\u201cLearn to code.\u201d For decades, that was the <\/em>go-to advice for anyone entering the workforce or looking to make a career change. <\/p>\n\n\n\n Software engineers earned higher-than-average starting salaries, and as the digital world exploded in size, demand for their skills seemed endless. The kicker? Coding was pitched as something anyone<\/em> could learn in just a few months, thanks to online bootcamps.<\/p>\n\n\n\n The reality turned out to be a little different \u2014 not everyone is wired for the kind of linear thinking<\/a> coding demands \u2014 but still, software engineering seemed like a solid career choice in the early 21st century, especially for anyone with the aptitude and hustle to beat the competition for coveted entry-level positions.<\/p>\n\n\n\n But now, practically anyone can code: All they have to do is tell an AI what they want to build \u2014 like a new version of the Freethink website<\/a> \u2014 and it\u2019ll translate their words into working software in minutes.<\/p>\n\n\n\n According to the Bureau of Labor Statistics, employment for computer programmers is now expected to shrink by 10%<\/a> between 2023 and 2033 \u2014 a loss of more than 13,000 jobs \u2014 and unemployment rates for recent computer science graduates are already on the rise<\/a>.<\/p>\n\n\n\n So, is there any point in learning to code anymore?<\/p>\n\n\n\n In search of answers to those questions, this week\u2019s Future Explored is taking a look back at the history of programming and talking to the creator of the most popular AI coding assistant in the world to find out whether he thinks \u201clearn to code\u201d is still worthwhile advice. <\/p>\n\n\n\n Like so many transformative technologies<\/a>, the roots of computer programming can be traced to the US military.<\/p>\n\n\n\n Artillery soldiers need to consider a lot of factors when aiming their weapons, including distance, shell weight, and wind speed. To save them time on the battlefield, the US Army funded the development of the Electronic Numerical Integrator and Computer (ENIAC), a machine designed to generate charts that soldiers could use to quickly determine their targets.<\/p>\n\n\n\n Getting ENIAC to perform the calculations needed to create these charts was a tedious process. <\/p>\n\n\n\n Mathematicians first had to figure out on paper how ENIAC\u2019s hundreds of switches and cables needed to be configured in order to run the desired calculation. After making those changes manually, they\u2019d use punch cards \u2014 sheets of stiff paper with precisely placed holes \u2014 to feed data into ENIAC. It would then perform the calculation and deliver its results as a new punch card that the mathematicians could analyze.<\/p>\n\n\n\n That whole process could take weeks and had to be repeated for each calculation, but in 1948, ENIAC was modified to store a simple program internally, which eliminated the need to rewire the computer for every calculation. This sped up the process, but a human still needed to calibrate the machine and handle the input and output of punch cards.<\/p>\n\n\n\n By the end of the 1950s, the first higher-level computer programming languages had emerged. Now, instead of using punch cards to communicate with computers, people could type their commands into a teleprinter. The computer\u2019s results would print almost instantly, but a mistake could mean having to retype entire lines of code \u2014 still a time-consuming process.<\/p>\n\n\n\n Then came the rise of video monitors in the \u201870s. This was a huge turning point as it meant programmers could edit their code right on a screen, moving the cursor to fix bugs without having to retype entire segments of their program. <\/p>\n\n\n\n The arrival of personal computers in the \u201880s then democratized computer programming. Instead of being accessible only to people connected to universities, government offices, and other big institutions, anyone with a PC could code from the comfort of their own home. <\/p>\n\n\n\n This created a market for new programming tools, such as integrated development environments (IDEs) \u2014 platforms that combine editing, debugging, and compiling into one interface, helping programmers work more efficiently.<\/p>\n\n\n\n The internet then gave all those new coders access to a whole network of other programmers to learn from and collaborate with. It also created demand for a lot of new things to code: People wanted websites, games, e-commerce platforms, and new communication systems.<\/p>\n\n\n\n Many new programming languages were also born in the late 20th century. Some were specialized for certain applications, like web development, while others were designed to make programming more accessible to beginners. <\/p>\n\n\n\n Before the turn of the century, many programming tools were already offering basic autocomplete functions, suggesting lines of code to programmers. By the 2010s, some of these autocomplete systems were advanced enough that they could use context \u2014 the code around the code \u2014 to make more intelligent suggestions.<\/p>\n\n\n\n We\u2019re now entering a new era in programming: By training generative AI models on huge databases of code, developers have created \u201ccoding assistants\u201d that can write whole blocks of code in response to a user\u2019s prompt.<\/p>\n\n\n\n Microsoft subsidiary<\/a> GitHub \u2014 a platform used by more than 150 million developers and four million organizations \u2014 was at the forefront of discovering the transformative potential of generative AI in software engineering. <\/p>\n\n\n\n \u201cWe started exploring this whole topic in the middle of 2020,\u201d GitHub CEO Thomas Dohmke told Freethink. \u201cGPT-3, OpenAI’s then state-of-the-art model, had just gone into early preview. We got access and started playing with the model and, on a Zoom call like this one, we asked it to write little code snippets, methods or functions, in different programming languages.\u201d<\/p>\n\n\n\n \u201cIt was able to write proper code, putting the parentheses, colons, and whatnot in the right places, and it could differentiate between different programming languages,\u201d he continued. \u201cIt wasn\u2019t making mistakes when asked to write a method in Python or a function in JavaScript.\u201d<\/p>\n\n\n\n Following the preview, GitHub started building a coding assistant, now known as Copilot, on top of Codex<\/a>, an AI model that OpenAI had fine-tuned specifically for programming.<\/p>\n\n\n\n \u201cFrom June 2020 to June 2021, we shipped the first preview of Copilot, and a year later, we launched general availability to over a million users,\u201d said Dohmke. \u201cBy November 2022, ChatGPT arrived and changed how AI systems are seen, but Copilot was already in the market.\u201d<\/p>\n\n\n\nWhere we\u2019ve been<\/h3>\n\n\n\n
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Where we\u2019re going (maybe)<\/h3>\n\n\n\n