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:
The public interface of the API (for engineers, this corresponds to the OpenAPI/Swagger spec).
#Code in the chosen #programming language, with a focus on modularity and GIT-friendly project structure.
Features like API security, configuration management, and log management.
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:
Currently, GPTs are accessible only to ChatGPT Plus users.
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
In this installment of the Generative AI series, we delve into the concept of “Prompt as new Source Code”. The ongoing revolution of generative AI allows one to amplify one’s task productivity by up to 30 times, depending on the nature of the tasks at hand. This transformation allows me to turn my design into code, eliminating almost the need for manual coding. The time spent typing, correcting typos, optimizing algorithms, and searching Stack Overflow to decipher perplexing errors, structuring the code hierarchy, and bypassing class deprecation among other tasks, are now compressed into one. This minimization of effort provides me with recurrent morale boosts, as I achieve significantly more in less time and more frequently; these instances are micro-productivity periods. To put it in perspective, I can simply think about it during the day, and have a series of conversations with my assistant while I commute. My assistant is always available. In addition, I gain focus time.
I don’t need to wait for a team to prove my concept. Furthermore, in my founder role, I have fewer occasions to write extensive requirement documents than I would when outsourcing developments during periods of parallelization. I just need to specify the guidelines once, and the AI works out the rest for me. Leveraging the AMASE methodology to fine-tune my AI assistant epitomizes the return on investment of my expertise. Similarly, your expertise, paired with AI, becomes a powerful asset, exponentially amplifying the return on your efforts.
Today, information technology engineering is going through a quantum leap. We will explore how structured coding is being replaced by natural language. We refer to this as prompting, which essentially denotes “well-architected and elaborated thoughts”. Prompting, so to speak, is the crystallization of something that aims to minimize the loss of information and cast-out interpretation. In this vein, “What You Read is What You Thought” becomes a tangible reality.
The Unconventional Coding Experience with AI
Although the development cycle typically commences with the design phase, this aspect will not be discussed in this article. Our focus will be directed towards the coding phase instead.
The development cycle with AI is slightly different; it resembles pair programming. Programming typically involves cycles of coding and reviews, where the code is gradually improved with each iteration. An artificial intelligence model becomes your coding partner, able to code 95% of your ideas.
In essence, AI acts as a coach and a typewriter, an expert programmer with production-level knowledge of engineering. The question may arise: “Could the AI replace me completely? What is my added value as a human?”
Forming NanoTeams: Your AI Squad Awaits
My experience leads me to conclude that working with AI is akin to integrating a new teammate. This teammate will follow your instructions exactly, so clarity is essential. If you want feedback or improvements in areas like internal security or design patterns, you must communicate these desires and potentially teach the AI how to execute them.
You will need to learn to command your digital teammate.
Each AI model operates in a distinct yet somewhat similar fashion when it comes to command execution. For instance, leveraging ChatGPT to its fullest potential can be achieved through impersonations, custom instructions, and plugins. On the other hand, Midjourney excels when engaged with a moderate level of descriptiveness and a good understanding of parameter tweaking.
A New Abstraction Layer Above Coding
What exactly is coding? In essence, coding is the act of instructing a machine to perform tasks exactly as directed. The way we’ve built programming languages is to ensure they are idempotent, repeatable, reliable, and predictable. Ultimately, coding is translated into machine language, creating a version that closely resembles human language. This is evident in modern languages like TypeScript, C#, Python, and Kotlin, where instructions or controlling statements are written in plain English, such as “for each”, “while”, “switch”, etc.
With the advent of AI, we can now streamline the stage of translating our requirements into an algorithm, and then into programming code, including structuring what will ultimately be compiled to run the program. Traditionally, we organize files to ensure the code is maintainable by a human. But what if humans no longer needed to interact with the code? What if, with each iteration, AI is the one updating the code? Do we still need to organize the code in an opinionated manner, akin to a book’s table of contents, for maintainability? Or do we merely need the code to be correctly documented for human understanding, enabling engineers to update it without causing any disruptions? Indeed, AI can also fortify the code and certify it using test cases automatically, ensuring the code does not contain regressions and complies with the requirements and expected outcomes.
To expand on this, AI can generate tests, whether they be unit tests, functional tests, or performance tests. It can also create documentation, system design assets, and infrastructure design. Given that it’s all driven by a large language model, we can code the infrastructure and generate code for “Infrastructure as Code“, extending to automated deployment in CI/CD pipelines.
To conclude this paragraph, referring to my first article in the “Generative AI series”, it is apparent that Natural Language Processing is now the new programming language expressed as prompts. The Large Language Model-based generative AI model is the essential piece of software for elaborating, structuring, and completing the input text into code that can be understood both by human engineers and digital engineers.
The New Coding Paradigm
This fresh paradigm shift heralds the advent of a new form of coding—augmented coding. Augmented coding diminishes the necessity of writing code using third and fourth-generation languages, effectively condensing two activities into one.
In this scenario, the engineer seldom intervenes in the code. There may be instances where the AI generates obsolete or buggy code, but these can often be rectified promptly in the subsequent iteration.
We currently operate in an explicit coding environment, where the input code yields the visible result on the output—this is known as Input/Output coding.
The profound shift in mindset now is that the output defines the input code. To elucidate, we first articulate how the system should behave, its structure, and the rules it must adhere to. Essentially, AI has catapulted engineers across an innovation chasm, ushering in the era of Output/Input coding.
Embracing Augmented Coding: A Shift in Engineering Dynamics
The advent of augmented coding ushers in a new workflow, enhancing the synergy between engineers and AI. Below are the core aspects of this transformation:
Idea Expression: The augmented engineer is impelled to express ideas and goals to achieve.
Requirement Listing: The engineer lists the requirements.
Requirement Clarification: Clarify the requirements with AI.
Architecture Decisions: Express the architecture decisions (including technology to use, security compliance, information risk compliance, regulatory technical standards compliance, etc.) independently, and utilize AI to select new ones.
Coding Guidelines: Declare the coding guidelines independently and sometimes consult the AI.
Business Logic: Define the business logic in the form of algorithms to code.
Code Validation: Run the code to validate it works as intended. This becomes the first order of acceptance tests.
Code Review: assess the code to ensure it complies with the engineering guidelines adopted by the company.
Synthetic Data Generation: Use AI to generate data sets that are functionally relevant for a given scenario and a persona.
Mockup-API Generation: Employ AI to generate API stubs that are nearly functionally complete before their full implementation.
Test Scenario Listing: Design the different test scenarios, then consult stakeholders to gather feedback and review their completeness.
Test Case Generation: Make AI to generate the code of test cases. The same technique applies to security tests and performance tests.
AI can even operate in an autonomous mode to perform a part of the acceptance tests, but human intervention is mandatory at certain junctures. It’s crucial to bridge results with expectations.
Hence, when uncertainties arise, increasing the level of testing is prudent, akin to taking accountability upon acceptance tests to ensure the delivered work aligns with the expected level of compliance regarding the requirements.
Non-Negotiable Expectations
In the realm of critical business rules and non-functional requirements such as security, availability, accessibility, and compliance by design, these aspects are often considered second-class citizen features. Now that AI in coding facilitates the choice, these features can simply be activated by including them in your prompts to free you up more time to rigorously test their efficiency.
Certain requirements are tethered to industry rules and standards, indispensable for ensuring individual or collective safety in sectors like healthcare, aviation, automotive, or banking. The aim is not merely to test but to substantiate consistent performance. This underscores the need for a new breed of capabilities: Explainable AI and Verifiable AI. Reproducibility and consistency are imperative. However, in a system that evolves, attaining these might be challenging. Hence, in both traditional coding and a-coding, establishing a compliance control framework is essential to validate the system’s functionality against expected benchmarks.
To ease the process for you and your teams, consider breaking down the work into smaller, manageable chunks to expedite delivery—a practice akin to slicing a cake into easily consumable pieces to avoid indigestion. Herein, the role of an Architect remains crucial.
Yet, I ponder how long it will be before AI starts shouldering a significant portion of the tasks typically handled by an Architect.
Ultimately, the onus is on you to ensure everything is in order. At the end of the day, AI serves as a collaborative teammate, not a replacement.
Is AI Coding the Future of Coding?
The maxim “And is greater than or” resonates well when reflecting on the exponential growth of generative AI models, the burgeoning number of published research papers, and the observed productivity advantages over traditional coding. I discern that augmented coding is destined to be a predominant facet in the future landscape of information technology engineering.
Large Language Models, also known as LLMs, are already heralding a modern rendition of coding. The integration of AI in platforms like Android Studio or GitHub Copilot exemplifies this shift. Coding is now turbocharged, akin to transitioning from a conventional bicycle to an electric-powered one.
However, the realm of generative AI exhibits a limitation when it comes to pure invention. The term ‘invention’ here excludes ideas birthed from novel combinations of existing concepts. I am alluding to the genesis of truly nonexistent notions. It’s in this space that engineers are anticipated to contribute new code, for instance, in crafting new drivers for emerging hardware or devising new programming languages (likely domain-specific languages).
Furthermore, the quality of the generated code is often tethered to the richness of the training dataset. For instance, SwiftUI or Rust coding may encounter challenges owing to the scarcity of material on StackOverflow and the nascent stage of these languages. LLMs could be stymied by the evolution of code, like the introduction of new keywords in a programming language.
Nonetheless, if it can be written, it can be taught, and hence, it can be generated. A remedy to this quandary is to upload the latest changes in a prompt or a file, as exemplified by platforms like claude.ai and GPT Code Interpreter. Voilà, you’ve just upgraded your AI code assistant.
Lastly, the joy of coding—its essence as a form of creative expression—is something that resonates with many. The allure of competitive coding also hints at an exciting facet of the future.
Short-Term Transition: Embracing the Balance of Hybrid A-Coding
The initial step involves exploring and then embracing Generative AI embedded within your Integrated Development Environment (IDE). These tools serve as immediate and obvious accelerators, surpassing the capabilities of features like Intellisense. However, adapting to the proactive code generation while you type, whether it’s function implementation, loops, or SQL code, can hasten both typing and logic formulation.
Before the advent of ChatGPT or GPT-4, I used Tabnine, whose free version was astonishingly effective, adding value to daily coding routines. Now, we have options like GitHub Copilot or StableCode. Google took a clever step by directly embedding the AI model into the Android Studio Editor for Android app development. I invite you to delve into Studio Bot for more details on this integration.
Beware of Caveats During Your Short-Term Transition to Generative AI
Token Limits
Presently, coding with AI comes with limitations due to the number of input/output token generation. A token is essentially a chunk of text—either a whole word or a fragment—that the AI model can understand and analyze. This process, known as tokenization, varies between different AI models.
I view this limitation as temporary. Papers are emerging that push the token count to 1M tokens (see Scaling Transformer to 1M tokens and beyond with RMT). For instance, Claude.ai, by Anthropic, can handle 100k tokens. Fancy generating a full application documentation in one go?
Model Obsolescence
Another concern is the inherent obsolescence of the older data on which these models are trained. For example, OpenAI’s models use data up to 2022, rendering any development post that date unknown to the AI. You can mitigate this limitation by providing recent context or extending the AI model through fine-tuning.
Source Code Structure
Furthermore, Generative AI models do not directly consider folder structures, which are foundational to any coding project.
Imagine, as an engineer, interacting with a chatbot crafted for coding, where natural language could reference any file in your project. You code from a high-level perspective, while the AI handles your GIT commands, manages your gitignore file, and more.
Aider exemplifies this type of Gen AI application, serving as an ergonomic overlay in your development environment. Instead of coding in JavaScript, HTML, and CSS with React components served by a Python API using WebSocket, you simply instruct Aider to create or edit the source code with functional instructions in natural language. It takes care of the rest, considering the multiple structures and the GIT environment. This developer experience is profoundly familiar to engineers. The leverage of a Command Line Interface – or CLI, amplifies your capabilities tenfold.
Intellectual Property Concerns
Lastly, the risk of intellectual property loss and code leakage looms, especially when your code is shared with an “AI Model as a Service”, particularly if the system employs Reinforcement Learning with Human Feedback (RLHF). Companies like OpenAI are transparent about usage and how it serves in enhancing models or crafting custom models (e.g. InstructGPT). Therefore, AI Coding Models should also undergo risk assessments.
The Next Frontier: Codeless AI and the Emergence of Autonomous Agents
These agents require only a minimal set of requirements and autonomously devise a plan along with a coding strategy to achieve your goal. They emulate human intelligence, either possessing the know-how or seeking necessary information online from official data sources, libraries to import, methods, and so on.
However, unless the task is relatively simple, these agents often falter on complex projects. Despite this, they already show significant promise.
They paint a picture of a future where, for a large part of our existing activities, coding may no longer be a necessity.
Hence, the prompt is the new code
If the code can be generated based on highly specific and clear specifications, then the next logical step is to consider your prompt as your new source code.
It means you can start storing your specifications instructions, expressed as prompt, then store the prompt in GIT.
Continuous Development/Continuous Certification (CD/CC) with Adversarial AI Agent
Suddenly, Continuous Integration/Continuous Delivery (CI/CD) becomes Continuous Development/Continuous Certification (CD/CC), where the prompt enables the development of working pieces of software, which will be continuously certified by a testing agent working in adversarial mode: you continuously prove that it works as intended.
The good thing is that benefits stack up: the human specify, the AI code/deploy and the AI certify, to finish with the human using the results of the materialization of its thoughts. Finally, the AI learns along with human usage. We close the loop.
Integrating New Technology into Traditional Operating Models
AI introduces a seamless augmentation, employing the most natural form of communication—natural language, encompassing the most popular languages on Earth. It stands as the first-of-its-kind metamorphic software building block.
However, the operating model with AI isn’t novel. A generative AI model acts as an assistant, akin to a new hire, fitting seamlessly into an existing team. The workflow initiates with a stakeholder providing business requirements, while you, the lead engineer, guide the assistant engineer (i.e. your AI model) to execute the development at a rapid pace.
Alternatively, a suite of AI interactions, with the AI assuming various roles, like dev engineer, ops engineer, functional analyst, etc. can form your team. This interaction model entails externalizing the development service from the IT organization. Here, stakeholders still liaise through you, as lead engineer or architect, but you refine the specifications to the level of a fixed-price project. Once finalized, the development is entirely handed over to an autonomous agent. This scenario aligns with insourcing when the AI model is in-house, or outsourcing if the AI model is sourced as a Service, with the GPT-4 API evolving into a development service from a Third-Party Provider like OpenAI.
AI infuses innovation into a traditional model, offering stellar cost efficiency. Currently, OpenAI’s pricing for GPT-4 stands at $0.06 per 1000 input tokens and $0.12 per 1000 output tokens. Just considering code generation (excluding shifting deadlines, staffing activities, team communication, writing tasks, etc.), for 100,000 lines of code with an average of 100 tokens per line (which is extensive for standard leet code), the cost calculation is straightforward:
100,000 × 100 = 10,000,000 tokens; (10,000,000 tokens × $0.12) ÷ 1000 = $1,200. This cost equates to a mere two days of development at standard rates.
For perspective, Minecraft comprises approximately 600,000 lines of Java code. Theoretically, you could generate a Minecraft-like project for less than $10,000, including the costs of input tokens.
However, this logic is simplistic. In reality, autonomous agents undergo several iterations and corrections before devising a plan and rectifying numerous errors. The quality of your requirements directly impacts the accuracy of the generated code. Hence, mastering the art of precise and unambiguous descriptive writing becomes an indispensable skill in this new realm.
Wrap up
Now, you stand on the precipice of a new coding paradigm where design, algorithms, and prompting become your tools of creation, shaping a future yet to be fully understood…
This transformation sparks profound questions: How will generative AI and autonomous agents reshape the job market? Will educational institutions adapt to this augmented coding era? Is there a risk of losing the depth of engineering expertise we once relied upon?
And as we move forward, we can only wonder when quantum computing will introduce an era of instantaneous production, where words will have the power to change the world in real time.
Looking through the lens of Generative AI, jobs are evolving rapidly in this age of Digital Augmentation. In the midst of all the artificial intelligence effervescence, I wonder what kind of new jobs will emerge soon.
One of them is the Prompt Engineer.
In this article, I imagined the job description of your business’ first Prompt Engineer.
The world is shifting rapidly. As a pioneer in generative AI and an advocate of productivity augmentation, we are excited to open the position of Prompt Engineer.
SuperSleek Jeans is a company providing tailored jeans to women and men. Our purpose is to make jeans like a second skin! Our values are sensorial audacity and durability leadership. We proudly employ 2700 talented souls dedicated to meeting people’s needs in a smart and compassionate manner. Technology plays a significant role in our way of working and exploring uncharted territories for the benefit of our employees and customers is part of our DNA.
We foster a dynamic and inclusive company culture that encourages growth, collaboration, and innovation. We offer competitive compensation packages, comprehensive benefits, and numerous opportunities for professional development.
Your Mission
Your mission is to establish and grow the practice of Prompt Engineering at SuperSleek Jeans.
Responsibilities
Learn and teach how to build products faster by analyzing and modifying the chain of analysis-to-design, design-to-build, and build-to-supervise for augmentation in each domain.
Lead the development of an Enterprise AI Spirit, a chat-based agent, sourcing its knowledge base from existing systems such as Wiki, Document Store, Databases, and Unstructured documents. Manage an up-to-date training data set.
Build a corporate prompt catalog for workers to provide reusable productivity recipes.
Determine which parts of business processes can be entirely automated.
Establish KPIs, a Steering Dashboard, and periodic reporting to measure the benefits of AI-augmented engineering and operations compared to current systems of work.
Introduce and evangelize the concept of Generative AI and Large Language Models (also known as LLM).
Build a legal and ethical framework to ensure risks pertaining to AI augmentation are addressed accordingly. Monitor the progress of domestic and international AI regulations.
Your Skills
Hands-on experience with Generative AI models and tools leveraging prompt engineering, such as ChatGPT, Midjourney, ElevenLabs, etc.
Proven algorithmic skills and mastery of engineering practices.
The ability to code in one of the most popular languages such as Python, JavaScript, Java, or C#. A basic understanding of SQL is a must.
Data management proficiency.
Excellent communication and ability to design stunning presentations with compelling storytelling.
Critical thinking and root cause analysis capabilities.
Conversational UX proficiency.
Soft Skills
Autonomous leadership with the ability to identify and propose the next best actions for yourself and your colleagues.
Effective change management and resistance handling.
Leading by example and providing assistance to colleagues when needed.
You walk the talk by advocating continuous augmentation and demonstrating how your productivity and quality increase with AI augmentation.
Benefits and Perks
An 85k€ to 105k€ compensation package based on your experience in engineering and AI knowledge.
Total health, dental, and vision insurance for all family members.
Retirement savings plan according to the national compensation scheme.
30 holidays with a generous paid time off policy.
Employee assistance program and wellness initiatives.
Craft your own professional growth and development along with your manager
Collaborative and inclusive company culture.
Free cinema tickets for your team once per quarter.
Living Your First Days in our Company
You start your onboarding as a treasure hunt which consists in visiting key people, visiting unusual places, and learning our way of working. Each step unlocks a new quest until the completion of your journey. Your manager, the employee experience manager officer, and teammates assist along your adventure.
Receive training so that you can rapidly feel comfortable with internal tools.
Enjoy a tour of the premises and surrounding environment, such as restaurants, shops, parks, etc.
As you familiarize yourself with the work environment, your first responsibility will be establishing a plan for transitioning our organization from Digital Transformation to Digital Augmentation.
Join and become part of a team that shapes the future of SuperSleek Jeans. Apply now and embark on an exciting and fulfilling career journey with us.
Feel free to unapologetically copy and remix this potential job offer in your business transition to Digital Augmentation.
In this series of articles, I explore the fascinating realm of Generative AI, as models of concentrated intelligence, and their profound impact on our society.
By tapping into the vast collective mind, digitization has enabled us to access the accumulated knowledge of humanity since the invention of writing.
Join me as we explore this intriguing topic in greater detail and uncover the exciting possibilities it presents.
A Glimpse of the Future
In 2060, David dreams of becoming the best defense attorney in the country. After losing his best friend under heart-breaking circumstances, he vowed to prevent any woman from enduring domestic violence under his watch. He is a fourth-year student, and today, he is taking his most important exam of the year.
There is only one supervisor in a room of 52 students. The senior shepherd devours her blue book, while the school’s AI monitor scrutinizes candidates.
David looks very confident. He is good at case-solving patterns. Since he has an excellent visual memory, he also has a good toolbox for cases and amendments. However, deep inside, he is stressed by his average analytical skills in evidence analysis and forensic correlation abilities. To pass the exam, he has permission to use the Internet, the LegalGPT AI model, and the online state court database.
David articulates his dossier like a virtuoso. His first composition is made of brief sentences. Subsequently, he links these pieces of evidence to references and precedents from previous cases and legal decisions. Shortly after, the legal argument is a dense one-pager. Next to none, using LegalGPT, he generates his entire lawsuit, a symphony of 27 pages written in perfect legal language. Finally, he makes a few adjustments, then generates a new batch of updates.
And voila.
Satisfaction and relief radiate from his face while he submits his copy. He stands up, packs his stuff, then stops briefly as the supervisor interrupts his focus. The latter looks at him and says:
“40 years ago, I had to write those 27 pages. Obviously, it is the end of an era”.
Dorine UWATIMINA, law professor (retired), grand supervisor.
Beginning the Era of Augmentation
The launch of GPT3 API in 2021 marked the beginning of a new era: the age of individual augmentation as a service. We are now living in an era of thought materialization, in which one can manifest their desires simply by articulating them. Ideas are designed, illustrated, musically composed, rendered in 3D, explained, or revealed by the AI.
Companies like Google (BERT), OpenAI (GPT-4), and Meta (LLaMA) are revolutionizing the domain of deep learning. They mark a significant advancement in natural language processing: Large Language Models (LLM) are picking up the spotlights on the world stage.
This means we are experiencing the transition from “programming” to “narrating”.
It is a paradigm shift in which artificial intelligence overwhelmingly simplifies and amplifies 3/4 of the corporate work relying upon Information Technology such as development, user interface design, illustration, workflow, or reporting.
Generative AI is the digitized embodiment of our collective knowledge and expertise.
As a consequence, we are beginning the mass update of the cognitive-based work that is convertible into algorithms and crystalized by pure logic. It leverages the most popular high-level programming languages: human languages.
From now on, spoken languages directly translate to machine language as if you could translate them using Google Translate, except you use ChatGPT.
As programming gets one step easier, your engineering thinking system matters more than your coding skills.
The burning question
I hear your question: Am I going to lose my job?
The answer will come further down this series of articles. Long story short: it depends on your ability to adapt by learning a practice that is new for everyone.
Unlike any other disruptive technology, it has changed the rule of the game forever: people using AI are going to replace you.
And who are these people using and building AI? The adventurous, the curious, the experimenters, the techies, the entrepreneurs, the hustlers, the bad guys, and the future AI natives, our kids.
Science is offering you a choice. For your own benefit, I am asking you to take the leap to understand what it is like to work with a digitized copilot and forge your thought opinion.
Should you take the red pill of adaptation, I recommend the following:
Start by trying at least once ChatGPT, or Bing Conversation. The latter includes the GPT model and renews the search experience. It heightens the googling experience to a whole new level.
Get acquainted with a Generative AI that is useful in your industry. For example Midjourney for generating images for email marketing.
Discover how you can be productive with this technology. It is not a silver bullet, but you can instantly acquire an arsenal of skills.
Build new habits so that you start feeling accustomed, connect the dots, and begin to improve your work until over-productivity.
Think about how someone else using some AIs can replace you, then be that person: replace yourself with the new you, your augmented version.
Or simply ignore all of it, swallow the blue pill of comfort, and undergo the first “Great Upgrade”.
Eat your own dog food
I have been experimenting with OpenAI technologies since 2020 and used Google Dialogflow since 2018. I released my first chatbot, which answered regulatory questions about GPDR and PSD2. Developing with Natural Language Processing (NLP) was an eye-opener. I concluded chat provides the ultimate user experience for interacting with machines. It all sounds so obvious now, yet it was not back then despite all the buzz around Siri, Google, and Alexa.
I did the exercise of working within AI augmentation on my experiments since GPT-3 came out. Considering the hard skills, the conclusion is daunting: Generative AI can perform most of what I know and what I am mentally capable of. I can safely state I am outperformed in some areas.
In addition, AI is simply miles away in terms of depth of knowledge. Furthermore, it possesses infinitely better linguistic skills than mines when it comes to articulating ideas in languages other than French and English.
Yet the surprise comes from its ability to develop a simple idea and make it grow by putting words in concert. AI feels like the genius child of Humanity.
Generative AI comes with a new discipline: Prompt engineering. It consists in finding the right text, and the rights qualifiers that will narrate the desired output as close as you have imagined it.
Ultimately, prompt engineering uses natural language as a modeling interface to command the “commendable world”. The more there are smart systems and devices, the more words animate the world!
The widespread innovative applications based upon Generative AI marks the end of the road for this generation and the beginning of a new breed of workers and creators.
Yet, another finding is that we still need a “general assembly semantic”. It would choreograph a fuzzy set of ideas that will accurately animate the world based upon a well-written thought.
The assembly process, which can be summarized into the loop “decomposition-planning-action-correction”, will likely open the door to Artificial General Intelligence (AGI). Coupled with the widespread natural language programming interfaces (NPI), this is the real end game. In that matter, we are already observing some interesting experiments like AutoGPT as sparks of AGI.
Transitioning from the Digital Transformation to Digital Augmentation
Picture this familiar situation.
Your maturity in terms of digital adoption is high. You are developing a culture of digital awareness, offering mobile-first customer interaction, and your brand is fighting for its visibility on social media. You have the feeling of doing great.
Congratulations.
Yet, the market atmosphere is heavy. You feel the pressure every week goes by. The competition is fierce, you are still looking for an army of IT engineers and data analysts for the last six months. Furthermore, customers get pickier because the offering is abundant. Your analytics tell you a client can switch in the blink of an eye if your experience does not meet his rising standards. Then, just when you thought you nailed it with your latest Instagram reels, it receives negative feedback. Even worst, there is a relentless wave of new product offerings mimicking yours. These startups and VCs are constantly trying to uncover the mythical unicorn while pushing your visibility back to Google’s page 2. And you feel this moment when your industry will be shackled, disrupted, or crippled may happen at any moment.
Who would have thought even Google’s dominance would be threatened?
Fortunately, there is a nascent vision. Transformation is not enough anymore. If you cannot obtain more skilled people now, why not acquire more skills for your people now?
AI is the key to unleashing your talents.
And, slowly, Augmented Work is the evolution of work, as we know it, characterized by these two elements:
A human is the sole team leader of his digital workers: he has the Applications, Automatas, and specialized A.I. models for numerous parts of your job, such as programming, translation, video editing, illustration, design, and planning.
Teams, as we know, will still exist, obviously, but augmented by AI also at the team level. The team has the opportunity to exist as an independent entity either in the company AI or as a single team companion if you need explicit segregation of duty. The “team spirit” has a whole new meaning with AI.
The flow of work evolves toward:
A. Human generates instructions using prompt engineering as explicit command requirements. The prompt is actually the evolution of the Command Line Interface (CLI), for a much greater general purpose.
B. AI generates a first draft
C. Human amend the sketch with input and then detail with new commands
D. Once the AI-driven engineering cycles are good enough for release change into the real world, you ship it for user acceptance or production if the risk is low.
The interaction with the AI becomes talkative. Either by chat or voice. AI is your new colleague.
AI starts having digital bodies, existing in a form of familiar avatars, and will be in multiple places: in your phones, your mixed reality glasses, in your Metaverse. Avatars could be Non-Player Characters (NPC), digitized versions of yourself, or even the retired expert that used to be your mentor.
So, am I going to be replaced by Artificial Intelligence?
You vs AI: you (still) have the upper hand
Here is a bet: 80% of white collars will keep their job. 20% of us will either refuse to learn these new tools to evolve either because of our fear of overwhelming technological advancement, or of conviction. Eventually, this minority will rush toward retirement and use these AI-powered services anyway to buy recommended stuff on Amazon after having been oriented by Google Bard from Google Search.
Why do I think that way? Because if we can produce much more with the same number of people, why would we deliver the same amount of products with fewer people?
Let’s take the example of Apple. The company entered the AI game in 2017 by introducing Core ML, an on-device AI framework embedded in iOS. The same year, it released the first generation of Apple Neural Engine (ANE) under the iPhone X with the A11 CPU.
Apple’s immeasurable impact comes from its ability to create and materialize an idea that is at the intersection of beauty, function, storytelling, and branding. Do you think Apple will push its culture of product excellence with the same amount of people amplified by a myriad of AI models, or will the company prefer reducing its workforce by leveraging more AI?
Pause for a second and think about it.
The other side of the coin
Taking the employer perspective in the era of AI Augmentation: what constitutes the difference between you and another candidate?
Any individual having a team of AI has the upper hand as he or she will be digitally augmented with skills and experience that usually takes years to acquire. What remains to develop are the skills to get used to these new abilities and use them at their best like an orchestra’s conductor.
You become the manager of AI teammates.
Hence, from the employer’s perspective, it results in hiring a virtual team vs an individual.
It raises the responsibility of Managers and the Human Resources department in the whole equation. Colleagues require to be upskilled to stay ahead, not only for the sake of the company but also to help them to keep building their personal value with respect to the market. Thus, leaders and HR have to set things in motion by organizing the next steps, while their own jobs are being reshaped and augmented…
Unlock the Future of Office Jobs Now
First, let’s admit once and for all you cannot win a 1 on 1 battle against AI, as much as you cannot win a nailing contest against a hammer.
The battle is long lost.
The battle doesn’t even make sense.
Because AI is the cumulative result of all humans’ knowledge, born from successful and failed experiments. To put it another way, as a sole individual, you cannot win against all of us and our ancestors combined!
And this is the incorrect mindset.
Hence, you will want to construct the future, your future, with all of us and our ancestors combined! You only need to be aware the future will be vastly different, and you should be part of the solution rather than engineering your problems.
AI is here to stay.
The questions to ask from now are:
Are we all going to benefit from it?
What portion of handcrafting do we want to keep?
How much evil is going to benefit from it?
How long until we get robots as widespread as vacuum cleaners?
When are we going to find truly sustainable and clean energy? (no, batteries are not sustainable)
The key is here and now: you need to invest in algorithmic and analytical skills to translate activities to algorithms in order to be augmentable.
Next, the winning companies and communities will be the ones tapping into their people’s intelligence combined with creativity augmented by AI, the physical resources to change the world, and their abilities to satisfy needs within an enjoyable experience while maintaining a transparent and engaging conversation.
The gap between “good” and “best” will be even smaller between businesses, but the proposed experience and the branding will have a tremendous impact. Then, consistency and coherence in how you serve the customer and engage with your fans will act as compound interests. This is how you win the perpetual game.
The term community inherits a new meaning given the free aspect of AI. You are not even needing to build companies to achieve your goals: you only need an organization that plans and organizes the agreed work, like in Open Source Communities and Decentralized Autonomous Organizations (DAO).
Hence, I encourage you to build an A.I. readiness.
How to be A.I. ready?
Here are my recommendations to get started as an individual, especially if you are a leader in a company:
“Socialize” with Generative AI applications useful to your job.
Know your data and data systems to identify candidates for augmentation.
Have “good” data. Good = true + meaningful + contextualized + accessible. As such, information must be stored in a secured and accessible location. Fortunately, Large Language Models are unstructured data friendly.
Have technologists that can pioneer lateral ideas. I recommend hands-on architects.
Assess and promote simple ideas on a regular basis, and establish an AI-dedicated project portfolio pipeline.
Select and run a set of competent AI in a fully autonomous fashion
Less is more until you reach the “optimal zone”, an inflection point that represents the optimal balance between effort, cost, and result. Exponentiality occurs when for minimal effort and expenses, you achieve unprecedented results.
The critical factor is this natural law: everything is born from need, will be driven by purpose, feeds on energy, is protected by self-preservation, and evolves to maturity.
Thus, until AI is not given the aforementioned five elements at the same time, then, its digital self-preservation is never programmed to be mutually exclusive with the preservation of living beings, and finally, AI self-evolution stays within boundaries, then AI growth will not be at the expense of humanity. Under these circumstances, humans can remain the dominating species.
As a consequence, one must consider what gives birth to a “trigger”: this initial impulsion taking the form of an idea that results in action delivered by willpower from the mind’s womb. Until then, an AI will not willingly use another AI, automaton, or application because it needs to, but because it has been commanded or programmed by us.
Until then, we are safe.
We are… Fine… Aren’t we?
This is not the right question
The right question is what is going to change for me?
Earlier I said, “It depends on your ability to adapt by learning a practice that is new for everyone”.
The long answer starts with a twist: the groups of humans producing AI and the others using AI as elements of augmentation and amplification of their skills will have an exponential upper hand because they can fulfill needs faster, optimally, and accurately at the cost of… just… time.
For example, building the next Instagram will depend on someone having:
The willpower
A distinguishingly desirable idea
A series of creative ideas
The skills
The drive to sell, communicate and promote their ideas to clients.
The resilience to continue developing the ideas
We can conclude that what consistently makes the difference are: the idea, the drive, the skills, the way user experience answers the client’s needs, and the resources you can obtain to make things happen.
But if ideas are cheap and abundant, and should cognitive skills can be acquired using virtually free AI Augmentation, then the remaining differentiators are the drive, the user experience, and the resources.
Thus, the Intellectual Property of a company becomes its Cognitive Know-how. Suddenly, high-value assets are the doers displaying high and consistent motivation, leaders that not only keep the Pole Star lighten but are able to keep their teamates inspired: the creative people, and the group of people having the capacity to invest and evolve in the same direction around the same flag: their brand, which I consider to be the result of maintaining a homogeneous identity of the combined people and products.
Graal or Pandora?
This new technology raises thousands of questions.
The development of Generative AI technology has opened up a vast array of possibilities, but it has also raised thousands of questions that need to be addressed.
For instance, one major question is how Generative AI will change our day-to-day interactions.
Furthermore, there is concern about whether this technology could lead to mass unemployment and economic inequality.
Another potential consequence is that it might devalue human creativity and originality.
Additionally, it is important to explore how Generative AI might impact human cognition and decision-making.
In terms of IT Engineering and Architecture, what is the impact of AI on these fields, and how will they adapt to this new technology?
Education is another area that could be significantly impacted, and it is worth considering how Generative AI might affect traditional learning methods.
Moreover, there is a concern that Generative AI could create a world in which we cannot distinguish between what is real and what is artificial. If this were to happen, what are the ethical implications?
Finally, the implications of Generative AI for democracy and governance are also important to consider, particularly with regard to its development and regulation.
Overall, the development of Generative AI technology raises many questions needing collaborative wisdom in order to fully prepare for its impacts on society.
I will attempt to answer these questions in upcoming articles of the “Navigating the Future with Generative AI” series.
Until then, if you are looking for the one thing to remember about this article: play with Generatice AI until it replaces just one activity of your daily routine, then boast your prompt engineering skills by spreading the word and educating your relatives.
