Teaching AI to Write Like Us: The Art of Refining AI-Generated Text 

By Richard Sebaggala

 

 

While traveling on a bus, I had a rare moment of uninterrupted reading.I found myself drawn to an article discussing a fascinating study on the differences between AI-generated and human-written text. Conducted by researchers at Carnegie Mellon University, the study attempted to pinpoint stylistic differences in how large language models (LLMs) like ChatGPT and Llama construct sentences compared to human writers. What made it particularly compelling was its relevance to an ongoing shift in the conversation about generative AI.  Not long ago, discussions about AI in writing were dominated by concerns over misconceptions, academic plagiarism, and ethical dilemmas. However, the debate has evolved. Today, a pressing question is: how much content is AI-generated, and can we truly differentiate it from human writing?

 

The study findings were fascinating. The study revealed that AI-generated writing tends to rely more on structured, information-dense phrasing, with certain quirks that set it apart from human expression. For example, LLMs often overuse present participle clauses (e.g., "Bryan, leaning on his agility, dances around the ring."), favor abstract nominalizations (e.g., "implementation of the policy" instead of "the policy was implemented"), and show strong preferences for specific vocabulary choices—words like “camaraderie,” “tapestry,” and “palpable” appeared far more frequently than in human writing.

 

While these insights are valuable, they raise an important question: Are these differences a limitation of AI, or simply a result of how we use it? A closer look at the study's methodology suggests that some of its conclusions may be shaped by underlying assumptions rather than definitive proof that AI cannot adapt to human writing styles.

At first glance, the study's results seem to confirm a widespread belief that AI-generated writing lacks the nuance and adaptability of human expression. However, a critical examination reveals several methodological flaws that may have influenced its conclusions:

1. A Narrow View of Writing Contexts

n The study evaluated AI writing using specific genres—TV scripts and academic writing—but did not consider other common forms of human writing, such as personal essays, journalism, or business communication.

n Human writing is highly diverse, shaped by context, purpose, and audience. Did the researchers test AI’s adaptability across a broader spectrum of writing styles?

2. The Lack of Iterative Refinement

n One key difference between human and AI writing is the revision process. Writers rarely produce polished work in a single draft; they refine, edit, and restructure.

n The study did not test whether AI writing could become more human-like through iterative refinements, where users provide feedback and make incremental improvements.

3. Prompting Matters More Than We Think

nAI models do not generate text in isolation; they respond to prompts. Poorly designed prompts often lead to unnatural or rigid outputs.

n The study does not clarify whether the AI was prompted in a way that encouraged contextual adaptability. Could different prompt engineering techniques yield more human-like writing?

4. Comparing AI to Expert Writers vs. the Average Writer

n If the AI-generated text was compared to professional authors or seasoned academics, it would naturally fall short in style and depth.

n A fairer comparison would involve a broader range of human writing abilities, including that of everyday writers, students, and non-expert authors.

These gaps suggest that while the study highlights genuine AI tendencies, its conclusions may not fully reflect AI’s potential when used strategically.

Instead of dismissing AI-generated writing as inherently flawed, I decided to test whether ChatGPT could refine its own writing to align more closely with human standards. I asked ChatGPT o1, an advanced reasoning model, to generate a structured prompt for improving AI-assisted writing based on article concerns. The results were impressive:

"I have an AI-generated article that I want to refine to make it more natural and closer to human writing. Please follow these steps to adjust the style:

1. Adapt Sentence Structure

· Avoid overuse of present participle clauses and vary sentence beginnings.

· Mix short and long sentences to create a natural rhythm.

2. Balance Voice and Tone

· Reduce excessive nominalizations (e.g., replace "implementation of the policy" with "the policy was implemented").

· Adjust passive voice usage where necessary to improve clarity.

· Adapt the tone to match the context—formal for academic writing, conversational for blog posts.

3. Diversify Vocabulary

· Replace overly sophisticated or AI-favored words (e.g., "camaraderie," "tapestry," "palpable") with more natural alternatives.

· Use synonyms and rephrase sentences to avoid unnecessary repetition.

4. Enhance Natural Flow

· Use smooth transitions to guide the reader logically through ideas.

· Ensure paragraphs connect seamlessly rather than feeling like separate, disjointed sections.

5. Simulate Human Revision

· Edit the text progressively, focusing on clarity, conciseness, and logical progression.

· Introduce slight variations in sentence structures, as humans naturally do when revising their work."

Applying this prompt transformed the AI-generated text significantly. Instead of rigid, overly structured sentences, the writing became more dynamic and fluid. Ideas flowed naturally, and the vocabulary felt more organic. More importantly, the revision process mirrored how human writers refine their work—by iterating, editing, and fine-tuning for clarity.

 

This experiment revealed a key takeaway: AI is not inherently incapable of writing like humans—it simply needs structured guidance and refinement. Instead of seeing AI as a threat to human writing, we should embrace it as a collaborative tool. Whether in academia, professional writing, or creative storytelling, AI can enhance writing quality when used thoughtfully. However, it should not be treated as a shortcut for bypassing the critical thinking, creativity, and revision processes that make writing truly human.

 

If you use AI in your writing, try refining its output using structured prompts like the one outlined here. The best writing—whether human or AI-assisted—is always a result of careful revision. AI may not naturally write like us yet, but with the right approach, we can guide it to produce work that is technically correct, genuinely engaging, nuanced, and human-like.

 The Secret to AI Proficiency: Why Repetition Is the Key to Mastery

By Richard Sebaggala

Jensen Huang, CEO of NVIDIA, once said: "You are not going to lose your job to AI, but rather to someone who uses it." This statement gets to the heart of what separates those who succeed in the age of AI from those who fall behind: a willingness to learn, adapt, and practice.

Since I started delivering training on the use of generative AI in research, I’ve met countless people who want to realize the full potential of AI. Many are looking for shortcuts — tools that promise immediate productivity gains with minimal effort. What is often overlooked, however, is the dedication and time required to achieve true proficiency. My journey with generative AI has been anything but instant. It has been a process of structured learning and deliberate repetition, sometimes requiring hours of practice to master just one tool.

 

The remarkable results I have achieved are not due to my talent alone. They came about because I understood and embraced the value of repetition — what Stan Goldberg aptly calls "neurological secret sauce" in his studies on memory and learning. Repetition may sound mundane, but it's the cornerstone of mastery in any field, whether it’s delivering a flawless speech, performing a symphony or, as in my case, refining workflows with generative AI.

Goldberg explains that the difference between beginners and experts lies in the strength of their "memory layers". When you start a new task, your first actions will feel clumsy and awkward. Think back to the first time you typed on a keyboard or learned to drive a car. But over time, your neural connections will strengthen, and the process will become second nature.

 

My first interactions with AI tools like ChatGPT were far from seamless. I often questioned my prompts, overlooked important features and misinterpreted the results. But through repetition, I was able to create a mental "blueprint" for using these tools. Today, writing structured prompts, refining results based on feedback, and integrating AI-generated content into research workflows feels as natural as typing.

Each iteration has strengthened my skills. Like a carpenter who masters his tools after thousands of uses, I have honed my expertise step by step. However, repetition alone is not enough. As Goldberg points out, training must be precise. Michael Phelps’ coach famously advised him not to practice strokes that deviated from perfection. And why? Practicing mistakes only reinforces bad habits.

 

This principle also applies to AI. In my practice sessions, I was not content to randomly perform queries. I critically examined the results, identified gaps and refined my techniques. For example, when l was practicing  literature review tools such as Avidnote or Elicit for systematic review , l compared the AI-generated summaries with those written by humans to identify weaknesses and adjust my prompts. This focus on "perfect practice" helped me internalize the subtleties of using AI effectively, which led to consistently better results.

 

Malcolm Gladwell popularized the idea of the "10,000 hour rule" in his book, in which he emphasizes that mastery requires an extraordinary amount of deliberate practice. Even though AI tools are designed to simplify tasks, maximizing their potential requires a similar commitment to practice. True mastery — the kind that allows you not only to use AI, but to realize its full potential — is based on time, effort and intention.

 

In my experience, the key to unlocking the transformative potential of AI is to regularly invest hours in learning and refining AI workflows. Every hour you spend practicing structured prompts, analyzing feedback, and applying the tools to real-world problems adds to the cumulative expertise that separates a proficient user from a casual user. Just as musicians, athletes and writers achieve greatness through thousands of hours of practice, AI users must approach their craft with the same dedication.

 

Even mastery requires nurturing. Without reinforcement, skills diminish. Jascha Heifetz, the legendary violinist, practiced scales daily despite his world-renowned talent. Similarly, I regularly engage with AI tools to ensure my knowledge remains up to date.

 

When tools like Chatgpt, Gemini and Avidnote release updates, I make it a priority to practice with their new features. I subscribe to several AI platforms to keep up to date with new developments in the AI industry. This continuous engagement ensures that I stay up to date and adapt to advances rather than becoming complacent.

One of the most valuable lessons from Goldberg is to "practice slowly". In the early days of learning AI tools, I deliberately slowed down the pace to focus on understanding each step. By resisting the urge to rush, I built a solid foundation that eventually allowed me to work faster and more efficiently.

For example, before I ventured into complex tasks such as automating literature reviews, I started with the simple creation of notes. This deliberate progression gave me the confidence and competence to tackle larger, more complicated projects with fewer mistakes.

A common mistake many learners make is practicing in an isolated environment. Goldberg’s analogy of delivering a speech in a quiet room — only to run into trouble in a noisy room — is very apt. Mastery requires the application of skills in dynamic, real-world environments. In the early days of learning AI, I initially practiced in a controlled environment and experimented with tools in private. But to really hone my skills, I decided to apply them in live workshops, group discussions and collaborative research projects. These real-world applications tested my adaptability and boosted my confidence so that my skills were not just theoretical, but practical.

Repetition is not the enemy of creativity, it is its foundation. Without structured practice, even the most innovative ideas fail to be realized. Just as athletes and artists rely on repetition to excel, mastering generative AI requires deliberate, repetitive practice. For those looking for shortcuts, this is a reminder: mastery isn’t about finding the perfect tool, it’s about cultivating the right habits. Take the time to practice, refine your techniques and make repetition your own. The results will speak for themselves.

As Stan Goldberg eloquently puts it, repetition reinforces the "blueprints" that guide our actions. Mastery is not a destination, but an continuous journey shaped by the time and effort we invest every day.

In the words of Jensen Huang, your job is not in danger because of AI, but if you do not learn how to use it, it's in danger. Embrace the process and let repetition and a commitment to mastering the 10,000-hour rule be your best allies in mastering the tools that will shape the future.

Navigating the AI frontier: How Deep Research is shaping the future of academia

                                               By Richard Sebaggala

 

Over the past few days, the AI community has been abuzz with news about OpenAI’s latest breakthrough: Deep Research. As someone who has long advocated for the integration of AI into academic work, I have watched this development with both excitement and frustration. As AI continues to evolve at breakneck speed, many in academia are hesitant — unsure whether to embrace or fully incorporate these new technologies.

In almost every academic setting where I have presented on the transformative impact of AI in education or research, I have observed that the audience tends to fall into three main groups: about 10% are Early Adopters — curious technophiles who eagerly integrate new AI tools into their workflows; another 10% are Resistant Users who vehemently oppose AI, prefer traditional methods and fear displacement of their jobs; and the remaining 80% are Pragmatic Adopters who are ready to use AI but are still waiting until they see clear, reliable benefits and a user experience that fits seamlessly into their existing processes.

The latest innovation in AI research assistants, Deep Research, is already challenging many of these entrenched views. Although the price for ChatGPT Pro subscribers is currently very high at $200 per month, there is hope for those put off by the cost, as OpenAI has announced plans to improve access to Deep Research with lower pricing and an expanded offering.

Deep Research is no ordinary chatbot that provides quick answers. It mirrors the traditional research process with a structured, multi-step approach that transforms it from a mere assistant to a powerful, autonomous research engine. Imagine submitting a query or prompt and having an AI that not only clarifies your query with insightful follow-up questions but also scans through hundreds of sources, from academic papers to breaking news. Within minutes, it synthesizes this information into a coherent, citation-rich report that occasionally reaches the depth of a doctoral dissertation.

 

OpenAI’s commitment to broader accessibility makes it even more exciting. While Deep Research debuted as an exclusive premium tool, OpenAI has already signaled plans to democratize its capabilities. Future pricing tiers promise to make this transformative technology accessible to everyone: ChatGPT Plus will provide access for as little as $20 per month, while Team, Edu and Enterprise plans will further expand availability. Even free users can expect a taste although with limited scope as usual.  OpenAI co-founder Sam Altman has indicated that the initial plan is to offer 10 uses per month for ChatGPT Plus and 2 per month for users of the free version, with the intention of expanding these limits over time. For those who have always wished that cutting-edge research tools weren’t so out of reach, these developments offer a promising glimpse into the future.

 

But as impressive as it all sounds, early adopters have also pointed out legitimate concerns. AI sometimes struggles with context and occasionally misses nuances that are important for in-depth scientific investigation. It can miss the latest developments or sometimes confidently generate information that is not entirely accurate. This phenomenon is technically called "hallucination. It also does not always distinguish between reliable and credible sources and those of dubious quality. While these problems are important, they are not insurmountable. The real challenge is not that AI could replace human researchers but rather whether the scientific community is ready to integrate such a tool while rigorously addressing its shortcomings. Moreover, the existence of these concerns underscores the need for responsible use of AI research assistance and requires fusion skills, such as intelligent questioning and critical thinking. As experts in AI ethics point out, these fusion skills are crucial to ensure that AI results are thoroughly vetted, cross-checked, and appropriately contextualized before being incorporated into scientific work.

This is what H. James Wilson and Paul R. Daugherty (2024) refer to “fusion capabilities" - intelligent questioning, integration of judgments, and cross-training. In their words, “In the future, many of us will find that our professional success depends on our ability to elicit the best possible performance from and learn and grow with large language models (LLMs) like ChatGPT. To succeed in this new era of AI-human collaboration, most people will need more of these ‘fusion skills'." These skills enable researchers to use AI not just as a passive tool, but as an active collaborator, ultimately strengthening the rigor of any research process.

Universities and research institutions are still debating the role of AI in science. Some fear that over-reliance on machine-generated insights could dilute academic rigor. However, in a field developing at breakneck speed, there is a risk of missing transformative opportunities by resisting innovation. AI is advancing exponentially, and while some academic circles cling to tradition, tools like Deep Research are foreshadowing a future where the role of the researcher shifts from simply retrieving data to the crucial tasks of review, synthesis, and interpretation. As the saying goes, “AI gives you what you already know" - it’s up to us to engage with that knowledge, question it, and build on it.

In conclusion, the academic world is now at a crossroads: it must adapt and integrate these powerful tools or risk falling irretrievably behind in an era where AI-driven insights are reshaping industries, policy, and scientific discovery. Deep research is just the beginning — a harbinger of the profound change on the horizon. It’s time for academia to shed its denial, jump on this AI tsunami, and harness the potential of AI to advance research while preserving the crucial human touch that makes research scholarship truly exceptional.

Breaking the Cycle: How Africa Can Avoid Digital Dependency in the AI Revolution

By Richard Sebaggala

For decades, Africa has been a passive consumer of technology, relying on innovations developed elsewhere. The rise of artificial intelligence (AI) offers a transformative opportunity, but the continent faces a critical choice: remain a consumer or actively shape its AI future.

The World Economic Forum's Future of Jobs Report 2025 predicts a net increase of 78 million jobs worldwide by 2030, largely due to AI. However, this change also carries risks: 44% of workers will need to be retrained or upskilled within five years. In Africa, where youth unemployment is already a pressing issue, these figures underline the urgency for the continent to develop its AI capabilities. The Future of Work in Africa report warns that without the development of AI on the ground, inequality could increase, job displacement could rise and digital dependency could deepen.

Digital dependency has long characterized Africa's technological landscape. Global companies dominate sectors such as telecommunications and financial services, leaving the continent reliant on external expertise. While Africa provides the data that drives global AI models, it retains little value from this contribution. Talented researchers and engineers often seek opportunities abroad, widening the gap. Governments often cite budgetary constraints as the reason for their inaction. However, the emergence of companies like DeepSeek challenges this narrative and proves that a focused strategy and commitment can lead to significant results, even without large financial resources.

To break free from dependency, Africa needs to invest in AI research and development. Universities and research institutions should lead innovation, supported by governments that create favorable policies. Public-private partnerships can provide critical funding, while local AI centers can nurture startups. Countries such as Nigeria, Kenya and South Africa already have growing AI talent, but much potential remains untapped due to insufficient support. The World Economic Forum emphasizes that Africa's young population, which is expected to reach 830 million by 2050, represents both an opportunity and a challenge. With the right investments, Africa could become a major player in the global AI economy. Conversely, it risks further marginalization if it does not act decisively.

Some African start-ups have already shown what is possible with the right support. InstaDeep, an AI company founded in Tunisia, has made a name for itself with its solutions in the fields of logistics, healthcare and finance. South African company DataProphet has improved manufacturing efficiency through AI, while Ghanaian company MinoHealth AI Labs is developing diagnostic tools to address the continent's unique healthcare challenges. These examples show that AI innovation in Africa is not only feasible, but also scalable with the right investment.

Talent development is critical to Africa's AI ambitions. The continent needs to equip its youth with the skills required for AI research and deployment. AI should be integrated into university curricula, research programs should be encouraged and hackathons should drive innovation. It is equally important to retain talent. Well-paid opportunities must be created to prevent skilled professionals from seeking work abroad. If action is not taken, Africa risks losing its talent and further marginalizing itself in the global AI ecosystem.

Africa cannot wait for global companies to offer AI solutions to its unique challenges. Sectors such as agriculture, healthcare and finance could benefit greatly from locally tailored AI innovations. The development of AI models trained with African data will lead to better insights and services. However, to achieve this, Africa must take control of its data. Implementing a data sovereignty policy is essential to ensure that Africa's digital capital contributes to its own progress rather than enriching foreign companies.

Strong policy and regulatory frameworks are needed to guide the development of AI. African governments must prioritize local capacity building over creating deeper dependencies. Regulations should strike a balance between innovation and ethical considerations and align the use of AI with Africa's social and economic priorities. Trade policies must protect local markets from exploitative practices that siphon value without reinvesting in the continent.

International partnerships can further accelerate the development of AI in Africa. Collaboration with global research institutions and technology companies can enhance local capabilities while maintaining control over progress. Countries like Rwanda are already working with leading global AI companies to establish research labs and innovation centers. More African countries should seek strategic partnerships to leverage international expertise while maintaining control over AI development locally.

No single African country can compete with the global AI power centers on its own. The fragmentation of AI development on the continent weakens its potential. Joint efforts, such as the formation of an African AI consortium pooling resources, expertise and funding, would allow for more ambitious projects and greater knowledge sharing. Intra-African AI trade should be encouraged, favoring solutions developed for African markets over foreign alternatives. AI conferences and networking platforms could foster stronger links between African researchers, entrepreneurs and policy makers, ensuring that innovation does not take place in isolated areas but as part of a broader movement.

Africa also needs to address the ethical dimensions of AI adoption. AI models developed outside Africa may reflect biases that do not align with African values or realities, leading to inequitable outcomes in areas such as hiring and law enforcement. Local AI governance structures need to be put in place to ensure ethical AI deployment, with transparency, fairness and accountability at the forefront. A proactive approach to AI ethics will protect African societies from the risks associated with poorly regulated AI models and increase confidence in indigenous AI solutions.

The AI revolution is developing rapidly and Africa is at a crossroads. It can either remain dependent on foreign technology or take bold steps to become a major player in AI development. The example of DeepSeek has shown that the barriers to AI innovation are not insurmountable. Budget constraints are real, but they are not the deciding factor in whether Africa succeeds or fails in AI. Rather, it depends on whether the continent is willing to make the strategic investments, build the necessary infrastructure and develop policies that promote AI as a tool for economic transformation and not as another path to dependency.

The Future of Jobs Report 2025 emphasizes that AI will determine economic competitiveness in the coming decade and that countries that do not invest will be left behind. The future of AI in Africa is not predetermined. It will be shaped by the decisions made today. Will Africa continue to watch from the sidelines or will it seize the moment and determine its own technological destiny?

 Is DeepSeek AI a Blessing or a Curse for the Generative AI Industry?

By Richard Sebaggala

DeepSeek AI's entry into the generative AI race has sparked debate about what this means for the industry, competition, and the future of artificial intelligence. Some see it as a sign of progress, a force that will drive innovation and reduce costs. Others fear that this is another step towards an AI arms race that will take us further into uncharted territory where the risks outweigh the benefits. As economists, it is impossible to ignore the fact that this is not just a technological shift, but also an economic and geopolitical one. The big question is whether the arrival of DeepSeek AI is good for the world or whether we are simply moving towards a future that we haven’t fully thought through.

In most industries, competition is a force for good. It leads to better products, lower prices, and better accessibility. The emergence of DeepSeek AI challenges the dominance of OpenAI, Google DeepMind, and Anthropic and brings a new player into what is starting to feel like an exclusive club.  As Apple and Android went head-to-head, smartphones got better and more affordable. As budget airlines entered the market, flying became accessible to more people. DeepSeek AI could have a similar effect: AI tools are becoming cheaper, smarter, and more widely available. In addition, the accessibility of AI is a major concern. Currently, developing AI models at scale is costly, and access to leading technologies is often limited. If DeepSeek AI aggressively pushes into the market, it could force incumbents to rethink their pricing strategies and make AI more affordable for small businesses, researchers, and developing countries. This democratization of knowledge and computing power is a compelling argument for more competition.

However, more competition does not always lead to better results. It can lead to reckless behavior when companies prioritize speed over safety. The AI industry is already in the process of developing larger models without fully understanding their long-term implications. The entry of DeepSeek AI means that all players may feel pressured to accelerate their development cycles, which could lead to the use of unfinished technologies and the neglect of ethical considerations. The nature of the generative AI industry raises unique concerns. Unlike traditional markets, which typically involve the exchange of goods and services between buyers and sellers, the impact of AI extends far beyond consumers and businesses and can reshape entire economies, influence policy decisions, and change labor markets. This raises critical questions about how the market for AI will evolve, particularly in terms of its structure and competitive dynamics.

A key question is whether the emergence of DeepSeek AI signals the development of a truly contestable market or whether the industry will continue to be dominated by existing players. In economics, a contestable market refers to a market where entry and exit are relatively easy, meaning that new companies can challenge incumbents without major obstacles. In such markets, even a monopoly behaves competitively because the threat of potential entrants keeps prices fair and innovation high. In the AI industry, however, the presence of first-mover advantage —when early entrants establish a strong position by developing superior technologies, amassing big data, and building brand awareness — can solidify the control of existing giants such as OpenAI, Google DeepMind, and Anthropic.

DeepSeek AI’s strategy may be similar to OpenAI’s approach, which favors rapid adoption of its technology over immediate profitability. This is a common tactic in technology markets, where early market dominance can be more valuable in the long term than short-term revenue. However, the AI industry is characterized by network effects, i.e. the value of a service increases the more people use it. This and the huge demand for data and computing resources make it difficult for new entrants to compete on an equal footing with established market players who already have access to huge data sets, powerful computing infrastructure, and strong industry partnerships. The key question remains: Does DeepSeek AI’s entry signify a real power shift in the AI industry, where competition is thriving and innovation is flourishing? Or is the company merely adding another player to an already established system dominated by a few tech giants that are more likely to maintain the status quo than change it?

Furthermore, fragmentation is a significant risk. If AI development splits into competing ecosystems — one dominated by Western companies like OpenAI and Google, and another led by China’s DeepSeek AI and others — we could end up with two separate AI worlds. Competing AI ecosystems, possibly divided along geopolitical lines, could lead to different standards of safety, ethics, and governance. This is particularly worrying given that AI has the potential to disrupt economies and political systems. If AI development becomes a race for supremacy rather than a collaborative effort for global improvement, it could have detrimental consequences.

The presence of DeepSeek AI is therefore a double-edged sword. It offers the potential benefits of competition — faster innovation, better prices and improved access — but also carries the risks of fragmentation, reckless deployment and a focus on dominance rather than accountability. The key question is not whether DeepSeek AI is inherently good or bad, but whether we can effectively manage the impact of an industry that exceeds our ability to control it. AI is now about power, politics and economics. As history shows, those who set the rules for emerging technologies ultimately determine their impact on the world. We pray and hope that those governing AI will prioritize ethical responsibility, global cooperation, and the collective good over short-term gains and geopolitical rivalry. The future of AI should be shaped not only by competition but by wisdom and foresight.

 A Green Park in a Polluted City: The Economics of Human-AI Collaboration

In an era characterized by artificial intelligence, thriving is no longer about competition, but about finding the right balance. Just as a green park thrives as a tranquil sanctuary in the midst of a polluted city, the partnership between humans and AI thrives on mutual reinforcement. Humans contribute judgment, creativity and context, while AI provides speed, data processing and efficiency. Together, they create something greater than either could achieve alone — a powerful synthesis of creativity and productivity.

But this partnership is not without risks. The misuse of AI has highlighted the dangers of unchecked technological progress. As Susie Alegre pointed out in her article "Don’t Count Out Human Writers in the Age of AI" (January 8, 2025), "academic publisher Wiley shut down 19 journals in 2024 in the face of a flood of fake scientific articles. To err is human, but falsification on an industrial scale is primarily a technological problem. AI has no professional ethics, no soul and nothing to lose — but the people who use it or ask others to use it for them do." This stark example underscores the need for human oversight to uphold ethical standards and protect the integrity of creative and academic work.

Maintaining integrity in a flood of content

The challenge for human authors and researchers is clear: they must maintain their individuality and integrity in a landscape increasingly dominated by AI-generated content. With the rising tide of machine-generated material, the temptation to rely solely on AI is growing. But much like a green park needs dedicated gardeners to maintain its vitality, the creative world needs human judgment and ethics to ensure authenticity and maintain trust.

AI may excel at generating content on a large scale, but it lacks the soul, professional ethics, and accountability that define human creativity. Without human guidance, AI can contribute to a landscape of superficiality, undermining the quality and depth that readers and audiences value.

The power of collaboration: examples of human ingenuity and AI

One of the most powerful aspects of AI is its ability to complement human ingenuity. When AI is used as a collaborator, it can contribute to better results by combining machine efficiency with human creativity.

Take, for example, a journalist working on a report about climate change. Traditionally, gathering data, interviewing experts and crafting a compelling story would typically be labor-intensive and time-consuming. With generative AI, the journalist can now streamline parts of the process while increasing the depth and creativity of their work.

·Data Collect and summarize: AI tools like Avidnote and ChatGPT can sift through hundreds of research articles, extract key statistics and summarize complex findings. This allows the journalist to focus on the most important findings instead of getting bogged down in data collection.

·Idea Generation and structuring: AI can suggest angles or topics for the article, e.g. the impact of climate change on urban flooding, innovative solutions in flood-prone areas and personal stories of resilience. These suggestions stimulate creativity and provide a solid starting point.

·Human Creativity and storytelling: Using AI-generated insights, the journalist weaves emotional narratives, conducts interviews and contextualizes data in a way that resonates with readers. The AI can suggest wording or sentence structures, but the journalist refines the language to fit their unique voice.

·Final Polishing: AI-powered tools like Avidnote help with grammar and readability, while the journalist ensures the content is ethically sound and factually correct.

The result is a thoroughly researched, emotionally engaging article that neither the journalist nor the AI could have achieved alone.

In academia, AI can also increase productivity while maintaining the intellectual rigor of the researcher. For example, a historian studying pre-colonial corruption could use AI to summarize historical texts and identify recurring themes. However, the historian’s expertise interprets these nuances, highlights lesser-known perspectives, and contributes original insights to the field.

The recognition of human expertise

While AI automates routine tasks, the unique contributions of human writers and researchers are increasingly valued. Clients and employers are beginning to understand that quality comes at a price. Whether it's writing compelling narratives, conducting insightful analysis or producing innovative research, human expertise is a premium service in a world inundated with machine-generated content.

Those who combine their unique talents with mastery of AI are leading this change. By using AI as a tool rather than a replacement, they are increasing their productivity and regaining their value. These people are redefining their industries and positioning themselves as indispensable in the age of AI.

A balanced path to the future

The future is not about AI versus humans. It's a story of collaboration. In the polluted landscape of overproduced and overwhelming  AI content, those who master and combine AI with their human skills shine as green parks of excellence. They elevate their work, stand out for their quality, and lead the way to a new era of creativity and productivity.

The lesson of 2025 is profound: the pen — guided by a human mind and augmented by AI — is more powerful than ever before. By embracing AI as an ally, human writers and researchers can amplify their strengths, maintain their authenticity, and push the boundaries of what is possible.

However, it is important to recognize that generative AI is still in its infancy and has significant limitations. One of its biggest weaknesses is its short memory — a limitation that is rooted in the Transformer architecture that drives these systems. This short memory is not a flaw, but an intentional feature, as current AI systems are designed to mimic human intelligence, not surpass it in every way.

But the exciting future still lies ahead. Efforts are already underway to develop AI that not only mirrors human intelligence, but also enhances it in ways that preserve and amplify what makes human thinking unique: our creativity, our ability to learn and adapt, and our capacity for deep understanding. Such advances could mark the beginning of a new chapter in which AI becomes a true partner for human innovation, allowing us to push the boundaries of what we can achieve while remaining deeply rooted in the essence of our humanity.

As we move forward, let us cultivate this balance between human ingenuity and technological advancement. In doing so, we not only increase our productivity and creativity, but also preserve the authenticity and ethical standards that define being human. In this balance lies the promise of a thriving, collaborative world where human excellence flourishes amidst the power of AI.

The future of creativity is not about replacing humans, but enhancing them — our ability to think, adapt and create in a way that matches both our intelligence and the technology we are developing. That's the promise of human-AI collaboration: a partnership where humans are at the center, leading with wisdom and purpose.

From Knowledge to Allocation: How Generative AI is Reshaping the Economics of Work and Learning

In today’s world, where information flows freely and tools like ChatGPT, Avidnote and Gemini are at our fingertips, we are experiencing a seismic shift in the way we approach work, learning and even daily tasks. The Economics of Work and Learning explores this relationship, focusing on how education systems and labor markets interact to shape economic productivity and individual career opportunities. As we look at how this dynamic is being changed by technology, it is important to understand its fundamental impact on personal and economic growth. What once required a high level of expertise-be it analyzing data, interpreting it or composing a professional email- is now streamlined and simplified by generative AI. For researchers, professionals and students, AI has transformed time-consuming processes into quick, manageable tasks, fundamentally changing the landscape of our economy.

Generative AI not only increases productivity, it also signals the transition from a knowledge economy to what I call an allocation economy. In the knowledge economy, success depended on how much information you could acquire and apply. Today, the focus is on how well you use and manage AI-driven resources to accomplish work. This shift suggests that the future of business and professional growth will revolve around resource management rather than pure knowledge acquisition. This shift aligns with the views of AI advocates such as Dan Shipper, co-founder and CEO of Every, who writes the Chain of Thought column. Shipper argues that the future of work depends less on the depth of individual knowledge and more on how effectively we manage the resources available. In this context, the most valuable skills of today or the future include navigating, selecting and orchestrating AI tools to turn ideas into reality.

In an allocation economy, the value of knowledge is shifting from the mere possession of information to the knowledge of how to use that information effectively with the help of AI tools. The real economic advantage is no longer in being an expert on every topic, but in integrating and allocating AI resources to achieve results efficiently. However, this does not mean that knowledge has lost its importance. Rather, it means a recalibration of priorities, where fundamental understanding is complemented by agile, strategic allocation capabilities. Generative AI tools enable users to apply their knowledge more broadly, achieving unprecedented levels of productivity and precision.

Preparing for the allocation economy

Resource management capabilities

With the variety of generative AI tools available today, the ability to evaluate and select the right tool for specific tasks is critical. Students, professionals and educational institutions need to develop a keen eye for resource allocation, prioritizing adaptability over rote learning.

Ethical and responsible use of AI

As AI becomes increasingly integrated into life and work, it is crucial to understand the ethical implications and learn to use AI responsibly. This includes recognizing bias, addressing privacy concerns and promoting transparency in the use of AI. In addition, individuals and organizations need to create frameworks that promote ethical AI practices in their operations.

Collaborative intelligence

AI tools work best when combined with the human mind. Communication, collaboration and critical thinking remain essential as humans interpret, refine and implement AI-generated insights. Educational institutions should focus on fostering these collaborative skills in students and professionals alike.

Continuous learning and adaptation

Generative AI is evolving rapidly. Continuous learning and constant updating of AI advancements are essential to realize the full potential of these tools. It is not only the responsibility of individuals, but also of companies to create an environment that supports continuous learning and adaptation.

A look into the future

As we move into the sharing economy, generative AI holds the potential to democratize productivity by providing tools that promote innovation and efficiency for individuals and companies,organization and professional bodies alike. The challenge is not only to adapt to these tools, but also to orchestrate them skillfully, i.e. to manage resources smartly to maximize impact.

As we embrace this change, the focus shifts from what we know to how we use and manage the resources available to us to build, create and innovate. This techno-economic transition signals a new era in which strategic resource allocation becomes the cornerstone of economic success.

The transition to an allocation economy driven requires a comprehensive assessment of the knowledge production landscape. Educational institutions, as well as individuals and professional organizations, must adapt by prioritizing resource management skills, ethical AI practices, and interdisciplinary collaboration. This shift is essential to ensure that educational and research paradigms remain relevant and effective in addressing the complexities and challenges of an evolving knowledge economy.

To succeed in this new environment, educational institutions must not only incorporate these elements into their curricula, but also foster a culture of continuous learning and adaptability. Individual professionals and their organizations must also be proactive in responding to these changes. By equipping the next generation with the necessary skills to navigate this allocative economy, we can cultivate a workforce that is not only proficient in the use of AI-driven tools, but also able to harness them for sustainable innovation and societal benefit.