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The $1 Trillion Agent Factory: How Generative AI Is Printing Power (Not Just Productivity) – Navigating the Future With Generative AI, Part 5

1. Why AI Agents Are the New Currency of Power

What if your company, or your entire nation, possessed the ability to print productivity by harnessing intelligence at scale?

This concept gained concrete form with the announcement of an unimaginable $500 billion investment in artificial intelligence infrastructure, which excludes the additional €331 billion independently committed by industry titans like Meta, Microsoft, Amazon, xAI, and Apple. This agreement, spearheaded by President Trump, involves a partnership between the CEOs of OpenAI (Sam Altman), SoftBank (Masayoshi Son), and Oracle (Larry Ellison). Their joint venture, named “Stargate”, aims to build “the physical and virtual infrastructure to power the next generation of advancements in AI,” creating “colossal data centers” across the United States, promising to yield over 100,000 jobs.

France and Europe, not to be outdone, responded swiftly. At the AI Action Summit in Paris in February 2025, French President Emmanuel Macron announced a commitment of €109 billion in AI projects for France alone, highlighting a significant moment for European AI ambition. This was followed by European Commission President Ursula Von Der Leyen’s launch of InvestAI, an initiative to mobilize a staggering €200 billion for investment in AI across Europe, including a specific €20 billion fund for “AI gigafactories.” These massive investments, on both sides of the Atlantic, show a clear objective. The highest level of commitment reflects understanding. This fact translates into one reality: being a civilization left behind is simply out of the question.

But the stakes in this global AI game are constantly rising. If the US and Europe thought they were holding strong hands, China, arguably the most mature AI nation, has just raised the pot. China is setting up a national venture capital guidance fund of 1 trillion yuan (approximately €126.7 billion), as announced by the National Development and Reform Commission (NDRC) on March 6, 2025. This fund aims to nurture strategic emerging industries and futuristic technologies, a clear signal that China intends to further solidify its position in the AI race, focusing among others, on boosting its chip industry.

The implicit “call” to the other players is clear: Are you in, or are you out?

Therefore, my first question isn’t issued from a sci-fi movie; it’s not some fantastical tale ripped from the green and black screen of The Matrix, where programs possessed purpose, life, and a face.

This is about proactively avoiding the Kodak moment within our respective industries.

This is about your nation avoiding the declining slope of Ray Dalio’s Big Cycle, where clinging to outdated models in the face of transformative technology is a path toward obsolescence.

raydalio the big cycle

This is the endgame: AI Agents are not just changing today—they are architecting the future of nations.

2. Architecting Sovereignty: How Nations Are Industrializing Intelligence

In November 2024, I had the privilege of delivering a second course on Digital Sovereignty, focused on Artificial Intelligence, at the University of Luxembourg, thanks to Roger Tafotie. I emphasized that the current shift toward advanced AI, especially Generative AI, represents a paradigm shift unlike before. Why? Because, for the first time in history, humanity has gained access to the near-infinite scalability of human-level intelligence. Coupled with the rapid advancements in robotics, this same scalability is now within reach for physical jobs.

05 Architecture of Digital Sovereignty

Digital Sovereignty in the age of AI is a battle for access to the industrialization of productivity. Consider the architecture of AI Digital Sovereignty as a scaffolding built of core capabilities, much like interconnected pillars supporting a grand structure:

  • Cloud Computing: The foundational infrastructure, the bedrock upon which all AI operations rest.
  • AI Foundation Model Training: This is where the raw intelligence is refined, like a rigorous academy shaping the minds of future digital workers.
  • Talent Pools: The irreplaceable human capital, the architects, engineers, data scientists, and strategists who drive innovation. These are the skilled individuals, the master craftspeople, forging the tools and directing the symphony of progress.
  • Chip Manufacturing: The ability to produce advanced CPU, GPU, and specially designed AI chips, like TPU and LPU, guarantees independence.
  • Systems of Funding and Investments: The ability to finance a long-term, consistent, and high-level commitment toward AI capabilities.

If you need a tangible example of how critical resources like rare earth metals and cheap energy are to this race, look no further than President Donald Trump’s negotiations with Volodymyr Zelenskyy. The proposed deal? $500 billion in profits, centered on Ukraine’s rare earth metals and energy reserves. Let’s not forget: 70% of U.S. rare earth imports currently come from China. Control over these resources is the lifeblood of AI infrastructure.

It’s not merely about isolated components but how these elements interconnect and reinforce each other. This interconnectedness is not accidental; it’s the key to true sovereignty – the ability to use AI and control its creation, deployment, and evolution. It’s about building a self-sustaining ecosystem, a virtuous cycle where each element strengthens the others.

Europe initially lagged, but the competition has only just begun. The geopolitical landscape will be a major, unmastered factor.

The current market “game” consists of finding the critical mass between the hundreds of billions invested in R&D, the availability of “synthetic intelligence”, and unlocking a new era of sustainable growth. The race to discover the philosophical stone—to transform matter (transistors and electricity) into gold (mind)— and to achieve AGI and then ASI is on.

Sam Altman knows it; his strategy is “Usain Bolt” speed: the competition cannot keep up if you move at a very fast pace with AI product innovation. Larry Page and Elon Musk intuitively grasped this first. OpenAI was not only created to bring “open artificial intelligence” to each and every human, but also serves as a deliberate counterweight to Google DeepMind. Now, Sundar Pichai feels the urgency to regain leadership in this space, particularly now that Google Search—the golden goose — is threatened by emerging “Deep Search” challengers such as Perplexity, ChatGPT, and Grok.

As of March 2025, HuggingFace, the premier open-source AI model repository, has more than 194,001 text generation models (+24.1% since November 2024) within a total of 1,494,540 AI models (+56.8 % since November 2024). Even though these numbers include different versions of the same base models, think of them as distinct blocks of intelligence. We are already in an era of abundance. Anyone possessing the necessary skills and computational resources can now build intelligent systems from these models. In short, the raw materials for a revolution are available today.

The stage is set: The convergence of human-level intelligence scalability and robotics marks a profound moment in technological history, paving the way for a new era of productivity.

3. The Revolution of the Agentic Enterprise

In October 2024, during the Atlassian Worldwide conference Team 24 in Barcelona, I had the privilege of seeing their integrated “System of Work” firsthand.

Arsène Wenger, former Football Manager of Arsenal FC and current FIFA’s Chief of Global Football development, was invited to share his life experiences in a fireside chat. It was a true blessing from an ultimate expert in team building and the power of consistency.

Arsene Wenger

Mr. Wenger articulated that the conditions and framework for achieving champion-level performances rely on a progressive and incremental journey. While talent can confer a slight edge, that edge remains marginal in the realm of performance. The key differentiator resides in consistent effort, with a resolute commitment to surpassing established thresholds. Regularly implementing extra work and consistently reaching your capacity is what separates a champion from the rest.

Thus, Atlassian pushed their boundaries, unveiling Rovo AI, an agentic platform native to their environment. Rovo is positioned at the core of the “System of Work,” bridging knowledge management with Confluence and workflow mastery with Jira. To my surprise, Atlassian announced they have 500 active Agents! This is a real-world example of printing productivity by deploying purpose-driven digital workers to the existing platform.

But the true brilliance lies in making this digital factory available to their customers. This type of technology should be on every CEO and CIO’s roadmap. How you integrate this capability into your business and technology strategy is the only variable – the fundamental need for it is not.

The Agentic Enterprise is the cornerstone of this change: creating autonomous computer programs (agents) that can handle tasks with a broad range of language-based intelligence.

We’ve transitioned from task-focused programming to goal-driven prompted actions. Programming still has a role to play as it guarantees perfect execution, but the cognitive capabilities of large language reasoning models like GPT O3 and Deepseek R1 lift all its limitations. Moreover, in IT, development is now about generated code. While engineering itself does not necessarily become easier – you still need to care about the algorithm, the data structure, and the sequence of your tasks – the programming part of the process is drastically simplified.

After years of prompt engineering since the GPT3 beta release at the end of 2020, I concluded that prompt engineering is not a job per se but a critical skill.

The Agentic Enterprise is not a distant dream but a present reality, fundamentally changing how organizations construct and scale their work.

4. Klarna’s AI-Powered Customer Service Revolution: How AI Assumes 2/3 of the Workload

In February 2024, the Swedish fintech Klarna announced that their AI contact center agent was handling two-thirds of their customer service chats, performing the equivalent work of 700 full-time human agents. It operates across 23 markets, speaks 35 languages, and provides 24/7 availability.

With full speech and listening capabilities now available in models like Gemini 2 Realtime Multimodal Live API, OpenAI Realtime API, or VAPI, the automation opportunities are virtually limitless.

What made this rapid advancement possible, suddenly?

Technically, the emergence of multimodality in AI models, robust APIs, and the decisive capability of Function Calling form the foundation. But more importantly, this transformation is primarily a business vision; it lies in adopting transformative technology as the main driving power, and using innovation as your key distinguishing factor, just as with Google and OpenAI. When this approach acts as the central nervous system in business strategy, then, the adoption is not perceived as a fundamental disruption but, rather, a gradual and consistent reinforcement.

The crucial capability here is Function Calling, which allows AI models to tap into skills and data beyond their inherent capabilities. Think of getting the current time or converting a price from Euros to Swiss Francs using a live exchange rate – things the model can’t do on its own. In a nutshell, Function Calling lets the AI interact directly with APIs. It’s like giving the AI a set of specialized tools or instantly teaching it new skills.

APIs are the foundation for the intelligence relevance of AI Agents and their ability to use existing and new features based on user intent. In contrast to prior generations of chatbots, which needed explicit intent definitions for each conversation flow, LLMs now provide the intelligence and the knowledge “out of the box.”. To further understand the fundamental importance of APIs for any modern business, I invite you to read my article or listen to my podcast episode titled “Why APIs are fundamental to your business“.

This has led to the rise of startups like Bland.ai that offer products like Call Agent as a service. You can programmatically automate an agent to respond over the phone, even customizing its voice and conversation style – effectively creating your own C3PO.

ElevenLabs, the AI voice company that I use for my podcast, has also launched a digital factory for voice-enabled agents.

Then, December 18 2024, OpenAI introduced Parloa, a dedicated Call Center Agent Factory. This platform represents the first of its kind, specializing in digital workers for a specific industry vertical.

Parloa

The promise? To transform every call center officer into an AI Agent Team Leader. As a “Chief of AI Staff”, your objective will be to manage your agents efficiently, handling the flow of demands, intervening only when necessary, and reserving human-to-human interactions for exceptional client experiences or complex issues.

The revolution in customer service is already here, driven by new AI-based call center solutions. This is a sneak peek of the AI-driven future.

5. The Building Blocks: AI Development Platforms

To establish a clear vision for an AI strategy and augment my CTO practice, I meticulously tested several AI technologies. My goal is to validate the technological maturity empirically, assert the productivity gains, and, more importantly, define the optimal AI Engineering stack and workflow. These findings have been documented within my AI Strategy Map, a dynamic instrument of vision. As a result, my day-to-day habits have completely changed and reflect my emergence as a full-time “AI native”. My engineering practice is reborn in the “Age of Augmentation“.

I changed my stack to Cursor for IT development, V0.dev for design prototyping, and ChatGPT o3 for brainstorming and review. The results achieved so far are highly enlightening and transformational.

Thus, engineering teams’ next quantum leap is represented by the arrival of Agentic IDEs, facilitating an agent-assisted development experience. The developer can seamlessly install the IDE, create or import a project, input a prompt describing a feature, and observe a series of iterative loops leading to the complete implementation of the task. The feature implementation is successful in 75% of cases. In the remaining 25%, the developer issues a corrective prompt to secure 100% implementation, indicating a need to supervise such technology when used as an independent digital worker.

Leading today’s innovative Agentic IDE market are:

Then, the landscape of mature AI frameworks gives companies a great array of enterprise-ready solutions. Automated agent technologies, specifically, have emerged as critical tools:

Finally, DEVaaS platforms are completely redefining the approach to IT delivery :

These technologies allow you to build applications from the ground up that are fully operational right from the start, without any additional setup time. Yet, it is not only about developing an application from scratch and then gradually adding features by using prompts as your instruction; it’s also about application hosting. These solutions now offer a complete DevOps and Fullstack experience.

Although they currently yield simple to moderately complex applications, and may not be entirely mature, the technologies are demonstrably improving at rapid paces. It is only a matter of weeks (not months) before seeing full system decomposition and interconnectedness that reaches corporate standards.
This is exactly what the corporate environment has been waiting for.

How will this impact the IT organization? It comes down to these three outcomes:

First, for companies where the IT organization is a strategic differentiator, the internal IT workforce will increase productivity by delegating development tasks directly to internal AI Agents. Sovereign infrastructure is most likely required in heavily regulated or secretive industries.

Second, companies where IT is a primary activity, but not a core organizational driver can outsource the IT development to specialized companies who also make use of Agentic development IDEs or platforms.

Finally, an option for outsourcing includes the reconfiguration of jobs and empowering base knowledge workers – individuals with no official IT background – to IT positions through AI training. This would be the full return of the Citizen Developer vision, previously promised by Low Code/ No Code trends.

Innovation within the AI domain occurs several times per day, thus requiring a proactive mindset to remain updated technically which leads into actionable technology perspectives.

6. The Power of Uni-Teams: The Daily Collaboration Between Man and Machine

What does it feel like to lead your own AI team?

From experience, it make you feels like Jarod, from the TV show “The Pretender“, a true one-man band capable of handling multiple roles.

Think of it this way, you are now capable of:

  • Writing code like a developer
  • Creating comprehensive documentation like a technical writer
  • Offering intricate explanations like an analyst
  • Establishing policy frameworks like a compliance officer
  • Originating engaging content like a content writerData storytelling like a data analyst
  • Data storytelling like a data analyst
  • Building stunning presentations like a graphic designer

From my experience, reaching mastery in any discipline often reveals an observable truth within the corporate world: a significant portion of our tasks are inherently repetitive. The added value is not in running through the same scaffolding process many, many times, if you are no longer learning (except perhaps to reinforce previous knowledge). The real value is in the outcome. If I can speed up the process to focus on learning new domain knowledge, multiplying experiences, and spending time with my human colleagues, it is a win-win.

My “Relax Publication Style“, an guided practice for anyone starting in content creation, has evolved into a more productive and enjoyable method – combining iterative feedback cycles from human/AI, to leverage strategic insights via structured ideas, ongoing AI-reviews, personal updates and enriched content using human-curation combined with in-depth AI search tools. I will explore this process in a future article.

One of the most surprising shifts in my workflow has been the emergence of “background processing” orchestrated by the AI Agent itself. It’s a newfound ability to reclaim fragments of time, little pockets of productivity that were previously lost. It unfolds something like this:

  1. The Prompt: I issue a clear directive, the starting point for the AI’s work.
  2. The Delegation: I offload the task to the AI, entrusting it to this silent, tireless digital worker.
  3. The Productivity Surge: I’m suddenly free. My capacity is expanded, almost as my productivity is nearly doubled. I can tackle other projects, collaborate with another AI agent, or even (and I’m completely serious) indulge in a bit of gaming.
  4. The Harvest: I gather the results, reaping the rewards of the AI’s efforts. Sometimes, it’s spot-on; other times, a refining prompt is needed.

In my opinion, this is a deep redefinition of “teamwork.” It’s no longer just about human collaboration; it’s about orchestrating a symphony of human and artificial intelligence. This is the definition of “Ubiquity.” I work anytime and truly anywhere – during my commute, in a waiting room, even while strolling through the park (thanks to the marvel of voice-to-text). It’s a constant state of potential productivity, a blurring of the lines between work and, well… everything else.

The next stage begins with gaining awareness and using AI. From there, it progresses to actively building your AI teams. The goals of a collaboration with agents, for instance, for a product designer or a system architect, would be to actually create an AI team so that a human product designer becomes, in fact, the team leader of their AI agents. Let’s see how and why.

The rise of AI teammates promises greater productivity and a fundamental shift in the way we approach problem-solving and work.

ai uni team

7. Evolving as a Knowledge Worker in the Age of AI

How can a worker adapt to the capabilities expansion of printing productivity?

It starts by understanding the current capabilities of AI; exploring them through training or by testing systems, and see how they can be applied in your own workflow. Specifically, with your existing tasks, determine which ones can be delegated to AI. Even though, at the moment, it’s more about a human prompting and the AI executing a small, specific task. Progressively, these tasks will be chained, increasing the complexity to the level of higher hierarchical tasks. This integration of increasingly complex tasks constitutes the purpose of agents – to have a certain set of skills handled by AI.

For functional roles such as product designers or business analysts, there is an opportunity to transition toward a product focus, to understanding customer journeys, psychology, behaviors, needs, and emotions. This can result in an experience (UX) driven approach, where the satisfaction of fulfilling needs and solving problems is paramount while leveraging data insights to enhance the customer’s experience.

Indeed, this technology is already showcasing its potential to bring us together. Within my organization, my colleagues are openly sharing a feeling of relief at how Generative AI empowers them by significantly reducing the workload of some specifically tedious tasks from days to mere minutes. But it is not all about saving time: the key progress that made me really moved, is that now, freed from a few tedious operations, they have now the capacity to explore their current struggles, identify past pain points, and articulate new business requirements. Additionally, it is the “thank you”, that directly acknowledges my teams’ efforts in bringing this new means to reclaim precious time and comfort. What is even far more compelling, and very inspirational from my point of view, is this ability to formulate and then resolve this new set of challenges using the capabilities of this recent AI ecosystem. Witnessing this emerging transformation gives me tangible joy and concrete hope for our collaborative future.

So, the key observation is that it’s up to early adopters and leaders to drive this change. They need to build a culture where people aren’t afraid to reimagine their jobs around AI, to learn how to use these tools effectively, and to keep learning as the technology evolves. The time to strategize how AI reshapes internal processes to master inevitable industry restructuration has arrived while simultaneously positioning your organization as a demonstrative leader for others to follow. To build that next-generation workforce, you need tools and specific actionables strategies; what are the core components to your next plan?

Ultimately, this era of augmentation is a strategic opportunity – one that requires everyone involved, including users and top executives, to actively foster continuous understanding, ongoing discovery, and strategic adaptation, thus contributing at multiple levels to building high-performing teams.

Disrupt Yourself, Now.

8. The Digital Worker Factory: A Practical Example in Banking

Let’s consider a practical example. Imagine delivering a project to create an innovative online platform that sells a new class of dynamic loans—loans with rates that vary based on market conditions and the borrower’s repayment capacity. This platform would be fully online, SaaS-based, and built as a marketplace where individuals can lend and borrow, with a bank acting as a guarantor. That is the start of our story. Now it’s about delivering this product.

What if you only needed a Loan Product Manager, a System Architect, and a team of agents to bring this digital platform to life?

Here’s how the workflow looks.

  1. As the product manager, you specify the feature set and map the customer journey from the borrower’s perspective. You define the various personas – a lender, a borrower, a bank, and even a regulator.
  2. The system architect then set up the technical specifications for the IT applications and LLMs, covering deployment to the cloud, integrations such as APIs, data streams, and more.
  3. You initiate the iterative loop by defining a feature. The AI Agent then plans and generates the code, after which you test the feature. Based on your feedback, the Agent troubleshoots and corrects the program accordingly. This loop continues iteratively until the platform fully takes shape. In this workflow, the product isn’t merely coded—it’s molded. The prompt itself becomes the new code.
agentic team

With a clear vision and the right framework, the path to production is not as complicated as it once was.

The Loan Expert Augmented: AI in Action

Consider the Loan Product Manager. They use AI to simulate loan profitability, examining various customer types and market variations. But, as importantly, they use AI to refine pitches, sales materials, and regulatory documentation. This results in streamlining compliance and ensures alignment with the existing framework.

Generative AI is also used to revise internal and external processes. The templates for product sheets are optimized and iteratively improved. Marketing materials, such as a webpage explaining the product, equally take advantage of Artificial Intelligence to reach the best clarity and impact.

Finally, personalized communication with a customer per specific client also relies upon Generative AI automation and data contextualization. If a customer needs a loan for a car or other tangible asset, the communication is perfectly tailored to the specific context.

This is personal banking at scale.

The focus is on the active role workers play in orchestrating, managing, and continuously evolving the AI systems they rely on in their daily work.

Hence, we’re effectively printing productivity now – a rare paradigm shift. Every professional needs to be proactive to seize this opportunity, not just react to it. Start by exploring AI tools relevant to your field, experiment with their capabilities, and consider how they can be integrated into your daily workflows. Whether you are a software engineer, product designer, or loan expert, the time to adapt is now.

Bear with me: the way you’ve operated up to this point—with data entry in applications, scrupulously following procedures, and writing lengthy reports in document processing software— is now directly challenged by individuals adopting the “automatician” mindset, evolving their skills from basic Excel macros into sophisticated, full-fledged applications. But remember: The future is not something you passively face. This world is your design, using available new technologies along with your pragmatic actions

9. Integrating AI Engineering in your System of Delivery, The Two Paths Forward: New vs Existing Systems

Now that Generative AI has entered your work and that you are integrating all the different aspects of the digital workers – either through pilot projects or all other internal activities – this awareness converges towards one strategic decision.

This decisive decision leads to two clear paths: either build a completely new application and workflow designed from the ground up around AI-augmented technologies or modernize existing complex systems to align with current AI-powered delivery.

Your choice needs solid considerations, even though both outcomes must lean toward a similar goal: a fully modern and flexible digital workforce, printed from your Enterprise Agent Factory. Thus, ensure you keep a strategic direction of impact toward a significantly better system.

Path 1, Building from Scratch: The AI Native Approach

The first and straightforward path involves building completely new systems. Here, the software specifications are essentially the prompts within a prompt flow. Think of this prompt flow as the blueprint for the code, all directly created within an Agentic IT delivery stack. The advantage of this approach is that the entire system is designed from the ground up to work seamlessly with AI agents. It’s like building a house from scratch with an AAA energy pass and all the home automation technologies included.

The prerequisite of this stage is to constitute a core team of pioneers that went successfully into production with at least one product used by internal or external clients. In the process, they successfully earned their battle scars, gained experience, selected their foundation technologies, established architectural patterns, and built a list of dos and don’ts, which ultimately will turn into AI Engineering guidelines and best practices.

Next – and this decisive break from the old system is non-negotiable – this group must devise a brand new method of work: building its strategic and actionable steps in all operational components that allow AI to be present from day one and without the limitation of legacy infrastructure that no longer fits. Because all this experience now results in a unique point of reference, a key ”baseline” from that point you can start designing a process to enable the full transformation from the current to future operations.”

Path 2, Evolving From Within: Growing AI Integration in the Existing Enterprise Application Landscape

The second path involves evolving existing systems. This is a more intricate process as it requires navigating the complexities of the current infrastructure. Engineers accustomed to the predictability and consistency of traditional coding methods now need to adapt to the probabilistic nature of AI-driven processes. They must deal with the fact that AI outputs, while powerful, can not always be exactly the same.

Initially, this can be unsettling because it disrupts your established practices, but with tools such as Cursor or GitHub Copilot, you can quickly become accustomed to this new approach.

This shift requires that software engineers move from the specific syntax of languages like Python or TypeScript to communicate in everyday language with the AI, bringing skills that were previously specific to them into the reach of other knowledge workers. Furthermore, it is not easy to introduce powerful LLMs in a piece of software that has an established code structure, architecture, and history. It’s like renovating an old house – you are forced to work with existing structures while introducing AI elements. This requires a deep understanding of the current code and the implications of architectural choices, such as why you would use Event Streaming instead of Synchronous Communication or a Neo4J (graph database) instead of PostgreSQL (relational database) for a specific task.

Accessing and integrating with legacy systems adds another layer of friction because the code is outdated or uses a proprietary language. While AI facilitates code and data migration, the increased efficiency of AI-native platforms often makes rewriting applications from scratch the most optimal strategy.

In summary, creating AI-native applications from scratch is easier, with an incredible speed of development, but it implies a bold decision. Transitioning an existing application is more difficult, as it has inherent architectural, data, or technological constraints, but it is the most accessible path for many companies.

The increasing power of LLMs to handle ubiquitous tasks that were previously exclusively human tasks implies a compression of tasks and skills within an AI. This shift moves some work regarding coordination, data management, and explanatory work from humans to machines. For human professionals, this will result in the reduction of these types of tasks, freeing them to focus on higher-level tasks.

The duality of paths ahead is a call for a pragmatic approach to transition; it’s about moving forward without disrupting too much of the familiar workplace.

10. The Metamorphosis: From Data Factories to Digital Workforce Factories

[Picture: A symbolic image representing a butterfly emerging from a chrysalis]

Today, we are gradually fully exploiting the potential of Generative AI, with text being the medium to translate, think, plan, and create. These capabilities are expanding to media of all kinds – audio, music, 3D models, and video. Consider what Kling AI, RunwayML, Hailuo, and OpenAI Sora are capable of; it is just the beginning and building blocks of what is possible.

These capabilities, originally for individual tasks, are now transforming entire industries – architecture, finance, health care, construction, and even space exploration, to name just a few.

If you can automate aspects of your life, you can automate parts of your work. You can now dictate entire workflows, methods, and habits. You can delegate. What’s the next stage?

So far, we have created automatons, programs designed to execute predefined tasks to fulfill a part of a value chain. These are digital factories comparable to factories in the physical world that have built computers, cars, and robots. And now, if you combine factories and robotics with software AI, the result is the ultimate idea: the digital worker.

The key is that it is no longer just about using or building existing programs but more about building specific agents. These agents represent specialized versions of the human worker and include roles such as software engineers, content creators, industrial designers, customer service providers, and sales managers. Digital workers have no limits in scaling their actions to multiple clients and languages at the same time.

The new paradigm consists of creating a new workforce. We used to construct data factories with IT systems, and now, with Generative AI, we are building Digital Workforce Factories. A Foundation Model is the digital worker’s brain. Prompts are defining their job function within the enterprise. API and Streams are their nervous system and limbs to act upon the real world and use existing code from legacy systems.

The extensive time once required to cultivate skilled human expertise—spanning roughly eighteen years in formal education, followed by years of specialization, and reinforced through real, tangible work applications— is now radically compressed due to the capacity of LLM technology. By which, as I detailed previously in “Navigating the Future with Generative AI: Part 1, Digital Augmentation“, it all highlights our mastery in having compressed centuries of structured knowledge: from methodical research, systematic problem-solving frameworks, and many and countless cycles of innovation process from implementation best practices. Still, key expertise now resides in both the method and application. New competencies should prioritize AI foundational model mastery, the value of highly skilled fine-tuning methods for specific domain applications, and how to leverage creative prompts to build a tangible output from such systems even with unexpected new scenarios.

It is paramount to fully grasp that what we experience now with AI transformation is more than just a set of groundbreaking techniques. It reveals a new structure—for better and for worse—impacting both knowledge workers assisted by AI agents and manual workers augmented by robotics. But remember, ‘and’ is more powerful than ‘or’: it is precisely the combination and convergence of these roles — human and digital working together — that creates true scalability and transformative potential.

The real path forward isn’t merely augmentation—it’s about fostering a genuinely hybrid model, emerging naturally from a chrysalis stage into a mature form. This new human capability is seamlessly amplified by digital extensions and built upon robust foundations, meticulously refined over time. Moreover, humans are destined to master Contextual Computing, where intuitive interactions with a smart environment—through voice, gesture, and even beyond—become second nature. This isn’t about replacing humans; it’s about elevating them to orchestrate a richer, more integrated digital reality.

Perhaps artificial intelligence is the philosopher’s stone—the alchemist’s ultimate ambition—transmuting the lead of raw data into the gold of actionable intelligence, shaping our environment, one prompt at a time.

Dear fearless Doers, the future is yours.


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Artificial Intelligence Automation Information Technology Society Strategy Technology

President Donald Trump just announced”Project #Stargate, a $500 Billion AI Datacenter investment.

It aimed at establishing the world’s largest #AI data center infrastructure in the US.

Headed by the trio:
– Larry Ellison, CEO of Oracle

– Masayoshi Son, CEO of SoftBank

– Sam Altman, CEO of OpenAI

… and supported by tech giants like Nvidia and Microsoft.

Project Stargate demonstrates a new breed of industry collaboration, aiming at generating massive economic growth.

The initial phase, constructing data centers in Texas, with each building spanning half a million square feet, clearly indicates a huge undertaking and scale.

According to the new 47th US President, this strategic investment could generate over 100,000 American jobs while positioning the United States as a leading powerhouse in AI infrastructure.

This can unlock major innovative breakthroughs for any kind of industry using AI. Larry Ellison is leading Oracle to use AI for early disease diagnosis, such as for Cancer.

The ask: Why aren’t Mark Zuckerberg and Elon Musk in the “Monster Trio”? (If you get the reference, let me know ;))

It’s simple.

Both are already massively investing in their own AI data centers: xAI’s “Colossus” and Meta topping 10 Billion in Louisiana, and many more projects.

This is a clear sign of how key is the AI datacenter landscape. AI is not only a technology, it is the key to “Printing Productivity” -> this is the title of my upcoming article in the series “Navigating the Future with Generative AI”.

Thinking about the Europe landscape, in my humble opinion, Europe needs companies like SoftBank to make bold investments like 200$ billions for engineering prosperity.

It’s time to think big and move fast.

In your opinion:
A) what these large investment implied in the collaboration and competition between US, China and Europe?

B) And are these move compatible with ESG promises?

(By the way, Pdt Trump also announced cars running on fossil fuels are very back as a major contribution to US economy.
Both you and I know these datacenters won’t solely run on green energy.)

Wishing you a great day 🫡

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Agents Artificial Intelligence Business Business Strategy Copilot Information Technology Innovation Microsoft

⭐ Microsoft brings “Everyday AI” to over 320 millions MS 365 users!

Yesterday, I received a notification that my Microsoft 365 subscription now includes MS Copilot 365 AI credits.

It’s a smart move to integrate AI tools more broadly, especially when considering that Microsoft 365 has over 320 millions daily active users globally (as of 2024), and more than 2.3 millions companies using the “office” productivity suite.

According to the FAQ, the Personal and Family plans contains 60 AI credits.

What’s an AI credit? I quote: “A credit is counted each time you specifically request a Copilot or equivalent AI services action, such as generating text, a table, or an image.”

My experience with Copilot 365 so far has shown incredible productivity boosts in MS Teams and Excel. However, PowerPoint still feels like it needs refinement.

I’m very keen to explore specialized versions of Copilot like the Project Management Copilot to enhance team efficiency further.

Have you tried MS 365 Copilot?

What are your experiences with MS Copilot so far?

Let’s share our experiences.

Categories
Artificial Intelligence ChatGPT OpenAI Technology

ChatGPT Launches New Search Feature: OpenAI Challenges Perplexity and Google in AI Search

#ChatGPT just released the #search feature.

Nothing revolutionary here. OpenAI is catching up with Perplexity, the company that has pioneered #AI search.

There’s a new button (globe icon) on the left of the chat input. When you tap on it, it opens the “Browse mode,” similar to using a custom #GPT.

Search results clearly display the sources. You have the ability to go through them all.

It is clear, the new battle of the new search experience is openly starting. This new breed is ready to take on Google Search.

🫡

Categories
Artificial Intelligence Automation Autonomous Agents Business Deep Learning GPT4o Information Technology Reinforcement Learning Reinforcement Learning Strategy Technology Technology Strategy

Navigating the Future with Generative AI: Part 4, Unstoppable AGI and Superintelligence?

AGI and Superintelligence 1

1. Connecting the Dots Between Two Life-Changing Milestones for Humanity

In a Times Magazine interview, Yann Lecun remarked, “I don’t like to call [it] AGI because human intelligence is not general at all.” This viewpoint challenges our common understanding of Artificial General Intelligence (AGI) versus the supposed limitations of human intelligence. The term “artificial general intelligence” itself seems overused and often misunderstood. While it initially appears intuitive, upon closer examination, nearly everyone with an informed perspective offers a different definition of AGI.

The fog only thickens with Ilya Sutskever, Chief Scientist behind the wildly popular GPT generative AI model. In an MIT Technology Review interview, he states, “They’ll see things more deeply. They’ll see things we don’t see,” followed by, “We’ve seen an example of a very narrow superintelligence in AlphaGo. […] It figured out how to play Go in ways that are different from what humanity collectively had developed over thousands of years. […] It came up with new ideas.”

Before DeepMind’s AlphaGo versus Lee Sedol showdown in 2016, we had IBM’s Deep Blue chess victory against Garry Kasparov in 1997. The unique aspect of these AIs is their mastery within a single, specific domain. They aren’t general, but superintelligent—surpassing human capability—within their respective areas.

In this article within the “Navigating the Future with Generative AI” series, we’ll explore two inevitable stages in humanity’s future: AGI and Superintelligence.

2. Defining AGI: What Do We Really Mean?

Numerous definitions exist for what we call AGI and superintelligence. These terms often intertwine in contemporary discussions around artificial intelligence. However, these are two very distinct concepts.

Firstly, AGI stands for Artificial General Intelligence. This signifies a state of artificial intelligence built upon several building blocks: machine learning, deep learning, reinforcement learning, the latest advancements in Generative AI and Imitation Learning algorithms, and basic code. These all contribute to a level of versatility in task execution and reasoning. This developmental stage of synthetic intelligence mirrors what an average human can achieve autonomously in various areas, demonstrating a generalized capability to perform diverse tasks.

These tasks stem from a foundation of knowledge—akin to schooling—combined with basic learning for completing new, periodically defined objectives to achieve specific goals. These goals exist within a work setting: finalizing an audit ensuring corporate compliance with AI regulations, ultimately advising teams on mitigation strategies. Conversely, they exist in daily life: grocery shopping, meal preparation for the next day, or organizing upcoming tasks. This AGI, working on behalf of a real human, benefits from globally accessible expertise. These attributes enable assistance, augmentation, and ultimately, complementation of everyday actions and professional endeavors. In essence, it acts as a controllable assistant: available on demand and capable of executing both ad-hoc and everyday tasks. The operative word here is general, implying a certain universality in skillsets and the capacity to execute the spectrum of daily tasks.

I share Yann Lecun’s view: a key missing element in current AI models is an understanding of the physical world. Let’s be more precise:

  • An AI requires a representation of physics’ laws but also an operational model determining when these laws apply. A child, after initial stumbles, inherently understands future falls will occur similarly, even without knowledge of the gravitational force field. They can learn, sense, and anticipate the effects of Earth’s gravity. Similarly, our bodies grasp the concept of weight calculation without comprehending its mathematical expression before formal learning.
  • Beyond this world model, an AI needs to superimpose a system of constraints, continuously reaffirming the very notion of reality. For example, we understand that wearing shoes negates the feeling of the hard ground beneath. Our preferred sneakers, due to their soles, elevate us a couple of centimeters, offering a slight cushioning effect while running. We trust the shoes won’t detach, having secured the laces. We vividly recall fastening those blue shoes before beginning our run as usual. Most importantly, we possess the unshakeable belief we won’t sink into the asphalt, knowing it doesn’t share mud’s consistency. Thus, we can confidently traverse our favorite path, striving for personal satisfaction, aiming to break that regional record.
  • An AI needs not only the ability to plan but also the capacity to simulate, adapt, and optimize plans and their execution. Recall your last meticulously planned trip. Coordinates meticulously plotted on your GPS, you set off with time to spare. But alas, the urban data was outdated, missing the detour at the A13 freeway entrance. Then, misfortune struck: an accident reported on the south freeway, traffic condensing from three lanes into one. Stuck in a bottleneck, only two options remain—pushing forward in hope or finding an alternate route. Checking your watch: 23 minutes left to reach your destination. This is how dynamic and complex planning a task can be. And yet, humans are capable of handling this all the time.
  • An AI requires grounding in reliable and idempotent functionalities, echoing the foundation of classical computing: programming, logic, and arithmetic calculation. The ability to call upon an internal library, utilize external APIs, and perform computations is paramount. This forms the basis of real-world grounding, maintaining “truth” as the very infrastructure of AGI. It’s about providing an action space yielding predictable, stable results over time, much like the verified mathematical theorems and laws of physics backed by countless empirical papers. Take, for instance, the capacity to predict a forest drone fleet’s movements using telemetric data, factoring in wind speed and direction, geospatial positioning, the relative locations of each drone and its neighbors, interpreting visual fields, and detecting obstacles (trees, foliage, birds, and so on).
  • An AI have to capitalize on real-time sensory input to infer, deduce, and trigger a decision-action-observation-correction loop akin to humans. For instance, smelling smoke immediately raises an alarm, compelling us to locate the fire source and prevent potential danger. Smartphones, equipped with cameras and microphones, display similar capabilities. Taking this further, devices like Raspberry Pis, when combined with diverse electronic sensory components, can even surpass human sensory capacities. Consider a robot with ultraviolet, infrared, or ultrasonic sensors, allowing it to “sense” things beyond our perception. This lends literal meaning to Ilya Sutskever’s statement.

This implies that AGI won’t necessarily be beneficial or provide significant added value in highly specialized fields, especially in areas where humans have been traditionally adept. This applies to domains like fundamental research, inventiveness, and engineering design – areas I believe will remain constrained by the currently available knowledge pool on the internet. This limitation arises because AGI’s continued advancement is largely driven by companies tailoring it to their specific expertise, often regarded as intellectual property.

Thus, we progressively journey towards AEI: Artificial Expert Intelligence. This translates to a model or agent, a pinnacle expert in its field. Imagine an AEI on par with the top 5% of experts (> 2σ) on this planet, reaching Olympian levels, like AlphaGeometry and AlphaProof, who secured the Silver Medal at the International Mathematical Olympiad.

The architectures with the most potential rely on active collaboration between expert models (Mixture of Experts) and between agents (Mixture of Agents). Even when individual model performance within this collaborative framework isn’t the absolute best, the collaborative outcome exhibits a quality level on par with, if not exceeding, that of the best individual models like GPT4-o. It’s a striking testament that collaboration, be it human or artificial, remains the most effective avenue to reach any objective.

3. Humanity’s Inevitable Ascent Towards Superintelligence

Revisiting the human versus machine narrative, 2018 marked a pivotal encounter: AlphaStar versus TLO (Dario Wunsch), then MaNa (Grzegorz Komincz), two professional gamers from the renowned StarCraft Team Liquid. Created by Google DeepMind, AlphaStar is a digital prodigy trained on the collective experience of 600 agents, equivalent to 200 years of playing StarCraft.

Consider the inherent imbalance when directly contrasting human capabilities against those of AI:

  1. Replication Capacity: AIs can be copied indefinitely.
  2. Relentless Training: AIs train ceaselessly, needing no sleep, nourishment, or breaks.
  3. Absolute Focus: AIs exhibit unwavering concentration on their designated tasks.
  4. Self-improvement through concurrent learning: AIs hone their abilities by training against their evolving intelligence, devising novel strategies to secure victory.
  5. Linear scalability: the more computing and memory resources you add, the greater the performance

The outcome: an AI consistently outmaneuvering the crème de la crème of a strategic open-world video game’s premier league. And as if that weren’t enough, it maintains its position within the Grandmaster league.

Here lies the very essence of an intelligence surpassing human decision-making abilities within a similarly vast and dynamic environment: this is what we classify as Superintelligence, or ASI.

Superintelligence, from my perspective, transcends mere human intelligence and even surpasses collective human intelligence. It indicates that even a group of individuals, regardless of their combined expertise and knowledge, would be outpaced, left trailing by an artificial intelligence capable of going beyond their cumulative potential.

Imagine instead a new form of synergy: a “super” human system collaboratively engaged in highly cognitive functions with this Superintelligence. This involves humans directing or, perhaps more accurately, guiding this Superintelligence based on our needs. While this Superintelligence operates with its own raison d’être, it wouldn’t clash with the fundamental purpose of humanity. This Superintelligence possesses access to those superior functions—understanding the universal model within which humanity exists. It possesses the model of reality itself.

Moreover, it resides within a self-improvement and discovery paradigm, continuously unveiling novel operations, new paradigms, and potentially even new forms of energy. Think entirely new physics laws that govern our universe; laws that humans, as of yet, have not uncovered. This encompasses diverse domains: medicine, engineering, revolutionary material science, new composite development, and engineering breakthroughs for unprecedented construction methods. Envision a symbiotic relationship between humans and machines fulfilling humanity’s ambitions. The limitations posed by individual human existence or the current state of collective human intelligence dissolve; no longer a barrier, it morphs into an expansive vision of human evolution, a potential accelerator for progress.

It even prompts new questions: How far can humans evolve? Or more precisely, how quickly?

However, we shouldn’t discount the possibility that artificial Superintelligence won’t be seen—or won’t see itself—as a novel species.

Therefore, being as rational as possible, we cannot accurately predict if this species would afford humanity the same compassion and civil collaboration that we strive for with our fellow human beings. It’s even plausible that they won’t hold any particular regard, instead pursuing their objectives, much like we think little of stepping on ants while daydreaming in a beautiful landscape, lost in contemplation, our thoughts oscillating between everyday worries and future aspirations.

4. What Would Constitute Human Superintelligence?

Human superintelligence embodies the culmination of all accumulated knowledge, discoveries, experiences, and yes, even the mistakes made by our ancestors to this point. Ultimately, this human superintelligence represents the collective “us” of today. It’s what fuels our intricate logistics and supply chains, our relentless pursuit of natural resources. It underpins our scientific endeavors: from breakthroughs in biology, mathematics, and agriculture, to understanding our global economic system – allowing us to manage our resources effectively, allocate them efficiently, and strategize our reinvestments. Money, in this light, transforms into a socio-economic technology.

Essentially, when comparing human superintelligence—today’s collective human intellect—with artificial superintelligence, a stark contrast emerges in their evolutionary cycles. Artificial intelligence advances at a significantly faster pace, powered by recent breakthroughs in training using our data. This data, importantly, reflects our findings, the mirror to thousands of years of human advancement accessible through the internet. This hints that artificial superintelligence would evolve at a much faster rate than humanity itself.

This rapid advancement stokes anxieties about potential disruption within the job market. Tech titans like Sam Altman advocate for Universal Basic Income (UBI) as a safety net for those displaced by artificial intelligence or robotics, allowing individuals to meet their basic needs even after losing their jobs. At that juncture, work itself detaches from its traditional role: that direct link between labor, contribution to the value chain, recognized worth, and societal standing. Instead, we confront the image of an economic umbilical cord, individuals sustained by the state-funded by fellow citizens.

While I remain undecided on my stance regarding UBI’s necessity, it compels contemplation. When UBI becomes a reality for a significant portion of the population, what function does money truly serve within our society? How do we sustain work motivation beyond “earning a living” when basic needs are met without active contribution? What ripples will be felt throughout a sovereign currency? Will the collective of people continue to control the economy, or is the future in the hands of AI-driven megacorporations?

There are so many answers yet to be uncovered.

After all, maybe “computing” should be considered a universal right. Therefore, we would shift the focus from UBI to UBC, Universal Basic Computing.

5. AGI and Superintelligence: Steering Toward a Future of Abundance or Ruin?

The next cycle hinges on resource accessibility and access to “programming” the world. Initially, artificial intelligence, at the very least, will permeate our daily lives. We are transitioning to personalized AI assistants, specializing in our chosen pursuits, whether robotics for errands, learning assistance for mastering a new language, or perfecting one’s singing voice. Next to none, specialized AI coaches will emerge to achieve elite athletic status, along with AI tutors guiding our artistic development beyond the readily available generated art of today.

Simultaneously, this superintelligence would be managing our complex systems: national infrastructures, electricity grids, vast transportation and logistical networks. Thus, it can drive early warning systems for natural disasters or power next-generation weather prediction platforms that incorporate oceanic currents. It will even account for stellar events such as shifts in the sun’s activity, factoring in our solar system’s dynamic positioning.

In conclusion, these are just glimpses into the potential futures shaped by AGI and superintelligence. However, the core message remains: we stand at a critical juncture. Depending on our collective appetite for progress, we could be headed toward a future of abundance or stumble along the path toward our own undoing.

Science offers an incredible opportunity: the chance to break free from a civilization driven by profit-motivated conflicts and ideological clashes. Instead, it enables collaboration guided by a neutral, third-party entity—one that embodies the best of what we, as a species, have strived for, built, and imagined. This collaboration offers a path for our societal framework to truly evolve.

The future is bright if we make it right.

🫡

Categories
Artificial Intelligence Business Businesses ChatGPT Engineering EU AI Act GPT4 GPT4o Information Technology Innovation Llama Meta OpenAI Regulation Technology

✨ Llama 3.1, Meta and the EU AI Act – Where are the areas of synergy between innovation and regulation?

img 20240727 wa00033116017234937086313
Llama 3.1 AI model

Llama 3.1, a 405 Billion parameters model, has just been released by Meta.

It comes with increased performances. Some early tests make it comparable to “GPT4o“.

A few perks:

  • Still #opensource
  • 128K token context window
  • Improved Multilingual Support. Meta is a leader in multilanguage models.
  • Comes with a new security and safety tool for advanced moderation and control mechanisms to ensure safe interactions.
  • Improved capabilities for creating synthetic data.

I find the partner ecosystem, including NVIDIA, Google Cloud, Microsoft, Groq supporting Llama already quite impressive (see picture).

But also…

While the EU AI Act has been officially published on July 12, 2024, in the EU official journal, to come into force on August 2, 2024, Meta made worrisome news for the #artificialintelligence open source community.

In a nutshell, Meta will withhold the rollout of multimodal AI models in the EU region until the regulatory rules are clarified.

The EU AI Act contains explicit rules for foundation models, also known as “general-purpose AI models”, amongst the following:

  • Article 51: Classification of general-purpose AI models as general-purpose #AI models with systemic risk
  • Article 53: Obligations for providers of general-purpose AI models
  • Article 55: Obligations for providers of general-purpose AI models with systemic risk
  • Article 56: Codes of practice

Let’s hope we will find a way to balance #innovation and #regulation.

🫡

Categories
Artificial Intelligence Automation Business Business Strategy Engineering Innovation Robots Strategy Technology Technology Strategy

Update on Tesla’s Optimus #Robot – it is progressing fast

Tesla’s Optimus Robot learning from humans

The most impressive part is the technique employed by the Tesla team for accelerating the robot’s dexterity: the robot physically learns from human actions. 

Now, let’s step back and analyse Tesla’s master plan here:

(Putting on my business tech strategy goggles) 

1. Tesla builds electric cars augmented with software programmability.

2. Tesla provides an electric grid as a service.

3. Tesla builds gigafactories that maximize the automation of car manufacturing. Almost every single part of the pipeline is robotized and optimized for speed of production.

4. Tesla builds Powerwalls (by providing energy storage, it also creates a decentralized power station network).

5. Tesla brings autonomous driving (FSD) to Tesla cars. Essentially, cars are now transportation robots governed by the most advanced AI fleet management system.

6. Tesla builds its own chips (FSD Chip and Dojo Chip)

7. Tesla builds its own supercomputers.

8. Tesla launches Optimus, which aims to replace the human workforce in factories and warehouses.

9. X.ai, which has recently raised $6 billion, X’s supposedly “child” AI company, brings the Grok AI model trained on X/Twitter data. While you may say X data is not the best, X has a algorithm balanced with human judgment (community notes), AND the company regroups the largest set of news publishing companies. Basically, it automates curation and accuracy.

10. A version of the Grok AI model will likely power Optimus’s human-to-robot conversational interface.

11. Tesla cars will be turned into robotaxis, disrupting not only taxi companies but also Uber (the Uber/Tesla partnership may not be a coincidence), and eating into the shares of Lyft and BlaBlaCar.

12. Tesla will enter the general services business, and retail industries to offer multi-purpose usage robots – cleaning services for business offices, grocery stores, filling the workforce shortage in the catering (hotel-restaurant-bar…) industry, etc.

Tesla is not the only one moving in the “Robot Fleet Management” business. Chinese companies like BYD (EV) offer strong competition, and there are several robot startups (like Boston Dynamics and Agility Robotics) racing for the pole position.

#AI #artificialintelligence #Robotics #Optimus #EV #software #EnergyStorage #Automation #powerwall #AutonomousVehicles #FSD #chips #HighPerformanceComputing #Robots #GrokAI #NLP #robotaxis #innovation #WorkforceAutomation

Categories
web architecture Artificial Intelligence Automation Autonomous Agents Information Technology Services Technology User Experience UX

Navigating the Future with Generative AI: Part 3, Building the AInternet – AI, Web, and Customer Experience

A Revealing Experience

Allow me to share a personal experience that perfectly illustrates the challenges I will discuss. I was involved in a car accident where a vehicle coming from the opposite direction severely damaged the right side of my car. Following the procedure, I filed an accident report with the other party, although I found myself unable to provide my insurance number simply because I didn’t have it readily available at that moment.

In the meantime, I went to my regular dealership so that an appraisal could be carried out and the next steps for repair could be determined. I then contacted my leasing company, and one of their agents agreed with me and the dealer that I would drop off the vehicle within two weeks. A replacement vehicle would be provided, and the full repair would take one to two weeks.

However, due to my lack of foresight, I did not deem it necessary to contact them again initially. A few days later, I received a letter from them informing me of the accident – which was correct – but also stating that I had not submitted the accident report and that without it, their insurance reserved the right not to cover the damages. In fact, I had sent this document a week earlier, but to the wrong email address. Out of habit, I had used their general contact details, avoiding contacting the agent in charge of my leasing file – who had recently retired. As a precaution, I had even added the generic address, but clearly without success since the insurance department had not received it.

I then called them back urgently to obtain clarification. They confirmed that the accident report was missing, and the agent, with great understanding which I acknowledge, told me that I had to send it to another specific address because the insurance department had not been notified by their colleagues in charge of customer relations. Moreover, the latter was not authorized to provide me with a replacement vehicle until the repair shop had received their approval – even though it was the approved dealership where I had been carrying out all maintenance operations for years.

This kind employee then offered, as an exception, to handle my entire case without further difficulty since the drop-off of my vehicle was imminent, just a few days away. She knew also that my leasing contract was expiring and that I would have to return the vehicle in two weeks to obtain a new one.

While this situation caused me a little stress, it was only temporary. An hour later, the agent contacted me again to confirm that everything was settled: I could bring my vehicle the following Monday and a replacement vehicle would be provided for the duration of the repairs.

Lessons from This Experience

You may be wondering why I am sharing this story with you.

First of all, I was unaware of the procedures governing the reporting of an incident in the context of a leasing contract. Should I first contact my company, directly the historical leasing company, or the new one? When I called them, why didn’t I reach the dedicated claims and insurance department directly? Why didn’t I find any information about this on their website? Why, when everything seemed clear to me – that I would drop off my vehicle within two weeks, that a replacement vehicle would be waiting for me, and that the repairs would be handled smoothly – did things unfold differently due to a lack of following the proper procedure?

Beyond that, how can a single service company exhibit such a lack of communication between two complementary departments?

The Revolution of the “AInternet”


We are entering a new era where artificial intelligence will be at the heart of exchanges between human beings. Where everyone previously had to search for information themselves on the Internet, navigating from site to site and compiling data to find a company’s contact details, the instructions for a recipe, the contacts of a repairman, or browse the Yellow Pages, the new paradigm will rely on exchanges between humans, intermediated or not by an artificial intelligence capable of performing synchronous or asynchronous tasks, i.e. in the background, to provide immediate knowledge to the user rather than forcing them to seek it out.

And to return to my use case, the AInternet brings a revolutionized customer experience that unfolds as follows:

When I am involved in an incident, I ask my personal AI assistant to help me fill out the accident report digitally. I do not have to provide all the information since my assistant has a global context encompassing data related to my vehicle, its insurance, my contract, my identity card, my passport, my postal and telephone contact details, my insurer, the maintenance status of my car, its technical inspection certification, etc. All this information allows for automatic and complete filling of this type of interaction.

Next, I only need to ask my assistant to contact the assistants of my leasing company and my insurance company, to ensure that the report I have validated and electronically signed is transmitted and processed by these two parties.

The assistant of the leasing company then informs the agent that a replacement vehicle is required and that an approved garage must be contacted to book an appointment for the repairs. It also determines whether my car should be taken directly to the dealership in charge of its regular maintenance. The relevant agent then handles my vehicle accordingly.

The agent only has to ask their assistant for the contact details of my garage to reach out directly.

From there, a genuinely empathic human relationship is established as we build a frictionless mutual understanding of the situation. Following the garage’s preliminary appraisal report, the leasing agent and the garage are prepared to agree on an appointment date, which is then recorded in the various systems.

The garage proceeds in an automated manner with the reservations and orders for the spare parts necessary for the repairs.

Simultaneously, the leasing company manages with the insurance company all the steps required to allow for the vehicle reparation and the provision of a replacement vehicle during the downtime.

Finally, the agent contacts me personally, by phone or message on a platform such as WhatsApp, to confirm everything is in order:

The incident has been properly recorded and the insurance company will cover all costs. An appointment has been set with my garage. A replacement vehicle will be provided during this period. An estimated date for returning the repaired vehicle has been communicated. They wish me an excellent day with a smile, since their assistant and mine have handled the entire procedure seamlessly. This augmented interaction allows us to reach new heights of fluidity and ubiquity in exchanges.

I am optimistic, indeed. Why wouldn’t I be? The transformation is already in motion.

The Internet will no longer be confined to a vast catalog of information to consult, such as books, encyclopedias, or applications, where interactions must be initiated and orchestrated by us, humans. But the orchestration between an individual and an organization, between two individuals, or between an organization and a computer system, will be performed like a symphony by intelligent agents, artificial intelligences.

This demonstrates an evolution of the World Wide Web architecture, which will constitute a veritable system of systems composed of human beings, applications, automata, and artificial agents.

The challenge from now on to enable this progression towards the era of digital augmentation will be to build artificial intelligence at the heart of human interactions. It is a matter of UX innovation.

It will no longer be a question of programming these interactions in advance by limiting the possibilities, but rather of training these artificial intelligences to handle a wide range of possible scenarios while framing and securing the use cases that could result from malicious computer hacking.

Ensuring a secure web environment requires a multi-layered approach that goes beyond safeguarding the AI models themselves. Equal vigilance must be applied at the integration points, where we erect robust firewalls and implement stringent access controls. These protective measures aim to prevent artificial intelligence from inadvertently or maliciously gaining entry to sensitive resources or confidential information that could compromise the safety and well-being of individuals, imperil organizations, or even threaten the integrity of the entire system.

Thus, emerging risks, such as jailbreaking, aimed at deceiving an artificial intelligence devoid of physical senses such as sight, hearing, and spatial awareness, allowing the authentication of a person, a company, or a system, will have to be compensated by other supervision and protection mechanisms.

It is on this note that this article concludes. We are living in an era of transition rich in exciting developments, and it will be up to you to build the Internet of tomorrow: the Augmented Internet.

🖖

Categories
Artificial Intelligence ChatGPT GPT3 GPT4 IT Architecture IT Engineering

API Hero 🤖” – The #GPT That Codes the API for You 🙌

APIs are key to scaling your #business within the global ecosystem. Moreover, your API is a fundamental building block for augmenting universally accessible #AI services, like ChatGPT.

Building an #API, however, can be daunting for non-IT individuals and junior engineers, as it involves complex concepts like API schema, selecting libraries, defining endpoints, and implementing authentication, among others.
On the other hand, for an expert backend #engineer, constructing your fiftieth API may feel repetitive.

That’s where “API Hero” comes in, specifically designed to address these challenges.

Consider an API for managing an “#Agile Planning Poker”. Given a list of functions in plain English, such as “Create Planning Poker”, “Add Participants”, “Estimate User Story”, etc., (including AI-suggested ones), the GPT will generate:

  1. The public interface of the API (for engineers, this corresponds to the OpenAPI/Swagger spec).
  2. #Code in the chosen #programming language, with a focus on modularity and GIT-friendly project structure.
  3. Features like API security, configuration management, and log management.
  4. An option to download the complete code package (no more copy-pasting needed 💪).

And there’s more!

Search for “API Hero 🤖| AMASE.io” on #ChatGPT’s GPT store. Give it a try and send your feedback for further improvement.

By the way:

  1. Currently, GPTs are accessible only to ChatGPT Plus users.
  2. If you want to know more about the decisive nature of API for your business, check my article/podcast “Why API are Fundamental to your Business”.

Link to the GPT: https://chat.openai.com/g/g-a5yLRJA1J-api-hero-amase-io

🫡

Categories
In 2060 AR/VR Artificial Intelligence Autonomous Agents Bioengineering Drone Fleet Management Hologram Holographic Display Information Technology Storytelling Technology Transportation Drone Writing

Bioengineering in 2060

“Nasir, check this out. Didn’t I tell you PSG would win against New Manchester? Two bitcoins, baby. Who is the soccer king?”

“Stop bragging, man. Gee, I don’t know how you do it. Hey, Betmania, tell me how this regular human with his XXL ears looking like satellite dishes can beat your prediction. Thank God, you were a freebie AI.”

The bet bot replied, “I am not qualified to review Dr Anoli’s performances. Yet, his 97.26% accuracy is…”

Nasir interrupted the hologram: “Ahh, shut it! It was a rhetorical question. My man! You are good, you gooood.”

He lifted his hand, nodding repetitively to perform his most vigorous handshake.

The upper deck lit up like a lighthouse, and then a deafening sound followed the illumination.
Beeeeeepp. “Emergency. Purple alert. All medical engineers on deck. I repeat, Purple Alert. All medical engineers on deck. This is not a drill.”

My heart was pounding. The message was still resonating in my head.

The sudden drop in temperature, produced by the arch-reactor of the medical drone transporter, announced the arrival of an unusual patient. This is the first time I have seen a flying one. Usually, these vehicles were stored in the Corps of Peacekeepers‘ R&D facilities.

The temperature drop caused a fog to rise like a curtain. I saw a blurry figure approaching slowly.

My colleague interrupted my stupor: “I haven’t seen a purple alert for 7 years now, it’s serious. Purple means death.”

The flying ambulance emerged at the heliport located at the center of the critical emergency service. Work is pretty easy on platform seventeen, actually. Nowadays, bioengineering is solving almost all critical injuries as they occur. An accident at a construction site? Any multifunction robot worker medic cauterizes your wound with accelerated healing enzymes, bypassing block surgery. As long as you’ve subscribed to the right medical service, the AI health model can be downloaded, and the tier two healing kit purchased for printing.

“What on earth could have happened?”, exclaimed Nasir.

The hologram of the AI diagnostician, Dr. Ernesto, popped up from the ambient hospital Phygital network while the paramedics were transporting the patient out of the medical drone transporter. Then, she said:

“This is unprecedented, we are losing it. We’ve tried traditional medicine and printed the generic assistance bacteria. Nothing works. The patient’s vitals fluctuate over time at an unconventional rate. Scanning the available knowledge from the current corpus does not provide any satisfying answers. The highest probable disease is the Genova IV virus with a probability of 31%”.

I replied “Too much uncertainty… What do you suggest, Nasir?”

“It looks like the emerging meta-virus. The… guy on B-Hacker YouTube Entertainment System… Sorry, I can’t remember the name of that journalist… said it was an open-source public CASPRed experiment.”

I could not believe it at first. But when I saw his wound mutating in front of me, there was no doubt that the potential disease was human-made.

“Arrrghhhhh. It burns… Please get it off me. I am begging you. Just cut it!” shouted the patient.

I was stunned. I cannot remember the last time I saw a real person suffering that much. Dr. Krovariv told me about it. This was surreal. So much pain.

“Hey! Gather yourself, buddy. There’s no time to waste. We might be facing a level 5 threat here,” said Nasir.

He was such a great guy. He was the embodiment of coolness. Not only was he a great bioengineer, but he always kept his cool on the field. I was more of a lab genius. I wish I could be more like him.

“Huh, yeah. Sorry, I froze,” I said with a coating of stupor, disappointment, and anger. I continued without hesitation, “What’s the status?”

While he was unplugging the portable bio-scanner from the trembling body, Nasir replied: “Body temperature 39.9. Heartbeats – 137. Adrenaline level 50.5. Oxygen level 92.12%. Stress level is increasing by 2.4% per minute. If we don’t act quickly, he is going to have a seizure. The stem cell differentiation rate… 272%! This is too high. It looks like he is growing a supernumerary limb, but I don’t know what kind.”

“Pick up the nano-extractors and verify traces of xeno-mRNA.”
It was a Sanofi Nano-extractors SNX43, a state-of-the-art fleet of nano-medical machines with a portable management system. Current extractors were all managed centrally by the hospital drone intervention unit.

I quickly put my face next to the patient’s ear and asked, “Sir, I am going to need your authorization to perform an intracellular analysis. We don’t know what you have, this might be your last chance…”

He grabbed my head and screamed, “What the hell are you asking for? Do it! Arghhhh!”

I felt foolish. Yet, there was no way I could bypass the authorization because the device had a legal lock encoded in it.

“That will do,” I whispered. The nano-extractors became operational, switching from blue ‘stand by’ to green ‘proceed.’ The extractors looked like a stick, no longer than a large pen. I placed it next to the wound, and it opened, deploying four branches on each side, and anchoring onto his skin. I heard a sequence of small noises, like pistons. The extractor fleet deployed into his body.

Then a ballet of lights came up. The SNX43 beamed a 3D representation of the arm’s inner part. The system rendered the location of the fleet in real-time.

They moved surprisingly slowly. I was disappointed. I was used to contemplating much livelier robots with the previous version.

Nasir said deductively: “There’s something wrong. They appear to be sucked in or blocked. Zoom in, Nicolas”.

I pinched out the holo-display to enter the zoom command and tapped on the max button. The illusion of slowness came from the fact that the machines were moving at such a speed that it was hard to follow with the naked eye. They looked like bees on steroids, fighting a wall of flesh. But the alien cells rebuilt the wall as soon as it was destroyed.

“I get it; the cellular growth must be faster than the robots can clear the path. It is not good. Not good at all.”

“Have you ever seen something like this before?”

“No…” said Nasir. It was the first time I saw fear in his eyes.

Then he jumped. “It could be contagious. I want all containment units on the platform now. I request a total lockdown of platform seventeen. Now!”

Nasir stopped, then looked at me insistently. I immediately thought something was happening to me.

Then, Nasir froze and fell to the ground.

I jumped immediately to grab him, but something inside me pulled back. My bioengineer spinal safety device prevented me from approaching the new pathogens. Whenever it triggered, it reminded me of the auto-braking mechanisms in my grandparents’ cars. I do not like the idea of having a machine tweaking my neural system. Yet, once more, it saved my life.

“This is not the time,” I whispered. I looked at the sky and said, “Dr. Ernesto. Launch orbital containment in ten. Authorization Kappa-Sigma-Omega-1337.”

Dr. Ernesto answered, “Authorization confirmed. Launching orbital containment in 10, 9, 8…”

“Meanwhile, call the World Health Emergency.”

“Opening channel.”

The dial ended almost instantly.

“Dr. Anoli, this is Dr. Krovariv – replica number 3. State the nature of your emergency.”

“I need to speak to the real Dr. Krovariv. This is a purple priority request.”

“Accepted. Please stand by…”

Time stood still while the platform was flying to space. The quietness of linear magnetic propulsion was staggering. My god. Is Nasir going to make it?

“The patient! I am a monst…”

“Dr. Anoli. What is it about?” Her question interrupted my thoughts.

“Dr. Krovariv. I am sorry, but Nasir has been contaminated by an unknown biological agent. I am heading toward space containment. Furthermore, the patient at the source of the contamination is dying. This agent is too dangerous. Please advise on the protocol,” I finished with a frail voice.

“In this situation, the protocols are clear. Once in space containment, you may use the ‘yellow horizon’ protocol. But you know what will happen if it fails.”

“Yes… I know.”

“I am sorry, son. Since CASPR got open-sourced, we are overwhelmed by these idiots… I am waiting for your decision.”

I had a deep look at Nasir. I knew it would probably be the last time I would see him. It was probably the last time I would see anything else.
I exhaled loudly and said, “Permission to print a new species as a countermeasure.”

“Permission granted. Good luck.”

Asking was the easy part. I was her protégé.

As I was climbing space, the platform changed its path toward a new direction. The Athena Life Engineering Lab, or ALEL, was lighting up like a giant light bulb. It was my dream to one day be worthy of visiting the lab. It was proclaimed as a symbol of hope. But not like this. Not on the verge of my very own death.

ALEL was the only accredited location where humans were allowed to engineer and print new life forms. No one was allowed to penetrate the labs.
Akin to the Athena Orbital Data Centre, it was solely operated by robots and machines following a strict chain of command.

Soon after, the platform slowed down, then stopped near the lab, and the docking system engaged to secure the system. A muffled noise gave me the chills.

“Welcome, Dr. Anoli. My name is Marie Curie. You have been granted the usage of the Athena Life Engineering System by Archdoctor Krovariv of the World Health Organization on the date of 30th December 2060. Any inquiry going against the principles of life and Human Civil Rights will result in your immediate termination. Do you want to hear these principles?”

“No, thank you. I already vowed to fight for life when I became a biomedical engineer.”

“Understood. Please state your prompt, please.”

I thought to myself, “What? Just like that?”

“Marie Curie, I need you to engineer a biological agent to counter Dr. Nasir’s condition. Focus uniquely on alien gene alteration and accelerated growth features. Do not augment the agent with inorganic material. Do not add metamorphic adaptation capabilities. Encode the maximum life duration to be 72 hours. Add automatic degeneration from 70 hours as a failsafe mechanism. No reproduction. No replication. I need you to confirm the supernumerary growth first before proceeding.”

“Supernumerary growth confirmed. Your prompt is acceptable. Proceeding to the registration of the new specimen identified by Anoli-alpha-31122060-001. Beginning prototyping phase.”

As I reached the point of no return, I could not help but wonder if our life would end here. ALEL was a universal robotic womb. What would happen if I asked the Life Factory to… recreate Nasir… Is rebirth possible?


In 2060