Tools & Platforms
Generative AI vs. regenerative AI: Key differences explored

Like many types of technology, artificial intelligence isn’t a single, uniform entity. There are different types of AI, and each has a different way of working, a different purpose, and a different effect on business operations and processes.
Generative AI (GenAI) has become well-known in recent years and is now commonly found among all types of technology users. With GenAI, users can easily summarize, create text and images, and get knowledge-based responses to prompts.
Regenerative AI, however, is a lesser-known and emerging concept. Rather than focusing on creating new content, regenerative AI emphasizes continuous self-improvement, adaptability and autonomous system optimization.
What is generative AI?
GenAI is a type of AI that generates new content such as text, image, audio and video. That new content is derived from patterns that a GenAI model has learned from training data.
The training process involves self-supervised learning on millions to trillions of data points, enabling models to generate contextually relevant and creative outputs through natural-language interfaces. GenAI uses deep learning, generative adversarial networks (GANs) and transformer-based AI architectures such as large language models.
Key use cases for generative AI include the following:
- Content summarization. It can summarize different types of content.
- Text generation. It can write any type of text-based content — including articles, reports and marketing copy.
- Image and video creation. It can produce any type of image, as well as video content.
- Music and audio. It can compose original music and generate voiceovers.
- Chatbots and virtual assistants. It commonly powers chatbots and virtual assistants, providing users with natural language interfaces to access information.
- Code generation. It can assist developers with code suggestions and automate software development.
There is an ever-growing list of GenAI tools, including the following:
- ChatGPT. The most widely used GenAI tool, OpenAI’s ChatGPT provides a conversational AI interface for content generation and Q&A.
- Gemini. Google’s Gemini is an advanced family of multimodal AI models that helps users summarize and generate content.
- Google AI Overviews. The Google search engine integrates GenAI-powered technology to provide clear and succinct answers to user queries. These AI Overviews typically appear at the top of search results.
- Midjourney. While there is no shortage of text-to-image generation tools, one example is Midjourney, which lets users create any type of image from a simple text prompt.
- GitHub Copilot. GitHub Copilot provides AI-powered code completion and suggestions.
GenAI is having a widespread effect across multiple industries, including the following:
- Media and entertainment. GenAI creates content, composes music and assists with video production.
- Application development. AI-powered development tooling is making it easier to build applications.
- Healthcare. It supports drug discovery, medical imaging and personalized medicine.
- Finance. It automates reporting, fraud detection and customer service.
- E-commerce. It offers personalized marketing, product design and customer engagement.
What is regenerative AI?
Regenerative AI is an emerging area of AI development with models and platforms that regenerate — or self-repair — optimize and adapt over time. This is all done without any human intervention.
The basic idea is to mimic the ability of biological organisms to adapt to changes in the environment. With biological organisms, changes in response to various factors are sometimes a function of evolution. With technology, there is an attempt to follow the same process using evolutionary algorithms, which are a subset of evolutionary computation.
Regenerative AI also uses multiple techniques that somewhat mirror how humans learn and think. A couple of techniques include the following:
- Reinforcement learning. Reinforcement learning trains models to take desired actions by rewarding positive behaviors and punishing negative ones.
- Neuromorphic computing. Neuromorphic computing techniques are a core element of regenerative AI, providing mechanisms that attempt to work the same way as the human brain with neurons and synapses.
The effect of self-repair capabilities
The self-repair capability of regenerative AI is one of the most noteworthy aspects of the technology and has the potential for a significant effect on the AI-technology landscape.
Instead of requiring manual, human intervention to fix an issue or fine-tune and optimize, self-repair handles that automatically. It reduces or eliminates the need for hands-on human maintenance, which has the potential to be particularly valuable in remote or hazardous environments where human intervention is limited. Self-repair will also enhance overall AI system reliability, reduce downtime and reduce operational costs.
Regenerative AI has several capabilities, including the following:
- Self-repair. It can detect and fix errors or inefficiencies autonomously.
- Process optimization. It can identify and correct inefficient workflows.
- Continuous learning. It can adapt to new data and environments in real time.
- Fault tolerance. Thanks to self-repair, regenerative AI models are fault-tolerant.
While currently still in the early stages of development, regenerative AI has potential for a variety of applications, including the following:
- Robotics. It is ideal for robotics, where systems can self-diagnose and fix malfunctions.
- Autonomous vehicles. It could be used to help autonomous vehicles adapt to changing road conditions.
- Cybersecurity. Regenerative AI could be used to help counter new cyber threats in real time.
- Electricity distribution. It could power smart grids that dynamically optimize energy use.
- Remote locations. In remote locations where connectivity is limited, its ability to self-repair would be extremely useful.
Differences between generative and regenerative AI
While both generative and regenerative AI fall under the umbrella of artificial intelligence, they operate on different principles. The following table summarizes their key differences:
Aspect | Generative AI | Regenerative AI |
Definition | Generates new content based on training data. | Can self-repair, adapt and improve over time. |
Core technology | Transformer-based neural networks, GANs and diffusion models. | Reinforcement learning, evolutionary algorithms and neuromorphic computing. |
Learning approach | Static training on massive datasets with periodic fine-tuning. | Continuous learning through real-time feedback and experience. |
Maintenance needs | Requires human intervention for updates and troubleshooting. | Self-maintains through autonomous error detection and correction. |
Output focus | Creative content (text, images, code and audio). | System improvements and adaptive responses. |
Market maturity | Wide commercial deployment in 2025. | Currently in experimental stage with limited practical applications. |
Future trends for generative and regenerative AI
There is much to look forward to for both generative AI and regenerative AI.
Trends show several future developments for generative AI, including the following:
- Agentic AI. GenAI is moving in a somewhat autonomous direction already with the growth of agentic AI, which can act and connect to different systems on behalf of users.
- Multimodal models. GenAI models are going multimodal, with single models able to understand and generate text, audio, images and video.
- Regulatory initiatives. There is a growing emphasis on addressing ethical concerns, such as user privacy and ensuring responsible use.
Regenerative AI also shows trends toward future developments, including the following:
- Transition from theoretical to practical. Regenerative AI has some ground to cover before it will be widely available and practical to deploy. In the coming years, the technology is expected to mature as computational hardware, software and algorithms improve.
- Advancements in neuromorphic computing. New forms of neuromorphic computing hardware, including silicon hardware, will be a key step in future development.
- Integration with the internet of things and edge computing. As the technology matures, it will find a natural fit in internet of things and edge computing deployments, providing the ability to self-optimize to changing conditions in real time.
Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.
Tools & Platforms
Tribal technology conference kicks off Monday with focus on hospitality, cybersecurity, and AI — CDC Gaming

The 26th annual TribalNet Conference & Tradeshow kicks off Monday in Reno. This year’s event has a heavy focus on gaming and hospitality technology on the first day, then a week-long emphasis on cybersecurity.
The conference at the Grand Sierra Resort runs through Thursday. It attracts IT professionals, gaming and hospitality executives, and others within tribal government operations, who discuss transformational technologies.
Cybersecurity has been a big focus in Nevada, which sustained a ransomware attack in late August. It impacted state offices, websites, and services and forced temporary, but ongoing, closures of offices.
Cyberattacks continue to plague tribal gaming operations. Since the pandemic, tribal casinos around the country have been temporarily shuttered due to the attacks.
“Plenty of attacks continue to cause issues in the cyber world,” said Mike Day, founder and executive director of TribalHub, which puts on the conference. “We’ve integrated best practices of what tribes are doing and we’re watching our Tribal ISAC (The Tribal Information Sharing and Analysis Center) grow, which is all about cybersecurity of cyber professionals by tribes for tribes. That communication among tribes is a game changer. They’re sharing information about threats much more quickly.”
The threat of cyberattacks is getting more complicated with the progression of artificial intelligence, Day said. These include impersonations of executives and identity theft aided by AI. Phishing attempts are more difficult to detect.
“A lot of people are rebranding well-known brands in their phishing attempts and these attacks are devastating,” Day said. “There are new ways of having to think about how to protect your employees and organization. No one is immune from this – governments, companies, and individuals.
The gaming and hospitality track has four sessions, three on Monday: cashless wallets and best practices to manage and succeed; what’s new with casino gaming systems; how to create the best customer digital experience; and emerging technology in gaming and hospitality and what the future may bring.
Panelists represent gaming-system leaders at Aristocrat, IGT, Light & Wonder, and CasinoTrac.
“We have the big gaming-systems companies here and we’re talking about what they’re doing to prepare casinos for the future,” Day said. “We’re asking them some AI and cybersecurity questions as well; they’re important for helping organizations drive new revenue. Technology is a critical piece of all your operations. If you’re more efficient and saving money in some way, it’s probably got a huge technology component. If you’re making new money, it almost assuredly has a huge technology component to it. That’s the message we’re trying to get across.
“People need to think about technology differently. It’s not just something happening in the back room adding up numbers,” Day said. “It’s driving revenue and saving money. It didn’t always do that. That’s why it’s important to have a strategic technology plan, whether you’re a CEO or CIO or any of the leaders from gaming and hospitality organizations.”
TribalNet is expecting its largest attendance in history and largest tradeshow floor ever, Day said. People are recognizing that it’s not just an information technology conference, but an event that’s driving where their organizations are going in the future.
More than 700 people are expected to attend, along with nearly 250 exhibitors. Combined, there will be 1,700 to 1,800 people or more at TribalNet.
Tools & Platforms
Beijing Bets on AI to Strengthen Ties with the Global South

By marking the United Nations Day of the Developing Global South on September 12, 2025, China emphasizes the need to develop and coordinate new and innovative AI programs and policies to assist developing countries in the Global South. China believes that the rise of the Global South represents a tremendous advance in the development of human society. The countries of the Global South are strong promoters and defenders of world peace and will play a major role in building a stable, multipolar world. Advanced AI technology, under China’s leadership and support, can play a significant role in strengthening interconnectedness among countries in the Global South.
Reflecting China’s growing role in shaping global governance standards for emerging technologies, Chinese Premier Li Keqiang announced a proposal to establish an International Organization for Artificial Intelligence Cooperation on July 26, 2025, in his opening remarks at the World Artificial Intelligence Conference in Shanghai. Beijing highlights its focus on engaging countries in the Global South as one of the most successful aspects of China’s Belt and Road Initiative. China has pledged to provide its technical expertise, products, and services, as well as its digital infrastructure, to countries that lack the capabilities to engage in the digital revolution.
The importance of China’s support for developing countries in the Global South in advanced AI technology and techniques comes after the global AI race witnessed a remarkable shift in recent years, particularly with China’s significant progress, represented by companies such as DeepSeek and Alibaba developing advanced and highly efficient open-source systems. This reflects China’s accelerating efforts to narrow the technological gap with the United States, particularly in developing technologies whose performance approaches the capabilities of the human mind, with the help and support of China’s partners in the Global South.
China, as the world’s largest developing country, belongs to the Global South and always shares the same destiny. It is an active advocate and important participant in South-South cooperation using advanced technology and AI innovation policies. China has declared that it will always closely unite with developing countries in the Global South to promote the building of a community with a shared future for humanity using advanced AI mechanisms in the Global South.
The AI divide may overtake the traditional digital divide. Which (modules computing power, governance, ethics) should be prioritized in the “South-South knowledge package” that global South developing countries most urgently need under Chinese support and supervision? Currently, global AI governance faces critical challenges, especially for developing countries in the Global South. The most prominent of these is the “digital divide between the South and the rest of the world.” Some developing countries and regions suffer from incomplete digital infrastructure, insufficient skills training, and a lack of digital resources. By the end of 2024, 2.6 billion people worldwide will still be offline. If this digital divide is not urgently bridged, the development gap between the North and the South will deepen, severely constraining global economic growth.
Artificial intelligence has witnessed tremendous progress in recent years, and countries around the world are investing heavily in infrastructure, education, and innovation. China is setting global standards for AI adoption, pumping billions of dollars into data centers and research centers for development using AI mechanisms and technologies. The media and technology ecosystems of the Global South, guided by the United Nations 2025 Development Goals, can work together to unleash the potential of AI to bring developing countries of the Global South together, led by China.
AI can create platforms for countries in the Global South to share and support each other with information, as information exchange is crucial for these countries to unite and support each other. Besides, the modules’ computing power, governance, and ethics should be prioritized in the “South-South knowledge package” that developing countries most urgently need.
Recently, the 2025 World AI Conference and High-Level Meeting on Global AI Governance launched the “Action Plan for Global AI Governance,” which proposed six core principles: orientation toward good and serving the people, respect for sovereignty, orientation toward development, secure governance, fairness and inclusion, and openness and cooperation.
Artificial intelligence impacts the developing Global South, where the lack of basic digital infrastructure and energy poses significant challenges to the spread of AI in countries of the Global South. AI requires stable internet connectivity, access to digital technologies such as smartphones and computers, and reliable electricity, which are not widely available in developing countries of the Global South.
The use of artificial intelligence and digital computing technologies for cooperation among developing countries of the Global South under Chinese support and encouragement represents one of the mechanisms that enhances the role of think tanks in addressing challenges by utilizing big data to predict future trends, thus enhancing governments’ decision-making capacity. Given the common challenges facing countries of the Global South, think tanks, through close collaboration, can contribute to providing important insights and solutions to common problems, formulating global public policies that are more equitable to the Global South, and placing the issues and problems of the Global South on the list of global priorities.
Developing countries of the Global South can collaborate with China through the use of AI technologies, including:
1) Working to build joint projects among countries of the Global South: These projects rely on the use of AI technologies to address common challenges by leveraging shared expertise to support decision-making.
2) Using AI to enhance government communication among countries of the Global South, both internally and externally: By building shared digital platforms through which think tanks can listen to diverse opinions and build bridges of communication between the peoples of the Global South.
Based on the above analysis, we understand the reasons behind Beijing’s support for AI technology in the Global South, with the expectation that strengthening global governance rules for AI technology, led and supported by China, will enable Chinese companies to establish a presence in international markets and compete for a share of a market expected to exceed $4.8 trillion by 2033.
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