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Artificial Intelligence (AI)

Rise of the AI Influencer: Advancements in Marketing and Communications

Velocity Ascent Live · March 19, 2025 ·

The reality of AI Influencers in Marketing

It is no surprise that artificial intelligence (AI) is reshaping industries across the globe. One of the most intriguing developments in this realm is the emergence of AI influencers—digital personas driven by sophisticated algorithms, designed to engage with audiences in much the same way as human influencers.

https://www.instagram.com/laila.khadraa/



These AI figures are not just marketing tools, they are also creating new possibilities for brands to connect with their audiences, offering a fresh take on digital influence and marketing. In this blog post, we’ll dive into the concept of AI influencers, taking a closer look at two high-profile examples in the fashion and sports sectors: Laila, the AI influencer created for Puma Morocco, and Maya Puma, the collaboration between IPG Mediabrands and Puma. We’ll also explore why these AI personalities matter for businesses and how they can play a key role in your marketing strategy.

Laila Khadraa: PUMA’s Virtual Ambassador Created Using AI

The Rise of AI Influencers

AI influencers are digital entities that use artificial intelligence and machine learning to interact with human audiences through social media platforms, advertising campaigns, and branded content. Unlike human influencers, AI influencers are entirely virtual—they are created from a combination of machine learning, image recognition, and data analysis, which allows them to respond to trends, audience feedback, and cultural shifts with remarkable precision.

Instagram

The attraction of AI influencers lies in their ability to remain “always on”—24/7 engagement, no sleep, no off-days. They are immune to controversies or scandals that can derail a human influencer’s career. Additionally, AI influencers can be designed to embody ideal brand traits and values, offering brands a chance to craft the perfect ambassador for their products.

In many ways, these AI personalities represent the next frontier in digital marketing, offering a new level of personalization, reliability, and scalability. But how do these digital avatars work, and how can brands leverage them to maximize their reach?

Puma Morocco’s AI Influencer Laila

In 2022, Puma Morocco launched Laila, an AI influencer designed to blend seamlessly into the digital fabric of Morocco’s fashion scene. The idea behind Laila was to create a virtual ambassador who felt human, yet was distinctly not, by combining local cultural nuances with the global appeal of Puma.

Instagram Post

What makes Laila so interesting is her ability to present herself as a relatable and “real” figure, despite being an entirely virtual creation. As the team behind her explains, Laila was not made to seem “too artificial”—she is, in fact, deeply humanized. The brand used her as an interactive tool in Puma’s social media and digital marketing campaigns, generating buzz with her appearances in photoshoots, engagement with fans, and promotion of the latest collections.

Laila was designed to resonate with Morocco’s youth, embodying style, inclusivity, and empowerment, key attributes that Puma wanted to highlight in their messaging. As a result, the AI influencer garnered significant attention for Puma in the region, engaging fans on a new level and helping the brand deepen its connection with the Moroccan market.

The OG AI Influencer Maya by IPG Mediabrands

Back in 2020, Puma and IPG Mediabrands took a bold leap into the future of influencer marketing with the creation of Maya, one of the first AI-powered influencers designed to promote Puma’s products to a global audience. Maya was groundbreaking for its time, a digital persona that pushed the boundaries of what AI could do in the fashion world. With a focus on Puma’s core values—performance, innovation, and inclusivity—Maya was designed to engage audiences in fresh ways while also making a strong statement about how AI could bridge the gap between technology and fashion.

At the time, Maya’s blend of machine learning and AI-powered responses to followers was cutting-edge. She responded to comments, stayed on top of trends, and interacted with Puma’s audience through dynamic content pieces that showcased the brand’s products in innovative and authentic ways. Maya was not simply a virtual model; she was an evolving character, interacting with fans and participating in a conversation about the future of AI in fashion.

Instagram

However, looking back now, Maya’s design seems almost quaint in comparison to the more advanced AI influencers of today. While Maya’s digital persona helped pave the way for AI personalities in marketing, the technology available in 2020 was still in its early stages. Maya’s interactions were fairly scripted, and her appearance, though advanced at the time, did not have the same level of realism or fluidity seen in more recent AI influencers like Laila.

What set Maya apart was her role in sparking a conversation about the place of AI in the fashion and marketing industries. She wasn’t just a tool for selling products—she was part of a broader cultural dialogue. Through collaborations, virtual interviews, and her own evolving “brand,” Maya embodied the beginning of a shift in how digital personas could engage audiences, but today, her technology feels like a stepping stone to the more sophisticated, lifelike AI influencers that have followed.

Instagram

Despite this, Maya’s presence was pivotal in showing the world what was possible at the time and served as a benchmark for the AI influencers that would come after her. Her legacy lies in her pioneering role, opening the door to a new wave of digital personalities that continue to redefine what it means to be an “influencer” in the digital age.


Why AI Influencers Matter to a CEO

CEOs should care about AI influencers because they represent an evolution in digital marketing and customer engagement that can help brands scale, personalize, and maintain relevance in an increasingly digital world. As customer expectations shift towards more personalized experiences, AI influencers offer a unique way to fulfill those demands at a fraction of the cost and risk associated with traditional human influencers.

Moreover, AI influencers can be programmed to embody a brand’s ethos in ways that human influencers cannot always maintain. They can be optimized to handle high-volume interactions, offer always-on availability, and remain completely brand-aligned, avoiding the unpredictable nature of human behavior.

For CEOs seeking ways to increase ROI on digital marketing campaigns, or to carve out new avenues for growth in emerging markets, the rise of AI influencers is a compelling option. They provide a clear advantage in terms of cost-effectiveness, scalability, and brand control.

Core Value Proposition of AI Influencers

The core value proposition of AI influencers is their ability to offer brands a scalable, cost-effective, and hyper-personalized marketing solution that aligns perfectly with the digital-first world we live in. By leveraging the precision of artificial intelligence, AI influencers can seamlessly engage with target audiences, build meaningful relationships, and stay perfectly aligned with brand values—all while remaining immune to the uncertainties and risks that human influencers bring to the table.

Key Value Propositions:

  1. Personalized Engagement: AI influencers can analyze data to craft tailored messages that resonate deeply with an individual’s preferences, behaviors, and emotional triggers. This level of personalization drives higher engagement and conversion rates.
  2. Brand Consistency & Control: Unlike human influencers who may experience fluctuations in their behavior, AI influencers are fully programmable to embody the brand’s ethos, ensuring complete alignment with the company’s values, tone, and messaging.
  3. Scalability & Availability: AI influencers are always on, never requiring breaks or vacation time. This 24/7 engagement allows brands to maintain constant interaction with consumers across time zones and at scale—something human influencers can’t match.
  4. Cost-Effectiveness: With AI influencers, there’s no need for expensive celebrity endorsements or the risks associated with human influencers’ erratic behaviors. They can be “deployed” at a fraction of the cost, while maintaining high engagement.

Primer: Enabling Technology Behind AI Influencers

The creation and success of AI influencers are driven by a combination of several advanced technologies, each playing a critical role in the ability of these digital avatars to influence and engage with audiences. Here are the core technologies that enable AI influencers to exist and perform effectively:

Cloud computing provides the infrastructure needed to handle the massive amounts of data that AI influencers interact with daily. By using cloud platforms, AI influencers can process real-time data, access large-scale training datasets, and continuously update their behavior and appearance.

Artificial Intelligence (AI) & Machine Learning:

AI enables influencers to learn from vast amounts of data and adapt to changing trends, user behaviors, and interactions. Machine learning algorithms analyze engagement patterns and audience sentiment, helping AI influencers generate more effective content.

Natural Language Processing (NLP) allows AI influencers to engage in meaningful and contextually aware conversations with followers, giving them the ability to respond to comments, queries, and mentions with an understanding of human language nuances.

Computer Vision & Deep Learning:

Computer vision technology allows AI influencers to “see” the world through visual recognition systems. This is essential for creating realistic, human-like appearances, and enabling them to interact with visual content like photos and videos.

Deep learning algorithms help AI influencers improve over time by interpreting complex data—everything from human emotions to visual aesthetics—making them more relatable and “authentic.”

Generative Adversarial Networks (GANs):

GANs are crucial for creating realistic images and videos of AI influencers. These networks consist of two neural networks—one generating content (the generator) and the other evaluating it (the discriminator)—which improves the visual quality of the AI character over time.

GANs help AI influencers appear lifelike, making them more engaging and relatable to audiences.

Synthetic Media & Digital Human Creation:

Using synthetic media, AI influencers can be created as fully virtual personas that appear in videos, photoshoots, and other digital media. This allows brands to create personalized avatars that resonate with specific target markets.

Digital human creation technology involves crafting AI-driven characters that mimic human behavior, facial expressions, and body language, all while retaining complete control over the digital persona.

Social Media Automation Tools:

AI influencers are empowered by social media automation technologies that allow them to manage and optimize interactions on platforms like Instagram, Twitter, and TikTok. These tools help AI influencers post content, reply to comments, and stay engaged with their audience automatically, ensuring a seamless experience.

Elevator Pitch: Adding AI Influencers to a MarCom Plan

Imagine a digital ambassador for your brand that works around the clock, never gets sick, and can interact with your customers in a personalized, meaningful way. That’s what an AI influencer offers. From generating authentic engagement to showcasing your products in creative, cutting-edge ways, AI influencers bring a unique blend of innovation, consistency, and precision to your marketing communications. Whether you’re looking to increase brand awareness, build deeper connections with consumers, or tap into new markets, AI influencers are an indispensable tool in your marketing arsenal.


BLACK HAT BEATDOWN – WARNING THIS IS EXPERIMENTAL

TRUST NOTHING. LOVE NOTHING. AGREE WITH NOTHING UNTIL IT SURVIVES A BEATING.

Alright, let’s tear this Elevator Pitch apart like it’s a bad alibi at a crime scene. First off, AI influencers? Really? Let me break this down, because we’re already knee-deep in fantasy land.

  1. Authenticity? Sure, you’re calling it “authentic engagement” like it’s the next holy grail. But authenticity requires human flaws—imperfection. Real people screw up, show vulnerability, and have stories that are hard to replicate, even with advanced algorithms. Your AI influencer is what? A programmed parrot? It doesn’t live, breathe, or feel. And consumers—especially now—are smart enough to sniff out a plastic smile from a mile away. If they feel like they’re being sold by a bot, they’re gone. End of story.
  2. Consistency? Consistency is a trap. Consistency breeds stagnation. It’s like trying to turn a serial killer into a law-abiding citizen by giving them a routine. People love the unpredictable, the surprising. AI might pump out content every hour, but it’ll be the same damn thing every time, almost like your brand’s trying to beat the same dead horse. Guess what? Consumers hate repeating themselves—especially when it’s the same old pre-packaged content.
  3. “Building Connections”? Have you ever tried connecting with a toaster? Same thing. AI doesn’t have real experiences or genuine emotions, so it can’t actually connect with people. You think it’s going to emotionally engage someone just because it can “talk” to them? Newsflash: it doesn’t feel, it doesn’t grow, it doesn’t care. It’s a well-dressed algorithm with a pre-scripted persona. How is that deeper than a poorly executed sales pitch?

Here’s the harsh reality: brands need real human touch, not digital puppets. The second your customers realize they’re chatting with a bot instead of a person, you’ve lost. It’s a crutch that brands are using to hide their inability to build actual, human-driven relationships. This isn’t innovation; it’s desperation wrapped in a digital disguise.

SOURCE:

Laila: > LINK

Maya: > LINK


AI fintech agents: Fifteen examples of the edge being cut.

Velocity Ascent Live · January 21, 2025 ·

These 15 AI agents represent the future of automation, decision-making, and workflow orchestration in fintech.

What if your software could not only read and write — but also learn about your particular business, its history, competitors, challenges, successes – and even the less than successful ventures.
With that knowledge your software will make a range of decisions, handle exceptions, and dynamically coordinate entire workflows while keeping you aware every step of the way.

Nerd Alert – Welcome to the era of AI agents: the successors to rigid RPA (Robotic Process Automation) bots and linear “automation” tools. These very smart, self-directed systems transform industries by bridging the gap between human and machine, enabling real-time financial analysis with complex task orchestration — all with human oversight as needed.

If you’re in fin tech — or almost any digital-heavy industry, — you’ve certainly noticed the shift towards automation and artificial intelligence. The rate at which the AI agent landscape is growing can feel overwhelming.

I jumped into that raging river of AI tools and came out holding 15 handpicked AI fintech agents across finance (obviously) and a mix of general-use categories that are a good starting point for testing the agent waters on what appears to be the future of business operations. These are AI-based tools are bespoke designed to manage your specific workflows, process live and historical data, and make a set of decisions without the need for constant human input.

Note: the following section has been written with a heavy dose of key words to feed those ever-hungry content bots. As always this list in no way implies endorsement – use at your discretion.

Fifteen Fintech AI Platforms:

  1. OpenBB – AI for Financial Analysis
    OpenBB is an open-source platform designed for finance professionals to access financial data, perform analysis, and automate reporting. It’s especially valuable for those in the fintech and merchant cash advance sectors, where real-time data analysis and decision-making are crucial. You can integrate it with other financial tools to streamline your operations.
    Explore OpenBB
  2. Docisional – AI for Business Decision Support
    Docisional helps businesses analyze financial data and make intelligent decisions by using AI-driven insights. It can integrate with CRM systems, financial databases, and even machine learning models to forecast trends and generate action plans. Ideal for fast-paced environments like fintech.
    Explore Docisional
  3. Jasper AI – Content Creation and Marketing Automation
    In the age of content-driven marketing, Jasper AI can help create copy, blog posts, and even financial reports. For fintech companies that need to produce regular content and communicate with clients, this tool saves time and improves quality.
    Explore Jasper AI
  4. CalypsoAI – Risk and Security Management
    For businesses involved in financial transactions, such as merchant cash advances, CalypsoAI is an essential tool for risk management and fraud detection. Its AI-driven solutions can help prevent fraudulent activity in real-time while maintaining secure transactions.
    Explore CalypsoAI
  5. LangChain – Multimodal AI for Data Retrieval
    For businesses managing complex data systems, LangChain simplifies information retrieval through natural language processing. It can help streamline workflows, making it easier to extract and use critical financial data in the fintech sector.
    Explore LangChain
  6. Cognigy – AI for Customer Support
    Cognigy powers AI-driven customer service chatbots. Its natural language processing capabilities make it perfect for handling customer inquiries in the merchant cash advance space, where fast, responsive communication is essential.
    Explore Cognigy
  7. Kahuna Labs – Automation for Financial Data
    Kahuna Labs uses AI to automate data handling in the financial services space. With this agent, fintech firms can automate credit scoring, loan processing, and even payment reconciliation, making business operations more efficient.
    Explore Kahuna Labs
  8. Fiddler AI – Explainable AI for Financial Services
    For financial firms that need transparency and accountability, Fiddler AI helps by making AI decisions interpretable and understandable. This ensures that data-driven decisions in fintech are both effective and compliant.
    Explore Fiddler AI
  9. D-ID – AI for Document Verification
    In the merchant cash advance industry, document verification is a crucial step in the process. D-ID uses AI to verify identity documents and validate their authenticity quickly and accurately, reducing errors and fraud.
    Explore D-ID
  10. Skyflow – Privacy and Data Security AI
    For any fintech business handling sensitive customer data, Skyflow provides a privacy-first data platform powered by AI. It helps ensure that financial data remains secure, private, and compliant with industry regulations like GDPR.
    Explore Skyflow
  11. Replit – Developer-Friendly AI for Building and Scaling
    Finally, Replit allows developers to integrate AI into their software more efficiently. For fintech companies building their own applications or automating internal processes, Replit can accelerate development, integration, and deployment of AI features.
    Explore Replit
  12. HoneyHive – AI for Financial Data Insights
    HoneyHive is an AI-powered analytics platform that helps fintech companies derive actionable insights from large datasets. It’s particularly useful for uncovering patterns in transaction data and identifying growth opportunities in real-time.
    Explore HoneyHive
  13. Prophet – Predictive Analytics for Financial Forecasting
    Prophet specializes in predictive analytics and time-series forecasting. It’s a perfect solution for fintech firms looking to forecast cash flows, investment performance, and market trends — helping businesses make proactive, data-backed decisions.
    Explore Prophet
  14. Gracker.ai – AI for Financial Risk Assessment
    Gracker.ai uses machine learning to provide real-time risk assessments for financial transactions. It helps fintech companies identify potential risks and prevent fraud before it happens, making it a key tool for anyone handling sensitive financial data.
    Explore Gracker.ai
  15. Radiant Security – AI for Cybersecurity
    As fintech companies deal with large amounts of financial data, Radiant Security offers an AI-driven cybersecurity solution that can detect and mitigate threats in real-time. It’s an essential tool for protecting both your company’s and your clients’ financial information from evolving cyber threats.
    Explore Radiant Security

… And there you have it – 15 of the latest collection of AI tools for fintech – just remember that half of these tools are probably obsolete at publish date. 🙂

Embrace the AI-Driven Fintech Future

The fusion of AI and fintech isn’t just a trend—it’s a revolution reshaping how we manage money, streamline operations, and innovate in the financial world. From automating mundane tasks to delivering hyper-personalized financial advice, the fifteen examples we’ve explored showcase the power of AI agents to transform both individual experiences and institutional efficiency.

Whether you’re an entrepreneur, a busy professional, or just curious about how AI is changing the world, these AI agents are built to make your life easier, your workflows smoother, and your financial operations more intelligent. The best part? Many of these tools are open source, meaning you can customize them to meet your specific needs and integrate them into existing systems. With options ranging from task automation to advanced data analysis, these agents adapt to your goals, saving you time and effort while enhancing productivity.

So, where do you go from here? The future of fintech lies in measured innovation—adopting these tools thoughtfully to balance progress with stability. Here are some actionable steps you can take today to join this transformation:

  • Experiment with Open-Source Tools: Dive into platforms like H2O.ai or LlamaIndex and test how they can automate tasks or analyze data for your personal or business finances.
  • Stay Informed: Follow fintech innovators on social media (like @stripe or @Klarna) to see real-time updates on how AI agents are evolving and being applied in the industry.
  • Start Small: Pick one AI agent from the list—say, a chatbot for customer service or a fraud detection tool—and integrate it into your staging workflow. Then measure the impact on your time and bottom line.
  • Join the Conversation: Share your thoughts or questions about AI in fintech in on social media to discuss how these agents might work for you.

The financial landscape is shifting fast, and AI agents are at the helm. Whether you’re looking to optimize your budget, scale a startup, or simply explore cutting-edge tech, now’s the time to take a step toward this intelligent future. How will you harness these tools to elevate your financial game? Let’s keep the momentum going—your next breakthrough might be just one AI agent away.

AI Biomechanical Engines: powering real-world solutions.

Velocity Ascent Live · December 20, 2024 ·

“AI-generated biomechanical engines” refers to a conceptual or technological framework that integrates artificial intelligence (AI) with biomechanical systems to create dynamic, adaptable mechanisms or devices.

That sure is a mouthful 🙂

Putting it simply, these engines could be a blend of biological principles (like human or animal movement) and mechanical or robotic engineering, powered or optimized through AI algorithms. Here’s a breakdown of the components:

1. AI (Artificial Intelligence): AI in this context would involve algorithms and computational models that enable machines or systems to learn, adapt, and make decisions, often based on data inputs. This could include machine learning, neural networks, or deep learning techniques, which allow systems to improve over time or adapt to new conditions.

2. Biomechanical: This term refers to the study and application of mechanical principles to biological systems. It typically involves understanding how living organisms, such as humans or animals, move, function, and interact with their environments. In the context of “engines,” it could refer to mechanical systems that replicate or augment biological movement, such as in prosthetics, robotics, or exoskeletons.

3. Engines: In this context, “engines” likely refers to systems or machines that drive or power a mechanism. It can include anything from the propulsion systems in robots to more complex devices designed to mimic biological functions, such as limbs, joints, or circulatory systems.

Practical Examples:

• Robotics: Robots that use AI to adapt their movements based on real-time data from their environment, effectively mimicking human or animal biomechanics. For instance, robots that use AI to improve their walking gait based on biomechanical principles.

• Prosthetics & Exoskeletons: AI-driven prosthetics or exoskeletons that use machine learning to optimize the movement of artificial limbs in ways that mimic natural human motion. These devices could learn to respond to a user’s intent more fluidly, adjusting for different terrains or activities.

• Biohybrid Systems: Devices that combine both biological and mechanical components, powered by AI to enhance movement or function. An example might be biohybrid robots that incorporate living cells or tissues, with AI systems guiding their movement.

Future Implications:

In the future, AI-generated biomechanical engines could lead to more advanced prosthetics, adaptive robotic limbs, and even new forms of biological augmentation.

Such systems could be used in medicine, personal assistance, military applications, or even in enhancing human capabilities. By integrating AI and biomechanics, these systems might be able to respond to their environment in real-time, providing more natural, human-like, or even superhuman abilities.

Examples in current practice

Certainly! Here are the links to the relevant sources for the examples I mentioned:

1. OpenAI’s Dactyl

• Overview of Dactyl:

• OpenAI’s Robotics: Solving Rubik’s Cube with a Robotic Hand (2018)

• OpenAI’s Dactyl

Dactyl uses reinforcement learning to manipulate objects like the Rubik’s Cube, demonstrating the combination of AI and biomechanical robotics.

2. Boston Dynamics’ Atlas

• Boston Dynamics Atlas:

• Atlas Robot – Boston Dynamics

• Atlas’ Parkour Performance (Video demonstration)

Atlas is known for its ability to perform complex physical tasks like running, jumping, and backflips using advanced machine learning algorithms to simulate human-like biomechanics.

3. Honda’s ASIMO

• Honda ASIMO:

• ASIMO – Honda Worldwide

• ASIMO’s Capabilities and Developments (ASIMO demo video)

ASIMO is a humanoid robot that combines AI with biomechanics to perform human-like movements, from walking to dancing, and adapt to its environment in real time.

4. ExoAtlet Exoskeleton

• ExoAtlet – Exoskeleton:

• ExoAtlet Official Website

• ExoAtlet Rehabilitation Exoskeleton (YouTube video)

The ExoAtlet exoskeleton is designed to help people with mobility impairments regain movement and stability using AI to adapt to the user’s biomechanics.

5. Sharp’s RoBoHoN

• RoBoHoN by Sharp:

• RoBoHoN Official Page – Sharp

• RoBoHoN in Action (YouTube)

RoBoHoN is a humanoid robot that integrates AI to enable complex movements like walking and conversation. It is a smaller, more interactive robot with applications in communication and mobility.

These resources provide additional context and examples of how AI has been applied to biomechanical engines, including robots and exoskeletons.

Fintech in Focus: AgenticAI is changing the Innovation Landscape.

velocityascent · November 29, 2024 ·

Ultimately, the human remains the final decision-maker, ensuring accountability and contextual judgment in critical scenarios.

The fintech and hedge fund sectors thrive on innovation, but adopting new technology comes with its own risks and responsibilities. Measured adaptation—like gradually integrating Agentic AI—allows organizations to innovate while maintaining stability and trust. In this post, we explore how emerging technologies, ethical considerations, and novel strategies are transforming the financial world.

What is Agentic AI?
Agentic AI refers to artificial intelligence systems designed to operate as autonomous agents that can analyze data, make decisions, and execute tasks with minimal human intervention. In fintech, Agentic AI can be used to create “digital twins” of team members—virtual replicas that simulate human decision-making in a closed environment that enhance productivity and collaboration. By using Twins, companies can streamline routine and mundane processes, freeing the team members to focus more energy on mission and innovation.

Ultimately, the human remains the final decision-maker, ensuring accountability and contextual judgment in critical scenarios.

1. Emerging Technology: Driving the Next Wave of Financial Innovation

  • AI-Powered Insights:
    Hedge funds like Two Sigma have embraced machine learning to analyze non-linear relationships in market data, helping them outperform traditional quant models. The measured integration of AI ensures algorithm reliability before full-scale deployment.
  • Blockchain Beyond Bitcoin:
    JPMorgan’s blockchain platform, Onyx, facilitates secure, efficient interbank payments. This cautious adoption of blockchain technology focuses on specific, scalable use cases instead of speculative ventures.
  • Quantum Computing:
    Although still in its infancy, quantum computing firms like D-Wave are collaborating with financial institutions on experiments in portfolio optimization. A measured approach involves using quantum technology for supplementary analyses rather than replacing conventional models.
  • Edge Computing & Real-Time Decision Making:
    Nasdaq’s use of edge computing allows for microsecond-level trade execution while maintaining centralized oversight to prevent errors.

2. Ethical and Privacy Concerns in a Hyperconnected World

  • Data Privacy and Usage:
    Apple’s differential privacy model showcases how firms can analyze aggregated user data without compromising individual identities. Fintech companies could adopt similar anonymization techniques for customer analytics.
  • AI Bias and Fairness:
    A study showed racial biases in credit scoring algorithms. By slowly rolling out AI with robust testing frameworks, companies like FICO are working to ensure fairness without jeopardizing operational efficiency.
  • Cybersecurity Threats:
    Hedge fund Citadel’s investment in zero-trust architecture highlights the importance of assuming all network traffic is potentially compromised and taking preventive measures accordingly.
  • ESG Metrics:
    BlackRock’s use of Aladdin Climate software ensures portfolios meet ESG benchmarks without compromising returns. The gradual introduction of such tools avoids alienating traditional investors.

3. Emerging Investment Strategies: Navigating a New Era

  • Sustainable Investing:
    The rise of renewable energy ETFs demonstrates how measured allocations to green portfolios can balance innovation with risk management.
  • Alternative Data Sources:
    Hedge funds like Renaissance Technologies have leveraged satellite imagery for crop yield predictions. However, their success lies in testing alternative data methods on historical datasets before integrating them into live strategies.
  • Decentralized Finance (DeFi) Opportunities:
    Aave offers decentralized lending protocols. Firms that explore DeFi cautiously by using small experimental funds can gain early insights without exposing significant capital.
  • Hybrid Models:
    Morgan Stanley’s AI-supported human advisors showcase the value of combining machine efficiency with human intuition. Gradual integration ensures customers retain trust in the personal touch.

Conclusion: Adapting to the New Normal

The future of fintech and hedge funds lies in measured innovation. Technologies like Agentic AI offer transformative potential, but thoughtful integration is key to sustainable success. Leaders who embrace change with calculated steps will shape the next chapter of financial excellence.

Next steps…
How will you measure your steps in the journey toward innovation? Our approach is process based and responsive. Let’s discuss.


Addendum: Live Links to Examples

  1. Two Sigma: AI and Machine Learning
  2. JPMorgan Onyx Blockchain Platform
  3. D-Wave Quantum Computing
  4. Nasdaq and Edge Computing
  5. Apple’s Differential Privacy Model
  6. FICO and Fairness in AI
  7. Citadel and Zero-Trust Architecture
  8. BlackRock Aladdin Climate Software
  9. Renewable Energy ETFs
  10. Renaissance Technologies
  11. Aave’s DeFi Protocols
  12. Morgan Stanley AI Advisors



Reality Check: Federated Agentic and Decentralized Artificial Intelligence

Joe Skopek · September 12, 2024 ·

AI is evolving rapidly, and a new approach is gaining momentum: agentic AI. Unlike current tools like ChatGPT, which require human input to operate, agentic AI is designed to act independently—monitoring competitors’ marketing efforts, scheduling real-time content updates, or predicting real-world equipment needs—without waiting for instructions. As these systems take on more autonomy, robust security measures become essential to ensure their actions remain safe, aligned, and trustworthy.

MIT Media Lab – Ramesh Raskar: A Perspective on Decentralizing AI

“A.I. co-pilots, assistants and agents promise to boost productivity with helpful suggestions and shortcuts. “

New York Times, September 2024

While this technology is still in the early stages, it’s being hyped as the next big thing in AI, promising to boost productivity and innovation. However, we’re not there yet—agentic AI is mostly a vision for the future that’s rapidly approaching.

Governance Challenges: Accountability, Regulation, and Security

Governance issues with Agentic AI and decentralized computing stem from a lack of centralized control, making regulation and enforcement difficult across jurisdictions. In decentralized systems, no single authority oversees operations, while in Agentic AI, autonomous decisions raise questions about accountability, such as who is responsible when things go wrong—developers, users, or the AI itself.

Ethical and legal compliance is a significant challenge, as both agentic AI and decentralized systems often operate beyond traditional frameworks, making it difficult to ensure they adhere to laws or ethical guidelines.

Security is another concern. Decentralized systems may suffer from vulnerabilities due to inconsistent protocols, while agentic AI can be manipulated or exhibit harmful behaviors. Existing regulatory frameworks are frequently outdated, creating oversight gaps for these emerging technologies.

Both technologies also face issues with coordination and standardization. Decentralized systems require consensus among many participants, which can slow progress, and agentic AI currently lacks widely accepted standards.

Finally, the lack of transparency in AI decision-making, combined with the difficulty of auditing decentralized systems, further complicates governance and accountability.

This is where Federated Machine Learning offers a compelling solution.

Federated Machine Learning (FedML)

FedML is an approach that enables organizations with limited data—so-called “small data” organizations—to collaboratively train and benefit from sophisticated machine learning models. The definition of “small data” depends on the complexity of the AI task being addressed.

In Pharma, for example, having access to a million annotated molecules for drug discovery is relatively small in view of the vast chemical space.

In Marketing that small data set might be in the form of brand specific visual data—brand guidelines scattered across PDFs, emails, and shared drives.

Image: Jing Jing Tsong/theispot.com

Is Federated Agentic AI the answer?

Federated Agentic AI refers to a blend of two advanced AI concepts: federated learning and agentic AI.

Federated learning enables AI models to be trained across decentralized devices or data sources while keeping the data local and secure, thereby enhancing privacy and scalability. Meanwhile, agentic AI refers to self-contained systems that, once implemented by humans, operate autonomously around the clock. These systems are capable of controlled decision-making and can adapt based on real-time data without further human intervention.

When combined, Federated Agentic AI allows multiple autonomous agents to collaborate across a secure distributed network. These agents can handle tasks independently while continuously learning from local data sources, without needing to share sensitive information across the network. This setup is particularly useful in environments like healthcare, finance, or IoT, where data privacy is critical but complex tasks still require intelligent automation.

For instance, a federated agentic system might be deployed in a network of smart devices where each device autonomously manages specific tasks (e.g., thermostats optimizing energy use) while learning from local data (e.g., weather conditions). These devices can also share insights without revealing user data, improving overall system efficiency and privacy​.

Final Thoughts: Designing for new technology is a completely different challenge from a traditional design project.

Typically, users already know how to interact with familiar products—like swiping a credit card at a payment terminal or using a TV remote to change channels. But with emerging technology, there are no familiar cues, making it harder for users to figure out how to engage with it effectively. Think back to when users first encountered smartphones—there was no clear precedent for touchscreens or gestures, making it challenging to learn entirely new interactions.

Adoption of new tech often lags behind confusion and speculation, so creating a seamless, intuitive user experience is essential for success.

That said, designing something entirely new isn’t easy—but it’s exactly where the team at Velocity Ascent excels. We navigate the space between excitement for emerging technology and the need to deliver real, secure, user-centered value. By following key principles, we transform unfamiliar tech into products that people and teams love to work with.

For CMOs, this means a potential shift in how to approach marketing and customer engagement. As AI becomes more autonomous, the question will be: how do we control and guide these powerful tools to enhance our strategies while still ensuring privacy? Ultimately it is about using AI in smarter, more effective ways to drive business growth.

Sources:

Analytics Vidhya, The GitHub Blog

MIT Media Lab: Decentralized AI Overview

A Perspective on Decentralizing AI

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