One thing you should never hand over to artificial intelligence: creative work

In a world where efficiency is king and disruptive technologies are creating multi-billion dollar markets overnight, it’s inevitable that businesses see generative AI as a powerful ally.

From OpenAI’s ChatGPT generating human-like text to DALL-E generating artwork on prompt, we’ve already seen a glimpse of a future where machines don’t just create alongside us, they may even lead innovation.

So why not extend this to research and development (R&D)? After all, AI can accelerate the generation of ideas, iterate faster than human researchers, and maybe even discover the next “hit,” right?

It all sounds great in theory, but here’s the reality: counting on AI to take over R&D is likely to backfire, even disastrously.

Whether you’re an early-stage startup looking to grow or an established company defending its turf, outsourcing the generational side of innovation is a dangerous game.

While embracing new technologies, people may lose the essence of truly breakthrough innovation, and even worse, may cause the entire industry to fall into a death spiral of homogenized, uninspired products.

Let's analyze why over-reliance on artificial intelligence in R&D can become a fatal weakness for innovation.

01. AI's "mediocre genius": prediction ≠ imagination

Artificial intelligence is essentially a super-powerful prediction machine. It creates by predicting the most appropriate text, image, design or code snippet based on a large number of historical precedents.

Although this seems efficient and complex, we must be clear: AI's capabilities are limited to its training data. It is not truly "creative" and will not engage in subversive thinking.

That is, AI is backward-looking and always relies on what has already been created. In the process of development, this becomes a fundamental flaw rather than a feature.

To truly break new ground, it takes more than incremental improvements extrapolated from historical data.

Great innovations often come from leaps, turns, and reimaginings, rather than slight variations on existing themes. Think about Apple's iPhone or Tesla in the electric car space, how did they improve on existing products?

Obviously, they all disrupt the existing model.

GenAI may continue to improve the design sketches of the next generation of smartphones, but it will not conceptually liberate us from the smartphone itself.

Bold, world-changing moments, those that redefine markets, behaviors, and even industries, all come from human imagination, not algorithmic probabilities.

When AI becomes the driving force of R&D, what you end up with is a better iteration of existing ideas, not the next epoch-making breakthrough.

02. The essence of AI is homogenization

One of the biggest dangers of letting AI control the product creative process is that the way AI processes content will lead to convergence rather than divergence, whether it is design, solution or technical configuration.

Due to the overlapping basis of training data, AI-driven R&D will lead to product homogenization across the market.

Perhaps there will be some slight changes in the product performance, but it is essentially the same concept with different "flavors".

Imagine this: you now have four competitors, and they all use AI systems to design the user interface (UI) of their mobile phones.

Each system is trained on roughly the same corpus of information, which is data collected from the Internet about consumer preferences, existing designs, best-selling products, etc.

Obviously, this will lead to very similar results.

Over time, one sees an unsettling visual and conceptual cohesion, as competing products begin to mimic each other.

Sure, the icons may be slightly different, and product features may be nuanced, but what about the substance, character, and uniqueness? Soon, they evaporate.

We’ve already seen early signs of this phenomenon in AI-generated artwork.

On platforms like Art Station, many artists have expressed concerns about the influx of AI-generated content, which, rather than showcasing unique human creativity, gives the impression of reusing pop culture references, broad visual tropes, and stylistic aesthetics. This is not the cutting-edge innovation that people want to fuel R&D.

If every company adopted generative AI as its de facto innovation strategy, the industry would not have five or ten disruptive new products every year, but only five or ten dressed-up clones.

03. Human “magic”: How does serendipity drive innovation?

History books tell us that penicillin was discovered when Alexander Fleming accidentally forgot to cover his petri dish; that the microwave oven was invented when engineer Percy Spencer stood too close to a radar device and accidentally melted a bar of chocolate; and that even the invention of the Post-it Note was a byproduct of a failed attempt to create a super-strong adhesive.

In fact, failure and serendipity are an integral part of R&D.

Human researchers have a unique sense for the value hidden in failures, and they are often able to see accidents as opportunities.

Serendipity, intuition, instinct are all key to successful innovation, just like any well-crafted R&D roadmap.

But here’s the crux of generative AI: it has no concept of “ambiguity,” let alone the flexibility to understand “failure” as an asset.

AI programming teaches it to avoid mistakes, optimize accuracy, and resolve data ambiguity. This is great if you want to simplify logistics or increase factory output, but it’s a fatal flaw in breakthrough exploration.

But here’s the crux of generative AI: it has no concept of “ambiguity,” let alone the flexibility to understand “failure” as an asset.

AI is programmed to avoid errors, optimize accuracy, and resolve data ambiguities. This is great if you want to streamline logistics or increase factory output, but it is a fatal flaw in breakthrough exploration.

AI eliminates the possibility of productive ambiguity, that is, explaining accidents and overturning flawed designs, but it also limits potential paths to innovation.

Humans embrace complexity and are good at discovering possibilities from unexpected outputs.

AI will only double down on certainty, mainstreaming mediocre ideas and rejecting anything that seems irregular or untested.

04. AI lacks empathy and foresight

Innovation is not only a product of logic, but also a product of empathy, intuition, desire, and foresight.

Humans innovate because they care not only about logical efficiency or the bottom line, but also respond to subtle human needs and emotions.

We dream of making things faster, safer, and more enjoyable because, fundamentally, we understand the human experience.

Think about the design of the first iPod or the minimalist interface of Google Search. These game-changing designs were successful not because of pure technological advantages, but because we could empathize with users' frustrations with complicated MP3 players or cluttered search engines.

The new generation of artificial intelligence cannot replicate this.

It doesn’t know what it’s like to wrestle with a buggy app, or feel the wonder of minimalist design, or the frustration of an unmet need.

When AI “innovates,” it does so without emotional context. This myopic approach undermines AI’s ability to come up with ideas that resonate with humans.

Worse, without empathy, AI-created products may be technically impressive but feel soulless, lifeless, and transactional—“inhuman.”

In R&D, this is an innovation killer.

05. Over-reliance on AI can lead to skill degradation

A final chilling thought for AI future enthusiasts: What happens if you let AI get involved too much?

It’s clear that in any field where automation erodes human involvement, skills will degrade over time.

Just look at industries that introduced automation early: employees lose their understanding of the “why” of things because they don’t regularly exercise their problem-solving skills.

In an R&D-heavy environment, this poses a real threat to the human capital that builds a long-term innovation culture.

If research teams become mere overseers of AI-generated work, they may lose the ability to challenge and surpass AI output.

The less innovative practice, the weaker the ability to innovate autonomously. By the time people realize they have lost their balance, it may be too late.

When markets change dramatically, this erosion of human skills is extremely dangerous, and no amount of AI can lead people through the fog of uncertainty.

Disruptive times require humans to break out of the box, and that’s exactly what AI will never be good at.

06. The Road Ahead: AI is an aid, not a replacement

The above is not to say that AI has no place in R&D. As an auxiliary tool, AI can allow researchers and designers to test, iterate creative ideas, and refine details faster.

Used properly, it can increase productivity without suppressing creativity. The key is: we must ensure that AI is a complement to human creativity, not a replacement.

Human researchers need to always be at the center of the innovation process, using AI tools to enrich their work, but never cede control of creativity, vision, or strategic direction to algorithms.

The era of artificial intelligence has arrived, but we still need the rare and powerful spark of human curiosity and courage, which can never be reduced to a machine learning model.

This is something we cannot ignore.

Original source:

1.https://venturebeat.com/ai/heres-the-one-thing-you-should-never-outsource-to-an-ai-model/

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