeling hopeless lately.

Perhaps they are only looking at what can be quantified. Perhaps if they got away from their computer data and out into the difficult-to-measure, fuzzy real world, they might understand.

The Seduction of Calculated Precision

Numbers feel objective. A report stating that "87.3% of participants demonstrated improved outcomes" carries an aura of scientific certainty.

Stories feel subjective.

Yet numbers can also create a false sense of precision.

Suppose an arts nonprofit reports that 96% of participants expressed increased self-confidence after attending workshops.

What does that actually mean?

Did their confidence endure?

Did it change future life choices?

Was the survey carefully designed?

Would another measurement have produced different results?

Quantitative evaluation is valuable, but it is rarely as exact as it appears.

Meanwhile, a single well-told story can sometimes illuminate truths that hundreds of survey responses cannot.

Humanitarian Work Is About Humans

Nonprofits exist because people are more than data points.

The elderly woman who receives companionship after losing her spouse.

The refugee who finally feels welcomed.

The child who discovers music.

The veteran who begins trusting people again.

These transformations involve emotion, identity, hope, confidence, belonging, and meaning.

Psychologists, philosophers, theologians, artists, and educators have spent centuries trying to understand these dimensions of human life because they are inherently difficult to measure.

Grant applications often ask nonprofits to compress them into percentages.

That tension deserves acknowledgment.

Accountability Without Reductionism

None of this argues against accountability.

Poorly managed nonprofits should not receive unlimited funding simply because their mission is inspiring.

Programs should be evaluated. Budgets should be scrutinized. Outcomes should be examined honestly.

The challenge is avoiding reductionism—the assumption that only measurable outcomes have value.

Good philanthropy recognizes that evidence comes in many forms.

Quantitative data matters.

Qualitative interviews matter.

Case studies matter.

Independent observations matter.

Professional judgment matters.

Community trust matters.

Sometimes decades of earned reputation tell us something that no spreadsheet can.

Toward a More Balanced Philanthropy

Perhaps the best grantmakers already understand this.

Many sophisticated foundations increasingly combine quantitative evaluation with narrative reporting, participant testimonials, site visits, photographs, interviews, and long-term relationships with grantees.

They understand that numbers explain how much.

Stories explain why it mattered.

Neither is sufficient alone.

The strongest evaluation often combines rigorous data with thoughtful human observation.

Remembering the Purpose

The charitable sector exists because society recognizes that not every important contribution can be justified by market forces alone.

Beauty rarely maximizes efficiency. There's a famous joke—you've probably heard it, but it bears repeating—

The CEO of a company fell ill on a day he had tickets to see a concert. As a gesture of kindness, he gave the tickets to the company’s efficiency expert to enjoy the show with his wife.

The next morning, the CEO found a formal memo on his desk, titled Performance Evaluation: Schubert’s Unfinished Symphony, offering the following, albeit comical, professional insights:

• Violin Overstaffing: The 22 violinists playing the same notes represented reckless waste and should be drastically cut.

• Oboe Inactivity: The oboe players had too much downtime, suggesting their number should be reduced to eliminate peak inactivity.

• Redundant Notes: The 16th notes were an unnecessary refinement, and repeating passages with different instruments was wasteful.

• Efficiency Gains: The expert concluded the two-hour concert could be finished in just 20 minutes by eliminating these redundancies.

  
  

Final Assessment: The efficiency expert concluded that if Schubert had implemented these recommendations, he likely would have found time to finish the symphony.

But that's just the beginning. Compassion seldom optimizes productivity. Often it can be diametrically opposed, in fact, and that isn't necessarily a bad thing.

Culture cannot always demonstrate return on investment. Was the Eiffel Tower "worth it"? Not originally.

Built as the extravagant, $1.5 million centerpiece for the 1889 World's Fair, it drew fierce criticism from 300 prominent artists who dubbed it a "useless and monstrous" "factory chimney" that would disfigure the city. 

Today, it is the world's most visited paid monument, drawing nearly seven million tourists annually. Its value to France simply as an object of promotion cannot be estimated.

Frank Lloyd Wright's buildings do not make sense "by the numbers." They are not the most efficient use of spaces or materials.

Yet these are among humanity's greatest achievements.

Perhaps the ultimate purpose of philanthropy is not merely to maximize measurable outputs, but to improve the human condition in all its complexity. That means embracing evidence while recognizing its limits. It means demanding accountability without confusing numbers for truth. And it means understanding the human condition—something you cannot learn from a computer screen, a PowerPoint deck or a spreadsheet.

It means remembering that the things most worth preserving—kindness, creativity, dignity, wonder, belonging, and hope—may never lend themselves to perfect metrics.

In the end, philanthropy should certainly ask nonprofits to measure what they can. But it should also have the wisdom to recognize that some of life's greatest gifts cannot be fully captured in a database.

After all, if everything that truly mattered could be reduced to numbers, we would have little need for art, music, literature, community—or charity itself.