AI is the new Language

The technology of the Greek Phonetic Alphabet changed human creativity. Now, speaking the language of software and humans, AI will transform society and the software industry. In doing, AI will become our new language.

Language is essential to human culture. The fundamental difference between humans and animals is our ability to capture, communicate, and create using commonly understood language. Human evolution accelerated once humans could understand the sounds we make and capture those sounds in writing. Our ability to speak new ideas, write common laws, or read inspirational prose are the critical foundations of our modern global civilisation. 

AI is about to rock our language foundation. 

A comparison with the emergence of the Greek Phonetic Alphabet around 850 BCE is insightful to understanding how AI will now become our new language.

Around 850 BCE, the Phoenicians dominated naval Mediterranean trade, and their central location between Mesopotamia, Egypt and emerging Greek states enhanced their culture and commerce, with their influence extending from Afghanistan to Spain.

At this time, single rulers controlled all trade, contracts, laws, and decisions from their respective courts. Scribes formally recorded all deals, taxes, and judgements from the leader's court. Few people could read and write, and rulers seldom learnt this skill. To argue with a ruler, foolish in itself, was made more complex when few could understand the knowledge that established their rule. Nothing of worth occurred beyond the court walls as the court created all records. Therefore, widespread illiteracy led to the centralised power of the ruler, and scribes empowered this control.

A significant incentive existed to limit literacy. A scribe was well-paid, trusted and respected. Increasing literacy would diminish the worth of scribes' hard-earned literacy skills. In turn, rulers did not need their subjects to read their proclamations, only to obey them as subjects may disagree if they read the details.

Alphabetic complexity also made learning to read and write highly challenging. The Phoenician alphabet was consonantal, similar to all other alphabets at the time. To read, someone had to understand the discussion topic and remember many highly complex consonant clusters. While the consonantal alphabet was a significant leap forward from hieroglyphs and cuneiform, which used hundreds of symbols instead, it still took years of learning even to be basically competent at reading consonant clusters. 

Scribes needed to learn a word's written form and then comprehend the subject matter to translate that written form into actual meaning. The result was years of dedicated learning and apprenticeship before a scribe could capture in writing a trade negotiation, a new law, or an announcement from the ruler.

Between 850 and 750 BCE, the Greeks adopted the Phoenician alphabet and realised that Greek had fewer consonants than Phoenician, as today English has fewer consonants than Arabic. Reducing a complex process, someone then took the leftover letters to indicate vowels.

In Anaximander, Carlo Rovelli describes this moment,

"The many vocalic inflections of the same consonant - ba, be, bi, bo, and so forth - all rendered in Phoenician with the single letter B, could be distinguished as Ba, Be, Bi, Bo, etc.

It may seem a small idea, but it was a global revolution."

Indeed it was.

Greeks created the first phonetic alphabet, making reading and writing child's play. Learning the alphabet enabled someone to write the sounds they made in a way others could comprehend. They could deconstruct the sounds of others by understanding the same letters and joining them together. A sentence like, "A bird flies through the air" could be understood even if they had never seen the word "bird" before simply by saying each phonetic part. Once the reader said it, they would know that B.IR.D meant the word bird. 

The Greek phonetic alphabet was the first technology to enable almost anyone to record, share, edit, and understand the human voice.

The impact of this technology was immense. Anyone could hear the words of their rulers and, in response, share their ideas. Traders no longer needed a scribe to capture their negotiations and could escape the central court. Opinions could be shared, understood, and improved. Secrets could be passed outside the control of the ruler's court. Love letters could be shared. Propaganda could be published. This revolutionary technology empowered democracy, commerce, civilisation, medicine, literature, science, and, in turn, our modern information technologies.

The most significant change was that power was no longer in the hands of the few and could now spread through the reading and writings of the many.

How does this primary history lesson impact AI?

Since 850 BCE, the technology of the alphabet has underpinned nearly every significant revolution. Whilst other languages and alphabets existed, the need for mass literacy, or attempts to limit literacy, has empowered scientific discoveries, political revolution, or religious zealots. Even the language of mathematics, the other core language of modern society, prospered because the concept and ideas could be captured and recorded. We have democratised and made public our knowledge, our education systems have enfranchised everyone to understand our wisdom, and our civilisations have thrived.

The Age of Software Languages

Mass literacy empowered the masses and gave voice to their ideas until the 1970s, when a new language began to appear and evolve, the language of software. This new language ignored phonetics and, again, like cuneiform or hieroglyphic scribes, required specialist training and knowledge to comprehend. Someone without that knowledge could not deconstruct meaning using a handful of symbols; even if they could, it would often be language specific, reducing the detail one speaker could gain from another. 

The language of software rapidly evolved and changed, constantly changing, merging the languages that preceded it and parenting new languages. National critical infrastructure teams have gone through the ordeal of identifying old languages and the people who understood them to address security risks. A coder from 1979 may understand elements of some of today's software languages with their expressions and statements. Yet, even a simple quantum language like Q#, which includes quantum states and operations, would take much work to comprehend. It is, simply, another language.

The world has undoubtedly changed through the language of software. Its influence is felt direct through our interactions with digital devices, one step removed through managing the core services and utilities that power our cities and culture or indirectly through shaping today's societies. It is hard to dispute that we are in an age of software.

The language of software has also created new rulers and empires. MS-DOS founded Microsoft with an operating system that enabled PCs to work in a standard manner translating functions into system activities. Apple created a translator between human interactions and their computers using hardware mice and later touch screens. Google began with software to understand the knowledge of the internet and make it accessible.

The language of software is behind all of these empires, their scribes are well-paid and respected, and their language is not accessible to the masses, even if those masses have access to these empires through controlled portals. Guarding critical source code for crucial programs and, in turn, the valuable IP from that code is one of the highest priorities for any software developer seeking to monetise their code. The protection is not just to prevent security risks but also to guard the very source of their business.

Consider how Twitter/X recently reacted when Meta launched its social media posting solution. Its first response was to claim that Meta poached developers (scribes) and used the code (language) that made Twitter successful. Where that challenge will end or its legitimacy may be debatable, but software companies protect their scribes and languages just like ancient rulers.

AI threatens these technology empires (unless those empires control that AI).

AI is starting to break down these barriers of understanding and competition at an accelerating pace as we are on the verge of a new democratised alphabet.

Nearly everyone has become excited by Generative AI (GenAI), the suite of AI tools that generate and create artistic products like art, words, or music. Most readers of this piece will have experimented with prompting AI to create an image, asking an AI tool to write a summary of a complex document, or even generating a poem about a particular subject.

It is child's play to use these GenAI tools.

As well as capturing our imaginations by producing new content, recent GenAI tools have also broken down another critical barrier around ease of use and access. The interfaces that we use to engage GenAI has democratised access to AI. There is no need to understand data, storage, coding, models, or languages to access AI tools that generate immediate and tangible results. Their prompt interfaces and widespread distribution across devices, platforms and software have placed AI in the hands of the generalist rather than the specialist.

Creating and editing complex illustrations required access to someone with artistic talent and the ability to express the requirement. Now, "/Imagine Greek Philosopher holding a laptop inspiring a crowd" generates the image at the top of this post.

Previously, ease of access has been a significant barrier to adopting any new technology. The phonetic alphabet addressed this by enabling anyone with a canvas and a stylus to write. The results have been written on walls, hides, papers, rocks, metals, wood and screens ever since, but it still took centuries to revolutionise society truly. In 1820 only 12% of the world's population were literate. By 1960 it was 86%. Think of the possibilities if literacy at that level was achieved centuries earlier.

GenAI contains AI as both translator and creator to provide similar ease of access to complex tools and has equal potential to transform our technology usage at a pace measured in months rather than millennia.

An area of particular interest is AI-generated code. Here, GenAI writes code based on other code elements to create a software product. Creating this type of GenAI requires analysing large, existing volumes of code stored in repositories to learn, replicate and mimic software functions and services.

Right now, the outputs are relatively limited. It can generate code to extract data from a website, analyse the content, publish the results, or create simple webpages for human input to generate another related output. 

These programs are currently limited by the length of output generated by the GenAI product, by the number of code snippets drawn upon to create the code, and by the owners of the GenAI service to ensure that expensive compute resources are not consumed building complex programs.

Today's GenAI services still cost significantly in terms of time and cloud resources to deliver large outputs, yet that cost is rapidly falling. As a result, new opportunities will rapidly emerge as AI speaks software.

AI becomes the new language because it can speak the language of software and the language of humans through intuitive interfaces.

This blending of language already translates from idea to output, adds insight to our thoughts, and generates new solutions for our problems. AI can mutually translate native and software languages, improving both. Again, like the Greek phonetic alphabet, AI empowers people to understand and build more. 

It is removing the barriers that protect today's software scribes and empires. 

Rather than AI generating a document, spreadsheet, or presentation, we need to consider what happens when AI can develop the tools that make documents, spreadsheets, or presentations.

Imagine prompting GenAI "to build a word processor" or "create a program that lets me edit financial spreadsheets".

These are different from the questions currently answered by GenAI solutions due to the capacity reasons explained, but they are questions we can pose very soon.

Pace of Change - who shot JFK?

In 2017, I was privileged to witness a Microsoft team develop an AI solution that comprehended and analysed over 36,000 documents relating to the assassination of President John F Kennedy. Around 20 people took eight weeks to digitise the documentation, create an AI suite to understand it and develop another set of tools to analyse and visualise the understanding. It was an impressive feat made possible through intimate knowledge of available AI tools and the team's ability to exploit software languages.

Today, using AI, a single coder could generate a similar solution in eight hours. 

Continuing that pace of change, in another six years, that time could be reduced to 1 minute, even if the rate of evolution and adoption remains the same as in the last six years. We know this is false as progress accelerates exponentially rather than stays constant. We also know that humans agreeing on what they want will take far longer than the machines delivering the request.

However, based on current progress and pace, we will soon use AI to generate complex programs and create personal, unique solutions to generic tasks. We will use AI to custom-build a word processor that works as we wish, adds functions we need, and share it with others to enhance.

Part of me reads these words as pure lunacy. Why would anyone want to create a Word or GMail replacement when perfectly effective solutions and alternatives already exist? Will we continue to use our existing toolsets as we have for decades? 

The answer to why is a mix of personal customisation and a question of cost. On cost, The Software Alliance estimates that software piracy costs an estimated $45bn per year in lost revenue to software developers. This figure indicates that many people want to use software products but are unwilling or unable to pay for them. Many pirated software users would use an AI-generated alternative that was freely available with similar functions. Crucially, many currently paying fees for a software licence or application would be tempted by a freely generated option. 

Few people are loyal to a particular application because of its brand or name. They are customers because it completes a required task with an experience they appreciate. Consequently, software developers include customisation and personalisation features to improve the user experience.

Again, GenAI coding will enable users to introduce levels of personalisation unique to that individual. Backdrops, colours, icons, layouts, and features can all become unique and specific or changed at a prompt's notice. People can remove functions, redefine forms, and crucially embed the same GenAI into their solutions to learn how the application is used and prompt suggestions to improve it. User input to develop new features is vital to good software development. GenAI coding will let users bypass the need to engage with developers and write directly to the application.

Software developers will have to introduce similar features into their products to compete. Still, they cannot compete if users build their own software to save money. Even with computing and development costs, developing a bespoke service with GenAI may be significantly less than the lease or purchase of the generic application.

Building your Own Software empowered through AI (BOSAI), with AI speaking the languages of humans and software, will transform how programs are distributed and used. Once achieved, BOSAI will revolutionise the software industry that has created our AI services.

Today, we still need to reach the point where BOSAI can provide the functions or capacity required to replace large commercial software packages. It is also clearly outside software developers' interests to release a tool with GenAI that replaces their unique skills. Like the ancient scribes, developers face a challenge between empowering everyone with a new literacy in languages against removing their own legitimacy. 

This may prompt slower enthusiasm by some for these toolsets, yet it is also an ideal opportunity for disruptive entrants to the software market. After all, the software industry has been disrupting itself from its first days.

This is how AI becomes the new language.

There was no master plan for creating the Greek Phonetic Alphabet. Whilst people could see the benefits and simplicity of the approach, they did not act with an intent to change the world. They acted to make their daily lives easier to share and their daily chores quicker to complete.

So too, with AI. Software developers have created a tool that can replicate their own language and created a way of accessing that tool that can be shared quickly and simply. What was previously guarded knowledge with high barriers to understanding and use has now become easy to exploit. The master plan was not to change society but to make their daily chores of writing code and creating applications easier to achieve.

Yet, like the alphabet, the implications are immense.

Software has created today's society. Without software, our society struggles to thrive and prosper. AI becomes as powerful as the first phonetic alphabet by simplifying the language to understand and develop software. AI disrupts industry sectors and businesses by providing new opportunities and presenting different approaches that software previously answered.

The ultimate result of AI is to replace the rulers and scribes that established our software society by removing the language barrier between Humans and Software. As the new language, AI can empower us all to speak Software.

With this power in our hands and being child's play to use, what will we do with it?