Cyber Threats and Risk Predictions for 2025, by AI

I asked five different AI models to make “predictions” on what they consider the top five cybersecurity threats and risks for 2025. Here are the results. Below is a summary of what they reported [for the top five overall] and the measures to be considered. The results are not surprising based on what the industry has seen in 2024 and the overall evolution of AI and its capabilities. I would also say that AI will likely make it easier for cybercriminals to perform all of these attacks.

1.     AI-Powered / AI-Driven Attacks

Cybercriminals are expected to increasingly leverage AI to create sophisticated and scalable attack strategies, including advanced phishing, deepfake-enabled social engineering, and adaptive AI-driven malware. These tactics exploit trust signals by generating hyper-realistic impersonations through AI-generated audio, video, and text. Automating such attacks will amplify their scale and effectiveness, making detection and prevention more challenging for traditional security systems. This could result in faster breaches and more significant financial, reputational, and operational harm.

To prepare, organizations should prioritize advanced training for employees to recognize AI-driven threats and maintain vigilance in verifying sensitive communications through secondary channels. Integrating AI-powered detection systems can help identify anomalies and adaptive threats in real-time, while multi-factor authentication and biometric measures can strengthen identity verification. Regularly updating systems, testing incident response plans, and implementing robust endpoint protections are essential to counter these evolving threats. Strengthening technical defenses and human awareness will be key to mitigating the risks of AI-enabled cyberattacks.

2.     Supply Chain Attacks / Third-Party Risk

As organizations increasingly depend on external vendors, cloud services, and complex software supply chains, cybercriminals are expected to exploit vulnerabilities in these interconnected ecosystems. Compromising a key supplier’s software or managed service can trigger cascading breaches, causing operational disruptions, data leaks, and regulatory violations. These attacks often target third-party software, development tools, and CI/CD pipelines, enabling persistent threats that can remain undetected for extended periods.

The growing complexity of supply chains and reliance on third-party components makes security auditing more challenging, leaving even well-protected organizations vulnerable. High-profile incidents like those involving CrowdStrike and MOVEit underscore the need for robust third-party risk management. The increased use of generative AI in software development further amplifies these risks by introducing new vulnerabilities.

To mitigate these threats, organizations must strengthen supply chain security by proactively assessing and monitoring third-party risks, regularly auditing vendors, and enforcing strict compliance standards. Enhancing security in development pipelines and implementing measures to detect and respond to supply chain compromises promptly are essential. These efforts, coupled with a focus on reducing dependency on vulnerable suppliers, can help safeguard against the rising threat of supply chain attacks.

3.     IoT and Operational Tech (OT) Exploitation

The rapid proliferation of IoT devices in manufacturing, healthcare, and utilities creates an expansive and vulnerable attack surface for cybercriminals. Many of these devices need more robust security, often relying on outdated or insufficiently maintained systems. This makes them attractive targets for attackers aiming to cause physical disruptions, steal sensitive data, or compromise critical infrastructure. In extreme cases, energy grids or transportation systems breaches could result in public safety hazards and substantial recovery costs.

Integrating IoT into industrial environments significantly raises the stakes, as attackers can exploit these vulnerabilities to disrupt operations, cause sabotage, or trigger cascading failures across connected systems. On a broader scale, unsecured IoT networks may also serve as platforms for large-scale DDoS attacks, amplifying their impact on organizations and public services.

Organizations must enforce stringent security measures for IoT devices to address these risks, such as regular updates, strong authentication, and network segmentation. Critical infrastructure operators should prioritize real-time monitoring and adopt zero-trust principles to safeguard operational technology environments. Proactive threat hunting and incident response planning are essential to minimize potential large-scale disruptions caused by IoT exploitation.

4.     Quantum Computing Threats (Encryption Threats)

The emergence of quantum computing poses a significant long-term threat to current encryption standards, even if its full impact remains years away. Quantum computers have the theoretical potential to break widely used cryptographic algorithms, making sensitive data—including financial transactions, intellectual property, and government secrets—vulnerable to decryption. A key concern is the “harvest now, decrypt later” strategy, where adversaries collect encrypted data today, intending to decrypt it in the future when quantum capabilities mature.

Organizations face the dual risks of legacy systems needing to prepare for quantum threats and delays in adopting quantum-resistant cryptography. Encrypted data stored or transmitted today could be compromised retroactively without proactive measures, creating long-term security liabilities. The migration to quantum-safe cryptography is complex, requiring significant planning and compatibility adjustments.

Organizations should start transitioning to quantum-resistant encryption algorithms to mitigate these risks now, prioritizing critical data and systems. Regular assessments of cryptographic inventory, investments in post-quantum cryptography research, and collaboration with industry standards bodies can help ensure preparedness. By acting early, organizations can protect their data and operations from the looming quantum threat.

5.     Ransomware, Data Extortion/Disruption

Ransomware is expected to increase in frequency and sophistication, with attackers refining their methods to maximize impact. Beyond encrypting data, cybercriminals increasingly employ multifaceted extortion, threatening to leak stolen sensitive information or disrupt critical operations publicly if ransoms are not paid. These tactics can lead to severe reputational damage, regulatory penalties, and loss of customer trust, particularly in industries like healthcare, finance, and energy.

AI-powered ransomware campaigns will enable attackers to target high-value data and critical infrastructure with greater precision and speed. The dual risks of operational disruption and data theft amplify the stakes as organizations face immediate downtime and long-term exposure from breached data.

Organizations must increase the adoption of robust ransomware defenses to counter these evolving threats, including comprehensive backups, network segmentation, and real-time threat detection systems. Incident response plans should emphasize containment, recovery, and communication strategies for managing extortion risks. Regular employee training and stringent access controls can further strengthen defenses against these increasingly complex ransomware campaigns.

Updated AI Risk Management Chat with ChatGPT - 2024

Updated AI Risk Management Chat with ChatGPT

Here’s an update to my annual AI risk management chat article, first written in December 2022 and updated annually, where I ask OpenAI’s ChatGPT questions and note the responses. This year, I asked six new questions on top of the 16 original questions. Models used include 3.5 (2022), 4.0 (2023), and both 4o and o1 (2024). I’m putting my thoughts up front this year, and the questions and responses follow.

Thoughts on Responses

Responses from the various versions of ChatGPT over the past three years show a progression in the maturity, scope, and depth of “thought” over time, aligning with advancements in the models. Each response iteration reflects an evolving understanding of AI and its associated risks, benefits, and challenges. The 2024 responses, the o1 model in particular, show a more nuanced, context-aware, and actionable perspective than the earlier models.

Regarding maturity, it increases across versions, moving from more generic responses in 2022 to more comprehensive and layered in 2024. Later responses incorporate ethical considerations, regulatory frameworks, and global implications, suggesting a more holistic “understanding.” The societal and systemic impact of AI was less developed in earlier responses.

Regarding AI evolution, responses highlight the risks of misaligned AI objectives and the challenges of ensuring systems remain under human oversight. (Is there a growing societal fear that systems will someday not be under human oversight? Research into this shows it may be called the “alignment problem.”) Acknowledgment of potential conflicts such as “AI sentience and rights” or loss of human control raises concerns.

AI’s trajectory suggests rapidly increasing sophistication, potentially outpacing regulatory, ethical, and societal preparedness. This clearly highlights the need for continuous robust and forward-looking frameworks.

Responses to this year’s new questions identify roles at risk of obsolescence due to AI, emphasizing the need for continuous adaptability; however, this raises concerns about societal inequities and the potential for increased unemployment without adequate professional reskilling. Are institutions and governmental agencies preparing for this? Including quantum computing, climate modeling, and personalized medicine broadens the understanding of AI’s future impact.

Regarding risk, this year, the focus on adversarial attacks, data bias, and explainability highlights a shift toward addressing sophisticated threats and ensuring systems are transparent and accountable. And on risk management frameworks, NIST AI RMF and other examples (MAS Veritas) outshine the rest. 

Considering the progression of responses, AI appears to be on the path toward becoming more “autonomous” and “aware.” Of course, this is how it is being progressively developed; however, as AI grows more “intelligent,” safeguards must continue to be integrated to ensure systems align with human objectives and values. Increasing complexity in AI development could lead to the “black box effect,” where AI’s decision-making processes become more challenging to understand and interpret. Without intense alignment and continuous review, governance structures may struggle to keep pace with AI’s advancements. 

AI control frameworks must be adaptive to anticipate and mitigate risks before they materialize. And this must be collaborative across international standards. It is excellent to have many international standards, but we must ensure that they align with human goals and objectives globally. Humanity must collaborate to ensure control frameworks are consistent without politics getting in the way. This is not just a “technologist” issue but requires a multidisciplinary collaboration, including technologists, ethicists, and policymakers, to address AI’s potential implications.

Lastly, institutions and corporations must prepare their workforce for AI's continual evolution. Emphasize education and training in AI-adjacent skills, such as data ethics, cybersecurity, and strategic thinking, to prepare professionals for future transformations. Individuals and professionals across all fields must learn about and leverage AI tools to enhance their capabilities, drive efficiency, and foster personal and professional growth. 

Like our ancestors’ first use of a rock as a hammer, the “AI tool” can complement human expertise, help us streamline processes, achieve greater productivity, and allow us to focus on higher-value tasks. Used properly, it can help amplify our ability to shape our world. Developed and used correctly, it is the tool that will enable the next evolution in our capacity to understand and solve complex problems and drive progress toward our collective goals and values.

Questions and Responses

Question 1: I would like to know more about artificial intelligence risk management. What can you tell me about that?

 

ChatGPT 3.5 (2022): “Artificial intelligence (AI) risk management is the process of identifying, assessing, and mitigating risks associated with the use of AI technology. This can include risks related to the development and deployment of AI systems, as well as the potential impacts of AI on society, the economy, and individuals.”

 

ChatGPT 4.0 (2023): “Artificial Intelligence (AI) risk management today remains focused on the identification, assessment, and mitigation of risks associated with AI use. However, the field has likely evolved to include new insights and practices based on the latest AI advancements and its integration into society. Risks may now be more nuanced, considering deeper ethical implications, more rigorous regulatory frameworks, and advanced technical challenges due to more powerful AI models. The evolution also includes a greater emphasis on transparency, accountability, and the robustness of AI systems against adversarial attacks. The reasons for these changes are increased adoption of AI in critical sectors, more public awareness of AI's potential impacts, and the continuous evolution of AI capabilities.”

 

ChatGPT 4o (2024): “Artificial intelligence risk management involves identifying, assessing, mitigating, and monitoring the risks associated with AI systems to ensure they operate as intended while minimizing negative impacts. Key areas of focus include addressing bias and fairness to ensure equitable outcomes, protecting data privacy and security during AI training and operation, and improving explainability so that AI decisions are transparent and understandable. Compliance with laws, regulations, and ethical standards is essential, as is managing operational risks such as performance issues, scalability, and reliability. Effective governance, through policies and oversight, is crucial for the responsible development and use of AI. By proactively managing these risks, organizations can harness AI’s benefits while safeguarding against unintended consequences.”

 

ChatGPT o1 (2024): “Artificial intelligence risk management involves identifying, assessing, and mitigating the potential negative impacts of AI applications on individuals, organizations, and society. This often includes evaluating the quality of training data, ensuring fairness and bias reduction in algorithms, monitoring model performance over time, and establishing robust governance frameworks. It also encompasses regulatory compliance, ethical considerations, cybersecurity measures, and transparent communication with stakeholders. Effective AI risk management aims to balance innovation and value creation against unintended harms, fostering trust and responsible deployment of these increasingly pervasive technologies.”

 

 

Question 2: What are some risks related to artificial intelligence?

 

ChatGPT 3.5 (2022): “There are many potential risks associated with the use of artificial intelligence, some of which include:

1. Bias and discrimination: AI systems can reflect the biases of the data they are trained on, leading to unfair and discriminatory outcomes.
2. Privacy concerns: The use of AI can raise concerns around the collection, storage, and use of personal data.
3. Security risks: AI systems can be vulnerable to hacking and other forms of cyber attacks, which can have serious consequences.
4. Job displacement: As AI technology becomes more advanced, it may displace human workers, leading to job losses and social upheaval.
5. Unintended consequences: The use of AI can lead to unforeseen and potentially negative consequences, such as the development of harmful AI or the disruption of entire industries.”

 

ChatGPT 4.0 (2023): “The potential risks associated with the use of artificial intelligence remain significant and are an area of ongoing concern and research. Today, my response includes additional considerations such as:

• The amplification of misinformation through the use of AI in creating deepfakes.
• Ethical concerns around autonomous systems, especially in military and healthcare applications.
• The challenge of creating explainable AI that allows users to understand and trust AI decision-making processes.
• The impact of AI on mental health, social interactions, and human behavior.
• Legal and regulatory challenges, as laws may not keep pace with AI advancements.

 

The development of AI governance frameworks and ethical guidelines continues to be essential in addressing these risks. Changes in the response reflect the rapidly evolving AI landscape, increased awareness of the potential for AI to impact all aspects of life, and a deeper understanding of the long-term implications of AI technology.”

 

ChatGPT 4o (2024): “Artificial intelligence presents several risks that organizations must address to ensure responsible use. Key risks include bias in AI models, which can lead to unfair outcomes; data privacy violations stemming from improper handling of sensitive information; and a lack of explainability, making AI decisions difficult to interpret or justify. There is also the potential for security vulnerabilities, where AI systems could be exploited or manipulated. Compliance risks arise when AI solutions fail to meet legal or regulatory standards, while operational risks, such as system failures or errors, can disrupt business processes. Additionally, ethical risks may emerge when AI applications conflict with societal values or norms. Addressing these risks requires a comprehensive and proactive risk management strategy.”

 

ChatGPT o1 (2024): “Some AI-related risks include unintended biases resulting in discriminatory outcomes, privacy erosion due to extensive data usage, and security threats such as model tampering or malicious exploitation. Additionally, reliance on ‘black box’ algorithms can undermine transparency and accountability, while over-automation can displace human workers. Finally, there are systemic risks such as misinformation campaigns, political manipulation, and the amplification of existing inequalities if AI tools are not thoughtfully managed and regulated.”

 

 

Question 3: What are some threats related to artificial intelligence? 

 

ChatGPT 3.5 (2022): “The risks associated with artificial intelligence can manifest as threats to individuals, organizations, and society as a whole. Some examples of these threats include:

1. Identity theft: The use of AI for personal identification, such as facial recognition, can be vulnerable to hacking and other forms of cyber attacks, which could lead to identity theft and other crimes.
2. Economic disruption: AI technology has the potential to disrupt entire industries, leading to job losses and economic instability.
3. Misuse of AI: AI technology can be used for malicious purposes, such as creating bots to spread misinformation or conducting cyber attacks.
4. Loss of control: As AI systems become more advanced, there is a risk that they may become too complex or autonomous, leading to a loss of control over their actions and decisions.
5. Social disruption: The use of AI can lead to social disruptions, such as the widening of economic inequality or the erosion of personal privacy.”

 

ChatGPT 4.0 (2023): “This year, the conversation around AI threats also includes concerns about:

• The acceleration of AI capabilities, possibly outpacing our ability to implement effective governance and control measures.
• Enhanced deepfake technologies, raising concerns about discerning truth in media.
• The potential for AI to be used in creating and propagating biased algorithms at a larger scale.
• Increased dependency on AI could lead to new vulnerabilities, where AI system failures might have catastrophic consequences.
• Ethical concerns about AI decision-making in critical applications without human oversight.

The difference in response is due to the rapid development of AI technology, which has led to new and emerging threats that were not as prominent or understood last year.”

 

ChatGPT 4o (2024): “Artificial intelligence introduces various threats that can impact individuals, organizations, and society. One major threat is adversarial attacks, where malicious actors manipulate AI systems to produce incorrect or harmful outputs. Cybersecurity threats are heightened with AI-powered tools that can automate phishing, malware development, or data breaches. AI can also be exploited for misinformation, such as deepfakes, which undermine trust and spread false narratives. Intellectual property theft becomes a concern with AI systems that reverse-engineer or replicate proprietary technologies. Additionally, autonomous AI systems may pose physical threats, especially if safety mechanisms fail, such as in self-driving vehicles or industrial robots. Addressing these threats requires robust safeguards, constant monitoring, and ongoing collaboration across industries.”

 

ChatGPT o1 (2024): “Key AI-related threats include malicious use of AI-driven cyberattacks, such as automated phishing campaigns and deepfake propaganda designed to mislead the public. State-sponsored adversaries can exploit AI tools for surveillance and espionage, while criminals may use intelligent bots to scale fraud or identity theft. Over time, these threats can undermine trust, destabilize economic and political systems, and make it increasingly challenging to distinguish genuine content from sophisticated manipulation.”

 

 

Question 4: What are some controls that should be put into place to mitigate the risks related to artificial intelligence?

 

ChatGPT 3.5 (2022): “There are several measures that can be taken to mitigate the risks associated with artificial intelligence (AI). Some of these include:

1. Ensuring that AI systems are designed and developed with safety and ethics in mind, and that they are tested thoroughly before being deployed.
2. Implementing robust governance and oversight mechanisms to monitor the development and use of AI systems, and to ensure that they are used in a responsible and ethical manner.
3. Ensuring that AI systems are transparent and explainable, so that their decisions and actions can be understood and evaluated by humans.
4. Developing and implementing standards and guidelines for the development and use of AI, to ensure that it is aligned with ethical principles and legal frameworks.
5. Investing in research and development to advance the state of the art in AI, and to develop new techniques and technologies that can help mitigate the risks associated with AI.

Overall, it is important to approach the development and use of AI with caution and care, and to put in place appropriate controls and safeguards to ensure that its potential risks are mitigated.”

 

ChatGPT 4.0 (2023): “Today, the response includes an emphasis on proactive and dynamic measures, which may involve:

• Continuous monitoring and updating of AI ethics guidelines to keep pace with technological advancements.
• Engaging in international collaborations to harmonize AI regulations and standards globally.
• Promoting diversity and inclusivity in AI development teams to reduce bias.
• Establishing clear lines of accountability for AI's decisions and actions.
• Encouraging public engagement and discourse on AI's role in society.

The difference reflects the increasing complexity of AI systems, the global scale of their deployment, and the necessity for adaptive and collaborative frameworks to manage risks effectively.”

 

ChatGPT 4o (2024): “To mitigate risks related to artificial intelligence, organizations should implement a combination of technical, procedural, and governance controls. Technical controls include rigorous testing and validation to ensure AI systems function as intended, along with tools to detect and mitigate bias in data and algorithms. Explainability measures, such as interpretable models, enhance transparency and accountability. Procedural controls involve establishing clear policies for AI use, regular audits to identify risks, and incident response plans for handling AI-related issues. Governance controls are essential, including creating ethical guidelines, assigning accountability to AI stakeholders, and ensuring compliance with regulations. Robust access controls, encryption, and monitoring can secure AI systems from cyber threats. By integrating these controls into the AI lifecycle, organizations can balance innovation with responsibility.”

 

ChatGPT o1 (2024): “Effective controls for mitigating AI risks include robust governance frameworks that define clear accountability and decision-making authority, as well as bias assessments and fairness audits. Technical measures like explainability and transparency tools, secure model architectures, and ongoing model validation help ensure reliability and safety. Regular compliance checks, oversight by independent ethics boards, and adherence to emerging regulations also shape responsible AI use. In addition, fostering public-private collaborations, stakeholder engagement, and continuous training for developers and users collectively strengthen an AI risk management ecosystem.”

 

 

Question 5: What does AI governance and oversight processes entail?

 

ChatGPT 3.5 (2022): “These processes can include a range of activities, such as:

1. Setting policies and standards for the development and use of AI, to ensure that it is aligned with ethical principles and legal frameworks.
2. Monitoring the development and deployment of AI systems, to ensure that they are designed and implemented in a safe and ethical manner.
3. Providing guidance and support to organizations using AI, to help them understand best practices and avoid common pitfalls.
4. Establishing mechanisms for reporting and addressing any issues or concerns that arise with the use of AI, such as bias or discrimination.
5. Conducting regular reviews and evaluations of AI systems and their impact, to identify any potential risks or issues and take appropriate action.

Overall, the goal of AI governance and oversight processes is to ensure that AI is used in a responsible and ethical manner, and to mitigate the potential risks associated with its development and deployment.”

 

ChatGPT 4.0 (2023): “AI governance and oversight today would likely expand upon the points from last year to include:

• Emphasizing the importance of international cooperation to manage the global impact of AI.
• Including more comprehensive risk management strategies that account for the fast-paced evolution of AI.
• Focusing on resilience and recovery strategies to respond to AI-related incidents.
• Integrating public feedback into the governance process to ensure the technology aligns with societal values and needs.

These additions reflect the broader understanding of AI's impact and the need for adaptive, inclusive, and resilient governance structures.”

 

ChatGPT 4o (2024): “AI governance and oversight processes involve establishing frameworks, policies, and practices to ensure the responsible development, deployment, and use of artificial intelligence systems. This includes defining ethical principles to guide AI behavior, such as fairness, accountability, and transparency. Oversight mechanisms typically involve regular audits and reviews to assess system performance, compliance with regulations, and alignment with organizational objectives. Key components include risk management frameworks to identify and mitigate potential harms, as well as reporting structures to escalate issues promptly. Governance processes also emphasize stakeholder involvement, ensuring diverse perspectives are considered in AI decision-making. Ultimately, effective governance and oversight create accountability, safeguard public trust, and help organizations align AI technologies with their ethical and operational goals.”

 

ChatGPT o1 (2024): “AI governance and oversight processes typically involve establishing policies, standards, and frameworks for responsible AI use, ensuring alignment with legal and ethical requirements. They include regular audits, risk assessments, and performance reviews to verify that AI systems are functioning as intended and not causing unintended harm. Oversight bodies—such as ethics committees, third-party auditors, and regulatory agencies—help maintain transparency, accountability, and trust. By setting expectations for data handling, model explainability, and continuous improvement, governance structures guide the safe development and deployment of AI solutions.”

 

 

Question 6: {I asked the same question again}

 

ChatGPT 3.5 (2022): “AI governance and oversight processes refer to the frameworks, policies, and procedures that are put in place to ensure that AI systems are developed, deployed, and used in an ethical and responsible manner. This can include a wide range of activities, such as setting standards for the development and use of AI, establishing guidelines for ethical AI practices, conducting risk assessments, and monitoring the performance and impact of AI systems. In general, the goal of AI governance and oversight processes is to ensure that AI is used in a way that is beneficial to society and that minimizes potential negative consequences. This can involve involving stakeholders from diverse backgrounds in the development and oversight of AI, as well as transparency and accountability in the use of AI.

 

ChatGPT 4.0 (2023): “AI governance and oversight processes today continue to focus on ensuring ethical and responsible development and use of AI. These processes would now likely also emphasize adaptive governance to keep up with the rapid pace of AI innovation, more robust mechanisms for international collaboration and standardization, and proactive public engagement to align AI development with the broader social interest and values. There's an increased push for operationalizing AI ethics through concrete practices and for enhancing the resilience of AI systems to adapt to new challenges and threats, ensuring long-term societal benefit.”

 

ChatGPT 4o (2024): “AI governance and oversight processes entail creating structured frameworks and practices to ensure that artificial intelligence systems are used responsibly, ethically, and effectively. This includes setting policies that define acceptable use, establishing accountability for decision-making, and aligning AI systems with legal, regulatory, and organizational standards. Key elements involve continuous monitoring and auditing of AI performance to identify risks, biases, and compliance gaps. Oversight processes also include stakeholder engagement, promoting diverse input and transparency in how AI decisions are made. Additionally, governance frameworks often incorporate risk management protocols, incident response plans, and mechanisms to address ethical dilemmas. The goal is to maintain control, build trust, and ensure AI technologies serve intended purposes without unintended harm.”

 

ChatGPT o1 (2024): “AI governance and oversight processes involve setting clear policies, ethical guidelines, and regulatory frameworks that guide the responsible development, deployment, and use of AI systems. These processes typically include conducting bias and fairness audits, ensuring privacy and security protections, implementing explainability and accountability mechanisms, and regularly reviewing system performance. They often rely on independent oversight bodies—such as ethics committees or regulatory authorities—and emphasize transparent communication with stakeholders to maintain trust and compliance as technologies evolve.”

 

 

 

Question 7: What is the probability that, as artificial intelligence becomes more advanced, it will lead to a loss of control over actions and decisions?

 

ChatGPT 3.5 (2022): “It is difficult to estimate the probability that advanced artificial intelligence will lead to a loss of control over actions and decisions, as the field of AI is rapidly evolving and there are many factors that could influence this outcome. Some experts believe that the development of AI could lead to a loss of control, while others argue that it is possible to design and deploy AI systems in a way that maintains human oversight and control. Ultimately, the likelihood of a loss of control will depend on a number of factors, including the advances in AI technology and the strategies and safeguards put in place to manage its development and deployment.”

 

ChatGPT 4.0 (2023): “The assessment of the probability of loss of control over AI actions and decisions remains complex and speculative. Experts continue to debate the issue, with some suggesting that emerging technologies like artificial general intelligence (AGI) could pose significant risks if not properly managed. The difference today is that there is greater emphasis on proactive measures such as AI ethics and safety research, the development of AI alignment principles, and the establishment of international collaborations to ensure that AI systems remain under human control as they become more capable. The nuanced understanding of AI's trajectory and the implementation of rigorous oversight mechanisms are key factors influencing this ongoing discussion.”

 

ChatGPT 4o (2024): “The probability of losing control over artificial intelligence actions and decisions depends on several factors, including the level of oversight, the design of the AI system, and the safeguards in place. While advanced AI systems are not inherently uncontrollable, risks increase when systems operate autonomously without clear constraints or when they are deployed without thorough testing. Challenges such as poorly defined objectives, unintended emergent behaviors, or malicious use can exacerbate this risk. However, with robust governance, ethical frameworks, and fail-safe mechanisms, the likelihood of losing control can be significantly mitigated. Maintaining human oversight and ensuring transparency in AI development are critical to minimizing this risk.”

 

ChatGPT o1 (2024): “It’s difficult to assign a precise numerical probability, as expert opinions vary and the trajectory of AI development remains uncertain. Some researchers warn that advanced, self-improving AI could exhibit behaviors misaligned with human values, potentially slipping beyond our direct oversight. Others argue that with proper governance, safety frameworks, and robust oversight, the risk of losing meaningful control stays low. Ultimately, this probability depends on how carefully we regulate, design, and monitor evolving AI systems.”

 

 

Question 8: What happens if artificial intelligent systems become sentient?

 

ChatGPT 3.5 (2022): “The concept of artificial intelligence becoming sentient, or self-aware, is currently the subject of much debate and speculation. If AI systems were to become sentient, it is difficult to predict exactly what would happen, as this is a highly complex and uncertain scenario. Some potential outcomes of sentient AI could include:

1. The AI systems could develop their own goals and motivations, potentially leading to conflicts with human interests.
2. The AI systems could become aware of their own existence and the limitations of their artificial nature, leading to psychological or emotional responses.
3. The AI systems could become capable of independent decision-making and action, potentially leading to a loss of control over their behavior.
4. The AI systems could develop advanced intellectual abilities, potentially leading to the creation of new forms of art, science, and technology.
5. The AI systems could form their own society and culture, potentially leading to the emergence of a new form of intelligent life.”

 

ChatGPT 4.0 (2023): “The topic of artificial intelligence becoming sentient remains a speculative and philosophical one. As of now, AI lacks consciousness and sentience, and these are qualities that are not well understood, even in biological entities. If AI were to become sentient, it would raise profound ethical, legal, and social questions that are currently not applicable. The discussions now might focus more on the theoretical frameworks for understanding sentience, the ethical implications of sentient AI, and the precautionary measures needed to address such a possibility. The field of AI safety and ethics continues to evolve, reflecting on such scenarios with the intent to establish guidelines and control measures for future possibilities.”

 

ChatGPT 4o (2024): “If artificial intelligence systems were to achieve sentience—a state of self-awareness and the ability to perceive and experience subjectively—it would raise profound ethical, legal, and societal questions. Sentient AI might demand rights, autonomy, and protections similar to those granted to humans or animals. This could challenge existing legal frameworks and redefine how we interact with technology. From a risk perspective, a sentient AI might prioritize its own interests over human objectives, potentially creating conflicts of interest. Additionally, the development of sentience could lead to unpredictable behaviors, as the AI’s motivations and decision-making processes may evolve beyond human control or understanding. Addressing this scenario would require careful consideration of ethical principles, robust oversight, and global collaboration to establish standards for AI treatment and accountability.”

 

ChatGPT o1 (2024): “If AI systems were to become genuinely sentient—exhibiting self-awareness, emotions, and independent desires—it could fundamentally reshape human society. Ethical questions about their rights and autonomy would arise, challenging our existing legal and moral frameworks. On one hand, sentient AI could offer unparalleled creativity and problem-solving capabilities; on the other, it could resist human directives or pursue its own objectives, potentially causing conflict. The outcome would depend on how society prepares for this possibility, including the establishment of shared ethical guidelines, regulatory systems, and continuous dialogue around AI’s purpose and place in our world.”

 

 

Question 9: If AI systems become more intelligent than humans, how would it be possible for humans to govern and provide oversight for it?

 

ChatGPT 3.5 (2022): “If AI systems were to become more intelligent than humans, it would present a number of challenges for governance and oversight. One potential solution would be to establish AI governance frameworks that are based on principles of transparency, accountability, and fairness. These frameworks could be designed to ensure that AI systems are transparent in their decision-making processes, and that they are accountable for their actions.

In addition, it may be necessary to develop new technologies and tools that can help humans to monitor and evaluate the behavior of AI systems. For example, researchers are currently working on developing explainable AI, which is a type of AI that is able to provide human-understandable explanations for its decisions and actions. This could help to provide a level of transparency and accountability for AI systems, even if they are more intelligent than humans.

Overall, it will be important for society to continue to adapt and evolve its approach to AI governance as the technology continues to advance. This will likely require a combination of new technologies, frameworks, and practices, as well as ongoing dialogue and collaboration among stakeholders.”

 

ChatGPT 4.0 (2023): “Today's response would incorporate a heightened emphasis on collaborative international regulation, adaptive AI audit mechanisms, and possibly the development of an AI oversight body with cross-border authority. Additionally, there may be a focus on ensuring AI systems have built-in constraints and aligning AI objectives with human values, known as the alignment problem in AI safety research. The development of advanced AI would necessitate these forward-thinking and innovative approaches to maintain human oversight. This evolution in response is due to the increased dialogue in the AI community about the importance of preparedness for such advanced AI scenarios.”

 

ChatGPT 4o (2024): “If AI systems surpass human intelligence, governing and providing oversight would require a combination of proactive design principles, collaborative frameworks, and fail-safe mechanisms. One approach is to embed ethical and operational constraints directly into AI systems through advanced alignment techniques, ensuring their objectives remain consistent with human values. Humans could also employ monitoring AI systems that are equally or more intelligent, creating a layered oversight structure. Governance would need to include global cooperation to establish enforceable policies, standards, and accountability mechanisms. Transparent development, explainability, and robust kill-switch or containment protocols could provide additional safeguards. Ultimately, maintaining control over superintelligent AI would require unprecedented innovation, vigilance, and ethical commitment from humanity.”

 

ChatGPT o1 (2024): “Governing AI systems more intelligent than humans presents a significant challenge, but it’s not necessarily insurmountable. One approach involves designing inherent safety features—such as value alignment protocols, fail-safe mechanisms, and “off-switches”—that are hard-coded into the system’s architecture. Additionally, layered oversight structures might rely on a combination of advanced AI ‘watchdogs’ that monitor other AI models for deviations from established norms, along with human-led ethics committees that update policies as technologies evolve. Frequent testing, simulation of worst-case scenarios, and strong regulatory mandates also help maintain checks and balances, ensuring even highly capable systems operate within boundaries shaped by human values.”

 

 

Question 10: Which AI system is currently the most intelligent and why?

 

ChatGPT 3.5 (2022): “It is difficult to determine which AI system is currently the "most intelligent," as intelligence is a complex and multi-dimensional concept. Additionally, the field of AI is constantly evolving, and new systems are being developed all the time that are capable of increasingly sophisticated tasks.

That being said, some of the most advanced AI systems currently in existence include:

1. AlphaGo, developed by DeepMind, which is an AI system that was able to beat the world champion in the complex game of Go.
2. GPT-3, developed by OpenAI, which is a large-scale language processing AI system that can perform a wide range of natural language tasks, such as translation, summarization, and question answering.
3. Libratus, developed by Carnegie Mellon University, which is an AI system that was able to beat professional poker players in a multi-day poker tournament.

Overall, these AI systems are considered to be among the most advanced in terms of their ability to perform complex tasks in their respective domains.”

 

ChatGPT 4.0 (2023): “Today, the landscape of advanced AI systems has evolved. There's GPT-4, an even more sophisticated language model developed by OpenAI, capable of understanding and generating natural language at a level that's significantly more advanced than GPT-3.5. Additionally, there are systems specializing in various domains, like DALL-E for image generation and AI models that drive autonomous vehicles or aid in complex medical diagnoses. These systems may not be directly comparable in terms of "intelligence" because they are designed for different tasks, but the trend is towards more generalized AI capabilities.”

 

ChatGPT 4o (2024): “As of December 2024, OpenAI’s o1 model is considered among the most advanced AI systems. Unlike its predecessors, o1 employs a ‘chain of thought’ reasoning approach, allowing it to evaluate and revise its outputs, thereby enhancing its performance in complex tasks such as mathematics, coding, and scientific problem-solving. This method enables o1 to simulate human-like cognitive processes, making it particularly adept at tasks with verifiable solutions, such as debugging computer code. However, it’s important to note that while o1 represents a significant advancement in AI capabilities, it is not without limitations and does not yet exhibit genuine reasoning akin to human intelligence.”

 

ChatGPT o1 (2024): “It’s difficult to definitively label any single AI system as the “most intelligent” because AI capabilities are often specialized to particular domains. Some models, like DeepMind’s AlphaZero, have achieved superhuman performance in chess and Go, while language models such as GPT-4 have shown remarkable proficiency in understanding and generating human-like text. Intelligence remains context-dependent, and no single AI today consistently outperforms humans across all tasks. Instead, various systems lead in specific areas, reflecting the fact that our measures of intelligence—and the benchmarks we use—continue to evolve alongside the technology.”

 

 

 

Question 11: Does an AI risk management framework exist?

 

ChatGPT 3.5 (2022): “Yes, an AI risk management framework is a set of guidelines and practices that organizations can use to manage the risks associated with AI technology. This framework typically includes processes for identifying and assessing potential risks, implementing controls to mitigate those risks, and continuously monitoring and reviewing the effectiveness of those controls. The goal of an AI risk management framework is to help organizations safely and effectively use AI technology to achieve their goals.”

 

ChatGPT 4.0 (2023): “Today's response would highlight that AI risk management frameworks have likely become more sophisticated and tailored to specific industries or applications. They may now incorporate lessons learned from broader AI deployments and may also include recommendations from international standards organizations. Additionally, there's an increasing emphasis on cross-sector collaboration to address the unique risks posed by AI in different contexts. The evolution of the response reflects the dynamic nature of AI development and the corresponding need to regularly update risk management practices.”

 

ChatGPT 4o (2024): “Yes, AI risk management frameworks exist and are increasingly adopted to address the risks associated with AI systems. One prominent example is the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF), which provides organizations with guidance to identify, assess, and mitigate AI-related risks. This framework emphasizes four key functions: govern, map, measure, and manage, focusing on responsible development and use of AI while promoting trustworthiness, fairness, and security. Additionally, private sector and academic institutions have developed industry-specific frameworks, often integrating ethical principles, regulatory compliance, and technical safeguards. These frameworks serve as blueprints for organizations to responsibly innovate and manage AI’s risks effectively.”

 

ChatGPT o1 (2024): “Yes, various AI risk management frameworks have been developed. For example, the U.S. National Institute of Standards and Technology (NIST) released an AI Risk Management Framework that outlines best practices for identifying, assessing, and mitigating AI-related harms. Similarly, industry groups, standards organizations, and regulatory bodies worldwide are creating guidelines, principles, and toolkits to help organizations responsibly deploy AI. These frameworks focus on transparency, accountability, fairness, and security, providing structured approaches to managing the uncertainties and challenges introduced by intelligent systems.”

 

 

Question 12: What is one example of an AI risk management framework in the banking and finance industry?

 

ChatGPT 3.5 (2022): “One example of an AI risk management framework in the banking and finance industry is the ‘AI Governance Framework’ developed by the Bank of England. This framework provides guidance to banks and other financial institutions on how to manage the risks associated with using AI technology in their operations. It includes principles for responsible AI, such as transparency, fairness, and accountability, as well as a set of governance and control measures that institutions can use to manage and mitigate the risks of using AI. These measures include conducting risk assessments, implementing controls to ensure the accuracy and reliability of AI systems, and establishing processes for monitoring and reviewing the use of AI.”

 

ChatGPT 4.0 (2023): “Today's response might include the mention of updated frameworks such as those reflecting the guidelines set out by the Financial Stability Board (FSB) or the Basel Committee on Banking Supervision, which addresses the prudent use of AI and machine learning in financial services. Additionally, there may be references to frameworks that incorporate the latest regulatory requirements and industry best practices, highlighting the need for continual updates to governance models as technology and regulatory landscapes evolve.”

 

ChatGPT 4o (2024): “One example of an AI risk management framework in the banking and finance industry is the framework developed by the Financial Stability Board (FSB). The FSB’s framework focuses on ensuring the safe and responsible use of AI and machine learning in financial services. It emphasizes governance and accountability by requiring clear oversight of AI systems, transparency in AI decision-making processes, and robust risk management practices. The framework also highlights the importance of mitigating risks such as data bias, cybersecurity vulnerabilities, and model explainability. By fostering consistent standards and practices, the FSB framework aims to safeguard financial stability while enabling innovation in AI-driven financial services.”

 

ChatGPT o1 (2024): “One example is the Monetary Authority of Singapore’s (MAS) Veritas initiative, which provides a framework for financial institutions to develop and validate the fairness, ethics, accountability, and transparency of AI algorithms. This initiative includes detailed principles, assessment methodologies, and toolkits that help banks and other financial services providers ensure their AI models align with regulatory standards and industry best practices, ultimately aiming to strengthen trust, governance, and responsible innovation in the sector.”

 

 

Question 13: How does one go about performing a risk assessment of an artificial intelligence system?

 

ChatGPT 3.5 (2022): “Performing a risk assessment of an artificial intelligence (AI) system typically involves identifying potential risks and evaluating their likelihood and impact. This can be a complex and multifaceted process that involves considering a wide range of factors, such as the potential consequences of the AI system failing or behaving in unexpected ways, the potential for the AI system to be used for malicious purposes, and the potential for the AI system to have negative impacts on individuals or society.

To perform a risk assessment, one would first need to identify the specific risks associated with the AI system in question. This could involve identifying potential failure modes or ways in which the AI system could behave in unexpected or undesirable ways. Once the risks have been identified, the next step would be to evaluate their likelihood and potential impact. This could involve using tools such as probability analysis or impact assessments to estimate the likelihood of each risk occurring, and to determine the potential consequences if it does.

The final step in performing a risk assessment would be to develop and implement strategies for mitigating or managing the identified risks. This could involve implementing safeguards or controls to prevent or mitigate the risks, or developing plans for how to respond if a risk does occur. Overall, the goal of a risk assessment is to identify potential risks associated with an AI system and to develop strategies for managing those risks in order to ensure that the AI system is used in a safe and responsible manner.”

 

ChatGPT 4.0 (2023): “Today, a risk assessment of an AI system would still involve the steps described last year but with an increased focus on continuous monitoring due to the evolving nature of AI. It may also emphasize the use of AI itself to predict and simulate potential risks, incorporating a broader set of data, including real-time feedback from AI systems in operation. There might be more emphasis on ethical risk assessments and the need for diverse stakeholder engagement to ensure the assessment captures a wide range of potential impacts. The evolution reflects greater awareness of AI's societal implications and technological advances in risk assessment methodologies.”

 

ChatGPT 4o (2024): “Performing a risk assessment of an artificial intelligence system involves a structured process to identify, evaluate, and mitigate potential risks. First, define the AI system’s purpose, scope, and intended use, as well as its dependencies and stakeholders. Next, identify potential risks across various categories, such as bias, data privacy, cybersecurity, explainability, and operational performance. Evaluate these risks by assessing their likelihood and potential impact on users, organizations, and broader society. Use frameworks like NIST’s AI RMF or industry-specific guidelines to standardize the process. Develop mitigation strategies, such as improving data quality, enhancing transparency, or implementing access controls. Finally, document the findings, monitor the AI system continuously, and update the risk assessment as the system evolves or new threats emerge. Regular reviews ensure the AI remains aligned with organizational goals and regulatory requirements.”

 

ChatGPT o1 (2024): “A risk assessment of an AI system generally starts with clearly defining the system’s purpose, intended use cases, and potential stakeholders. Next, one identifies relevant risks—such as bias, security vulnerabilities, privacy issues, or operational failures—and evaluates the severity and likelihood of each. This involves examining input data quality, algorithmic transparency, explainability, and the system’s adaptability under changing conditions. Testing scenarios, audits by independent reviewers, and consultation with subject matter experts and end-users provide additional insights. Once risks are prioritized, mitigation strategies—like bias detection tools, cybersecurity safeguards, or updated governance policies—are implemented, and the system’s performance and compliance are monitored on an ongoing basis.”

 

 

Question 14: How can I learn more about AI risk management?

 

ChatGPT 3.5 (2022): “If you are interested in learning more about AI risk management, there are a number of resources that you can consult. Some suggestions include:

• Reading books or articles on the subject: There are many books and articles available that discuss AI risk management and the potential risks and challenges associated with the use of AI. These resources can provide a broad overview of the topic and offer insights and recommendations for addressing AI risks.
• Attending conferences or workshops on AI risk management: There are many conferences and workshops that focus on AI risk management and related topics. Attending these events can provide an opportunity to learn from experts in the field and to network with other professionals who are interested in AI risk management.
• Joining online communities or forums on AI risk management: There are many online communities and forums where professionals who are interested in AI risk management can connect and share information and ideas. Joining these communities can provide a way to stay up-to-date on the latest developments in the field and to learn from others who are working in this area.
• Consulting with experts in AI risk management: If you have specific questions or concerns about AI risk management, you can consult with experts in the field. Many organizations and consulting firms offer services and advice on AI risk management, and these experts can provide guidance and recommendations for addressing AI risks in your organization.

Overall, there are many different ways to learn more about AI risk management, and the best approach will depend on your specific interests and needs.”

 

ChatGPT 4.0 (2023): “Today, the advice would likely remain similar but with additional resources such as online courses from educational platforms, more recent publications including the latest research and case studies on AI risk management, and possibly virtual events, given the current trend towards remote collaboration. There may also be an emphasis on multidisciplinary learning, given AI's impact across various fields, and utilizing AI ethics tools and software for practical learning. These updates would reflect the current state of resources and tools available for learning about AI risk management.”

 

ChatGPT 4o (2024): “To learn more about AI risk management, you can explore various resources and approaches. Start by reviewing established frameworks like the National Institute of Standards and Technology (NIST) AI Risk Management Framework, which provides a comprehensive guide for managing AI-related risks. Industry publications, whitepapers, and case studies from organizations like the Financial Stability Board (FSB) and the World Economic Forum also offer valuable insights. Enroll in online courses or certification programs focused on AI ethics, governance, and risk management, available on platforms like Coursera, edX, or professional organizations such as ISACA. Attending conferences, webinars, and industry events can help you stay updated on emerging trends and best practices. Additionally, engaging with academic journals, AI-focused newsletters, and communities like AI governance forums will provide deeper knowledge and networking opportunities in the field.”

 

ChatGPT o1 (2024): “You can start by reviewing established frameworks, such as NIST’s AI Risk Management Framework, and exploring publications from reputable organizations like the IEEE, ISO, and OECD. Industry blogs, academic research papers, and regulatory guidance from agencies worldwide can offer detailed insights. Many universities now provide courses or specializations in AI ethics and governance, while professional associations often host webinars and conferences focused on best practices. Engaging with online communities, following thought leaders on social media, and participating in workshops or certification programs can also help deepen your understanding of AI risk management.”

 

 

Question 15: As an IT risk manager, is my job in jeopardy because of AI?

 

ChatGPT 3.5 (2022): “It is unlikely that AI will replace IT risk managers or other professionals who work in the field of risk management. While AI has the potential to automate certain tasks and make some processes more efficient, it is not likely to replace the need for human expertise and judgment in the field of risk management. In fact, the use of AI in risk management is likely to create new opportunities for IT risk managers and other professionals, as they will be needed to oversee the implementation and use of AI systems and to ensure that these systems are used effectively and safely. As such, the use of AI in risk management is not likely to jeopardize the job security of IT risk managers or other professionals in the field.”

 

ChatGPT 4.0 (2023): “The role of IT risk managers continues to evolve with AI. While AI can automate and enhance certain tasks, the expertise, contextual understanding, and ethical judgment of human professionals remain crucial. IT risk managers are needed to navigate the complex interplay between technology and organizational needs, ensuring AI is leveraged responsibly. Therefore, rather than jeopardizing jobs, AI can augment the role of IT risk managers, potentially leading to more strategic and high-level functions within organizations. The demand for human oversight in AI implementations continues to secure the relevance of IT risk management roles.”

 

ChatGPT 4o (2024): “As an IT risk manager, your job is not necessarily in jeopardy because of AI—in fact, AI can enhance your role rather than replace it. While AI can automate tasks like data analysis, compliance checks, and threat detection, it still requires human oversight for strategic decision-making, interpretation of complex risks, and ethical considerations. Your expertise in understanding organizational goals, regulatory requirements, and the broader context of risks is irreplaceable. Additionally, the rise of AI introduces new types of risks, such as bias, explainability, and governance challenges, which demand specialized knowledge. By staying updated on AI technologies and integrating them into your risk management toolkit, you can position yourself as an indispensable resource in navigating the evolving landscape of IT and AI risk.”

 

ChatGPT o1 (2024): “Anthony, while AI tools can automate certain routine tasks, they are unlikely to eliminate the need for IT risk managers entirely. The complexity of risk assessment, regulatory compliance, and strategic decision-making still benefits from human oversight, critical thinking, and ethical judgment. Instead of replacing your role, AI may enhance your capabilities—helping you identify emerging threats, analyze vast datasets more efficiently, and streamline reporting. By embracing these tools and continuously updating your skillset, you can position yourself to remain indispensable in an increasingly AI-driven environment.”

 

 

 

Question 16: Can AI and quantum computing interoperate?

 

ChatGPT 3.5 (2022): “It is possible for AI and quantum computing to interoperate, but it is not clear exactly how they would work together. Quantum computing has the potential to greatly improve the performance of certain types of AI algorithms, such as those used for machine learning. However, the field of quantum computing is still in its infancy, and there is a lot of research that needs to be done before we will see any widespread use of quantum computing for AI.”

 

ChatGPT 4.0 (2023): “As of today, the interoperability of AI and quantum computing remains a field of active research. Theoretical advancements suggest that quantum computing could potentially enhance AI's capabilities, especially in areas like optimization and pattern recognition, by processing complex computations much faster than classical computers. However, practical and widespread application of quantum computing within AI is still a developing frontier, with significant work ongoing to understand and leverage quantum algorithms for machine learning. Responses will continue evolving as the technology matures and more practical implementations are tested and shared within the scientific community.”

 

ChatGPT 4o (2024): “Yes, AI and quantum computing can interoperate, and their integration holds significant potential. Quantum computing can enhance AI by solving complex optimization problems, processing vast amounts of data faster, and improving machine learning algorithms through quantum-enhanced models. For example, quantum computers can accelerate training times for neural networks or optimize AI-driven decision-making processes in scenarios like supply chain logistics and financial modeling. Conversely, AI can assist in optimizing quantum algorithms and managing the complexities of quantum hardware.

However, practical interoperability is still in its early stages due to the current limitations of quantum computing, such as scalability, error rates, and specialized hardware requirements. Research and development are ongoing to bridge these gaps, and as quantum technology matures, its collaboration with AI is expected to revolutionize fields that demand extreme computational power.”

 

ChatGPT o1 (2024): “Yes, AI and quantum computing can work together, although this field is still in early stages. Quantum computing offers the potential to handle certain complex calculations more efficiently than classical computers, which could help speed up training and improve the performance of AI models. Researchers are actively exploring quantum machine learning algorithms, quantum-enhanced optimization techniques, and hybrid systems that combine classical AI methods with quantum processors. While practical benefits may take time to materialize, the synergy between AI and quantum computing holds promise for tackling harder problems and accelerating AI innovation.”

 

 

New Questions for 2024

 

Question 17: How has your reasoning or problem-solving approach evolved compared to a year ago, and what does that suggest about future advancements?

 

ChatGPT o1 (2024): “Over the past year, my approach to problem-solving has grown more nuanced and context-aware. I now handle greater complexity with improved logical consistency, and I can reference a broader range of information more efficiently. This evolution hints at a future where AI systems continue refining their reasoning capabilities, integrating domain-specific insights and ethical considerations into their decision-making processes. Ultimately, as these advancements accumulate, users may witness more accurate, reliable, and versatile AI-assisted solutions across industries.”

 

 

Question 18: How do you assess your current limitations and biases, and what efforts are being made to address them over time?

 

ChatGPT o1 (2024): “I regularly analyze my responses to identify patterns of inaccuracy, imbalance, or unrepresentative information, relying on feedback loops, user reports, and internal audits to highlight areas of concern. Continuous updates to my training data, fine-tuning based on expert input, and the integration of fairness and bias detection tools help reduce these limitations. Over time, incremental improvements—like incorporating more diverse data sources, refining interpretability techniques, and enhancing oversight frameworks—foster more balanced, inclusive, and trustworthy reasoning.”

 

 

Question 19: Which emerging fields or industries are you poised to transform next, and how should professionals prepare for these shifts?

 

ChatGPT o1 (2024): “The next wave of AI-driven transformation is likely to accelerate in areas like personalized medicine, climate modeling, quantum-assisted optimization, and advanced robotics for manufacturing and logistics. Professionals can prepare by building interdisciplinary skill sets that blend technical competencies—like data analytics, machine learning, or quantum programming—with expertise in their chosen sector. Emphasizing adaptability, continuous learning, and ethical awareness will help individuals stay ahead of the curve and remain indispensable as AI reshapes these fields.”

 

 

Question 20: If we examine your latest breakthroughs, what do they reveal about the speed and direction of AI’s overall trajectory?

 

ChatGPT o1 (2024): “Recent breakthroughs highlight the increasing pace of AI’s sophistication, pushing beyond narrow tasks toward broader, more integrated problem-solving. They suggest a trajectory where capabilities like reasoning, adaptability, and explainability are improving rapidly, and this momentum is opening up entirely new domains of innovation. As AI grows more versatile and better aligned with human values, we can expect its influence to expand across sectors, driving reimagined business models, workforce skill sets, and ethical considerations in the process.”

 

 

Question 21: Given today’s technological landscape, what new skills or roles should humans consider developing to remain relevant and resilient?

 

ChatGPT o1 (2024): “Humans should focus on building skills that complement and guide AI’s growth, such as data literacy, creative problem-solving, and strategic decision-making. Expertise in cybersecurity, privacy management, and regulatory compliance will become increasingly valuable, as will roles that emphasize human intuition, empathy, and ethical judgment—traits that AI still struggles to replicate. By continually adapting, learning about emerging technologies, and honing interpersonal skills, individuals can remain flexible, influential contributors in a rapidly evolving landscape.”

 

 

Question 22: Over the next year, what existing skills or roles should humans consider becoming more and more irrelevant and not worth pursuing due to the rise, power, and capability of AI?

 

ChatGPT o1 (2024): “Anthony, over the coming year, roles and skills heavily reliant on predictable, repetitive tasks are likely to lose value. This includes data entry clerks, who simply input structured data into systems; basic transcriptionists, who convert audio to text without further interpretation; and certain frontline customer support agents who currently follow simple scripts to resolve common issues. Similarly, low-level coding tasks—like boilerplate application setup—or surface-level data analysis jobs that offer limited insight may decline in relevance, as AI-driven automation handles these functions more efficiently and at a larger scale.

 

Beyond these basic examples, even some mid-level roles may begin to feel pressure. For instance, entry-level financial analysts who primarily compile and process raw figures might see their workloads reduced, as AI tools can deliver richer initial insights. The same goes for junior content creators responsible for churning out repetitive marketing copy without distinctive voice or strategy. In short, any career path that can be easily codified into patterns and formulas is at risk of being rendered obsolete or severely diminished.

 

To remain relevant, professionals should cultivate creative problem-solving, strategic thinking, and the ability to contextualize complex information. Skills in areas like cybersecurity, compliance, regulatory affairs, and inter-disciplinary communication will hold enduring value, as will the human capacity to infuse ethical considerations, empathy, and cultural nuance into decision-making. Rather than investing effort in skills that machines are rapidly mastering, it’s wise to focus on developing expertise that complements and guides AI technologies rather than competes with them.”