Devin, The First AI Software Engineer
Devin, The First AI Software Engineer
The Software Engineer is Dead. Long Live the Software Engineer.
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Devin is the new AI by Cognition Labs and when the demo dropped, the discussion around SWEâs getting their jobs replaced re-emerged. Everyone from new grads to seasoned industry veterans can feel monumental shifting underfoot. Is the software engineer dead? Letâs get into it.
Devin, The First AI Software Engineer
Cognition Labs, who raised a $21 million Series A in Nov 2023, recently dropped their demo video of Devin, the first AI software engineer.
Given Devinâs impressive performance, talk about how software engineers would lose their jobs and that computer science was âno longer a field worth majoring inâ sparked again on Twitter.
But despite the wow-factor, some experts such as Gergely Orosz, noted that Devin did not perform much better than existing coding assistants and that the claim of âAI software engineerâ over âAI coding assistantâ was likely due to severe competition:
AI dev tool startups need outlandish claims to grab attention. The âAI coding buddyâ space feels already saturated. For organizations hosting source code on GitHub, Copilot is a no-brainer. For companies using Sourcegraph for code search, Cody is the clear choice. Those using Replit for development, the Replit AI tool is the one to go with. The only major source control platforms that don't have AI assistants yet are GitLab and Bitbucket, and this is surely just a matter of time.
This means, one of the few ways to launch an AI dev tools startup is to claim you will fully replace software engineers. Anything less, and thereâs no reason why developers should swap their existing coding assistant, and open up their codebase for a brand new tool to crawl and contribute to.
The Cognition AI team smashed it on media attention; it feels like their launch garnered almost as much developer attention as ChatGPTâs launch in November 2022. The company broke through to mainstream media â no small feat!
And it seems Cognition Labsâs claims worked quite well; they then raised an additional $50 million Series B, for a total evaluation of $2 billion, within less than 6 months after founding.
But even if Cognition Labsâs AI Devin is a coding assistant, there is some validity to how software engineering as we know is changing & significantly so. After all, a AI coding assistant completing 1/7 of coding tasks is where we are today. Itâs the floor; the performance is only going to get better from here.
So does that mean software engineers are an endangered species? Well software engineering will change but not in the dystopic way tweeters like to push it.
To understand where weâre headed, we have to understand where weâve been.
Software Engineering: The Evolution
Intially, programming was a labor-intensive process involving manual hardware switching. Engineers interacted directly with the machine's innards, toggling switches to represent binary valuesâ1s and 0s; with enough switches, you could create a simple calculation.
Then the interface evolved. People began using cards with circles punched out and then inserting them into slot machines. Punches in the card signified 1s & 0s and formed the basis of an equation to run. Someone who worked with computers punched cards, then inserted them into machines to run calculations and recorded outputs.
Then the magnetic tape storage in the 1950âs and terminal interface emerged in the 1960s, marking the transition from physical to electronic interaction with computers. Suddenly to work with computers was to type into a terminal; the green glow and plastic look of IBM computers are familiar in this era.
But binary is not exactly friendly; reading a long list of 1s & 0s makes it hard to tell what exactly is going on. So low level languages entered the scene. We had COBOL (1959), then later more easier to use languages such as C (1972) that handled even greater abstraction. In order to center software around handling data, methods such as object-orientated programming arose (1960âs). From there came the radical concept of the GUI (Graphical User Interface) from XParc in the 1970s (ever heard of the mouse?) followed by the IDE (Interactive Developer Environment) in the 1980s. Researchers figured out that typing into a terminal straight wasnât too enjoyable or productive for engineers; turns out we want more than just black and green screen.
But newer generations of engineers were not satisfied. Why bother with type checking and memory management; why not have check at runtime & automatically handle memory? And so came around high level languages such as Python in 1991.
You now typed human-readable words in a relatively nice looking screen to program a machine.
From here, computer science continued to grow in popularity and in ease to learn. Version control systems like Git emerged in the 2000s to handle many developers entering the field and working on the same piece of software with cloud computing platforms (AWS) took to the scene to offer scalable resources on demand.
With massive codebases and libraries of documentation, AI-assisted coding tools such as Github Copilot and AI Devin entered to aid engineers in making sense of the growing complexity and to drive impact much sooner.
We now review each otherâs code from across the globe, push&pull at all hours of the day, establish elaborate APIs + standards + system architecture, and collaborate with others to update behemoths of code that no single engineer could hope to comprehend.
While in each of these eras how we interacted with a computer changed, the fundamental interaction with computers remained the same. Therefore, our need to interact with machines wonât change; itâs just that software engineers are the latest iteration of doing so.
Today, software engineers may use LLMs to generate 50% of their code and then fill in the rest with their know-how. 5 years from now, the job title, âsoftware engineerâ may be defunct and it may be software prompters who prompt & style machine behavior to meet business needs. This may involve a long running conversation that requires great deal of effort to get that final 2% of tuning, a sort of 80-20-Pareto-principle-situation. In 10 years, it may be software facilitators who guide long running discussions back and forth between clients and machines to properly craft exceedingly complex visions into digestible instructions for highly intelligent machines.
To adapt, software engineers have to continue to do what weâve always been doing; evolving with the technology.1 And in doing so, tomorrowâs software engineers will look different than todayâs just as they always have.Â
-KiranÂ
And interestingly enough, unlike previous technologies, the introduction of LLMs acclerates learning. Whereas the transition from C to Python did not aid C programmers in learning Python, LLMs also have the capacity to cushion the disruption that they create.
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