Developing Technological Frontiers with Quantum Computing and Optimizing Risk and Return

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Hi, enjoy this weeks curated risk and business updates.

Quantum computing has the potential to solve large scale problems that current computing capabilities can not address. Its capabilities could disrupt future innovations, business, telecommunications, security, and global geopolitics in a more dramatic way than most currently known technologies. However, most leaders and managers have had little exposure to quantum computing and the associated opportunities and risks that could affect their business in the near future.

As background, classic computers are based on binary, true/false, on/off logic and their power scales in a linear fashion. Quantum computers on the other hand are based on probabilistic logic - the probability that a statement is true/false, or on/off, and their power scales in an exponential manner. Comparing bits in a traditional computer to qubits in quantum computing can help understand the difference and the opportunity:

Bits

• Bits are in a binary state at any given time - true or false, on or off, or 1/0

• Bits are independent - their binary state does not affect the binary state of other bits

• Information stored scales linearly with each bit

• Each bit can do only one computation at a time

Qubits

• The state of a qubit is called its superposition - the probability it will be true or false, on or off, 1 or 0 until it is measured

• Qubits can be connected to, or “entangled” with, other qubits - allowing the state of one qubit to affect the state or one or more other qubits.  

• Entanglement allows the amount of information that can be processed to scale exponentially with each additional qubit

• Entangled qubits in a state of superposition can create “waves” of probabilities that can be used to solve problems.

“A classical computer needs to run one path after the other until it finds the way out of the maze. If the maze comprises 256 possible paths, the classical computer has to run through the maze about 128 consecutive times (on average, half of a maze’s paths must be tried to find the right one). A quantum computer, however, is able to work with all 256 paths at once.”

Classic computers are better at solving simple problems and tasks:

• Storing and processing large sets of data

• Everyday Tasks such as browsing the internet, writing emails, or creating spreadsheets

“In general, quantum computers are slower than traditional ones and thus, all else equal, they are less capable. But all else is not equal. Quantum computers can run some algorithms that classical computers cannot. This is because quantum computers can represent large amounts of information in dense “superpositions”, a feat that classical computers cannot achieve. This representation allows quantum computers to perform operations on the patterns between individual bits (superpositions), not just on the individual bits themselves.”

Quantum computers will be more useful for complex computations such as:

• Simulating financial marketplace behaviors, weather behavior, molecular systems (drug research)

• Optimization of supply chain routes, manufacturing production, vehicle routing, etc.

• Unstructured search - finding information in an unstructured data set - the proverbial needle in a haystack

While the promise is high, risks related to quantum computing could evolve rapidly and in unexpected ways, but some areas of consideration include:

• Cybersecurity - Ability to crack encryption systems that are at the core of secure transactions, communications and securing private data

• Challenges hiring, compensating and retaining quantum computing expertise

• Cost inefficiencies and poor ROI from deploying quantum computing solutions

• Uncertain regulatory environment

AI applications using quantum computing could lead to technology misuse, bias, propaganda and disinformation capabilities, etc.

Request more information on DelCreo’s Risk Universe and risk assessment services.

As a reminder, here are our Risk Universe categories that we leverage to tackle and understand risk which include:

• External Risk

• Governance Risk

• Strategic Risk

• Product Risk

• Business Operations Risk

• Legal & Compliance Risk

• Financial Risk

• Technology Risk

These high-level risks are fairly consistent between different companies and risk profiles.

We leverage our understanding of risk maps and risk universes to better advise our clients in strategic business decisions and to optimize the management of risk throughout the enterprise.

Weighing the Risks

Weekly Highlights

Three Key Ideas:

• European Market Response to Monetary Policy: European stock markets showed a modest rise after a European Central Bank official indicated possible rate cuts, reflecting investor optimism and market sensitivity to central bank policies. However, ongoing geopolitical tensions and wage growth uncertainty continue to cloud the economic outlook and contribute to market volatility.

• Trust and Governance in AI: The establishment of trust in AI systems is crucial for their successful integration into business practices. Organizations need to focus on developing and implementing the "AI Trust Equation" which incorporates security, ethics, accuracy, and control to foster trust and thereby enhance revenue, competitiveness, and customer relations.

• Strategic Risk Management in Digital Transformation: Successful digital transformation strategies require aligning projects with business objectives and engaging all relevant stakeholders. By prioritizing projects based on their impact and feasibility, businesses can better manage risks associated with disruptive innovations and ensure sustained alignment with market trends and business goals.

Recommendations:

• Implement Comprehensive Risk Management Frameworks: Businesses and risk managers should develop and implement comprehensive risk management frameworks that address the volatility stemming from global economic uncertainties, the governance challenges posed by AI integration, and the strategic risks associated with digital transformation. This includes continuous monitoring of external and internal risks, revising strategies in response to changing monetary policies and geopolitical scenarios, ensuring AI systems align with ethical standards and regulations, and engaging stakeholders in transformation projects to mitigate risks effectively.

Risk Universe Weekly Updates

External Risk

• Stocks Get Lift From Rate Outlook, Dollar Dips: Markets Wrap

  • European Market Volatility and Policy Speculation: European shares experienced a slight increase in thin trading following signals from a European Central Bank official suggesting potential consecutive rate cuts starting next month. Uncertainty remains regarding subsequent steps due to factors such as wage growth uncertainty and geopolitical tensions, including conflicts in the Middle East, impacting market sentiment and economic stability.

  • Global Economic Indicators and Market Dynamics: Key economic events and policy decisions, including inflation data releases and central bank speeches, are expected to influence market movements throughout the week. Traders are closely monitoring developments in various regions, including Asia, Europe, and the US, amid ongoing discussions on monetary policy, geopolitical events, and supply chain disruptions impacting commodity prices and currency fluctuations.

Governance Risk

• Trust in AI is more than a moral problem

  • Trust as a Key Governance Factor: Trust in AI systems is crucial for organizational success, with C-suite executives recognizing its impact on revenue, competitiveness, and customer success. The lack of trust in AI systems poses significant governance risks, necessitating the development of a new "AI Trust Equation" focused on practical application, including factors such as security, ethics, accuracy, and control.

  • Implementation Strategies for Building Trust: Organizations must evaluate AI platforms based on their usefulness, security, ethical considerations, accuracy, and control to ensure alignment with organizational goals and values. By tailoring the AI Trust Equation to their specific needs and fostering trust in AI systems, organizations can unlock economic potential and shape the future of technology and society.

• EU Launches AI Office To Shape Future AI Governance And EU Ecosystem

  • Establishment of European AI Office: The European Union is setting up the European AI Office to centralize AI expertise and provide guidance on best practices, regulation, compliance, safety, and innovation, aiming to position the EU as a global reference point for AI governance.

  • Leadership and Implementation of AI Act: Margrethe Vestager highlights the role of the AI Office in ensuring coherent implementation of the AI Act, the world's first comprehensive law on artificial intelligence, which will enter into force soon after the European Elections. However, some lawmakers express concerns about the structure of the AI Office, perceiving it as lacking vision and merely reorganizing existing Commission units.

Strategic Risk

• Top priorities for digital transformation strategy implementation

  • Strategic Alignment and Stakeholder Engagement: Effective digital transformation strategies prioritize understanding business objectives, engaging key stakeholders, and aligning projects with overarching strategic initiatives. By involving senior management, business leaders, IT teams, and external partners, organizations can ensure that digital initiatives address brand and reputation risk, competition risk, and company execution risk while driving value in areas such as customer experience and operational efficiency.

  • Risk Mitigation and Prioritization: Prioritizing projects based on impact, feasibility, and alignment with business needs helps mitigate risks associated with disruptive innovation, business model, and concentration. A detailed business/technology roadmap, coupled with quarterly strategy reviews, ensures alignment with evolving market trends and business goals, while considerations for technologies like generative AI necessitate evaluation of data privacy, security, and ethical use to minimize potential risks.

• Entrepreneurship in the Entertainment Industry: Analyzing Profitability and Risk

  • Industry Potential and Revenue Streams: The entertainment sector presents lucrative opportunities for entrepreneurs, with diverse revenue streams spanning film production, streaming services, music, licensing, and merchandise. However, high initial investment costs, market volatility, oversaturation, and intellectual property challenges pose significant risks to investors.

  • Data-driven Decision Making and Networking: Successful entrepreneurs leverage data analytics to tailor content and optimize marketing strategies, as exemplified by Netflix. Building strong networks with key industry players and diversifying portfolios across different segments of the industry are vital strategies for mitigating risks and achieving success.

Legal & Compliance Risk

• Scarlett Johansson’s OpenAI clash is just the start of legal wrangles over artificial intelligence

  • Intellectual Property Concerns and Legal Ramifications: The use of AI-generated voices resembling celebrities raises significant legal and compliance risks, particularly concerning the right of publicity and intellectual property. Unauthorized use of a person's likeness, voice, or image without permission can lead to legal disputes and potential lawsuits, as seen in past cases such as Bette Midler's lawsuit against Ford Motor Company.

  • Industry Regulations and Advocacy Efforts: Sag-Aftra and legal experts advocate for the establishment of federal intellectual property rights to protect performers from unauthorized digital replicas, emphasizing the need for legal clarity and regulatory frameworks in the entertainment industry. Compliance with industry laws and regulations, along with ethical considerations, is crucial to mitigate privacy risks and maintain trust in AI technologies.

Technology Risk

• Report: AWS to spend billions on new cloud data center infrastructure in Italy

  • Expansion of AWS Data Center Operations in Italy: Amazon Web Services Inc. (AWS) is in talks with the Italian government for a potential multibillion-dollar investment to expand its data center operations in the country, either by expanding its existing facility in Milan or building a new one elsewhere. This expansion aligns with AWS's broader strategy to boost its presence across Europe and cater to the growing demand for cloud services in the region.

  • European Expansion Plans and Market Dynamics: AWS's investment in Italy comes amidst its aggressive scaling of European expansion plans, with recent announcements of significant investments in Spain and Germany. The move reflects the increasing demand for cloud services in Europe, driven by factors such as data privacy regulations, the adoption of artificial intelligence, and the resurgence of IT spending by enterprises.

• Google research shows the fast rise of AI-generated misinformation

  • Rise of AI-Generated Misinformation: The research highlights the rapid growth of AI-generated images as a form of misinformation online, becoming nearly as prevalent as traditional text manipulation, attributed to the release of new AI image-generation tools by major tech players in recent years.

  • Challenges for Platforms and Solutions: Social media companies and tech giants like Google face challenges in combating AI-generated misinformation, with instances of fake celebrity images affecting search results and social media platforms. Initiatives like digital watermarking are being explored as potential solutions to identify and mitigate the spread of AI-generated fake images.

• Quantum Computers Can Now Run Powerful AI That Works like the Brain

  • Potential of Quantum Transformers: Researchers are exploring the possibility of running transformers, such as those powering ChatGPT and other chatbots, on quantum computers, with a recent study demonstrating the feasibility of rudimentary quantum transformers. These quantum-AI combinations could address crucial challenges in encryption, chemistry, and other fields, leveraging the unique properties of qubits to potentially outperform classical counterparts.

  • Challenges and Prospects: While quantum transformers have shown promise, challenges remain in scaling them up to match the capabilities of classical counterparts like Google's Gemini or OpenAI's ChatGPT. However, a hybrid approach integrating classical and quantum systems may offer a promising solution, leveraging the strengths of each to tackle complex problems efficiently and potentially reducing energy consumption compared to traditional approaches.