After The Buzz Fades: What Our Data Tells Us About Emerging Technology Sentiment
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Information technology has always had a lot of hype around new developments. New tech emerges, piques the curiosity of technology enthusiasts, and optimistic speculation about the future begins. But sometimes when the technology gets field tested, it doesn’t always enter into wide adoption. Gartner even has a phrase for this process: the hype cycle.
We wanted to see how developers feel about the tech making headlines, so our latest pulse survey asked developers to think about nascent trends in technology and tell us how they felt about them. Despite many of these technologies having been around for quite some time, the conversations about them and their applications are evergreen and always evolving. With AI-assisted technologies in the news, this survey’s aim was to get a baseline for perceived utility and impact of a range of buzzworthy technologies in order to better understand the overall ecosystem.
The survey results’ matrix below shows four quadrants of sentiment where technologies are grouped into areas of “Positive-Emergent”, “Positive-Proven”, “Negative-Proven”, “Negative-Emergent” and in the opposing “Negative-Emergent” and “Positive-Proven” quadrants is where this analysis has placed a lot of focus due to the strength of sentiment shown in this survey’s results. Open source is clearly positioned as the north star to all other technologies, lighting the way to the chosen land of future technology prosperity.
Experimental to proven: Who’s ready for prime time?
Technologies such as blockchain or AI may dominate tech media headlines, but are they truly trusted in the eyes of developers and technologists? On a scale of zero (Experimental) to 10 (Proven), the top proven technologies by mean score are open source with 6.9, cloud computing with 6.5, and machine learning with 5.9. The lowest scoring were quantum computing with 3.7, nanotechnology with 4.5, and low code/no code with 4.6.
|Internet of Things||5.7|
|Natural Language Processing||5.5|
|Rapid prototyping tools||5.3|
|Privacy preserving technologies||4.9|
|Low code/no code||4.6|
By grouping frequency of scores into three sentiments and looking specifically at a handful of interesting standouts elsewhere in the survey, a pattern emerges: those without strong feelings about whether a specific technology is definitely experimental or proven take a larger share of the responses for those technologies that also have larger proportions of emergent ratings.
One hypothesis for this pattern is that technologies considered experimental may also be technologies that developers have less experience with and therefore absence of strong feelings. Those neutral feelings appear to be correlated with the question asked in the survey about whether respondents agreed on which technology would never be widely used in the future: blockchain and low code/no code both received more than 10% making them the top two choices in that category. It makes sense that you may grade a technology lower on the experimental-to-proven scale if you don’t believe it will be used much in the future.
Conversely and against expectations, those technologies that scored high proven scores were not necessarily the same that were chosen from our list of technologies as those that developers believed would be widely used in the future, however they were very close with one main exception. AI comes in at the top of the list by a large margin, but our three top proven selections (open source, machine learning, cloud computing) follow after. Technologists are willing to concede that AI isn’t a proven technology as of today but seem to be very positive about the direction it’s going.
The hero we need: the Positive Impact Score
It’s one thing to believe a technology has a prosperous future, it’s another to believe a technology deserves a prosperous future. Alongside the emergent sentiment, respondents also scored the same technologies on a zero (Negative Impact) to 10 (Positive Impact) scale for impact on the world. The top positive mean scoring technologies were open source with 7.2, sustainable technologies with 6.6 and machine learning with 6.5; the top negative mean scoring technologies were low code/no code, InnerSource, and blockchain all with 5.3. Seeing low code/no code and blockchain score so low here makes sense because both could be associated with questionable job security in certain developer careers; however it’s surprising that AI is not there with them on the negative end of the spectrum. AI-assisted technology had an above average mean score for positive impact (6.2) and the percent positive score is not that far off from those machine learning and cloud computing (28% vs. 33% or 32%).
|Privacy preserving technologies||6.4|
|Natural Language Processing||6.4|
|Internet of Things||6.1|
|Rapid prototyping tools||6.0|
|Low code/no code||5.3|
Possibly what we are seeing here as far as why developers would not rate AI more negatively than technologies like low code/no code or blockchain but do give it a higher emergent score is that they understand the technology better than a typical journalist or think tank analyst. AI-assisted tech is the second highest chosen technology on the list for wanting more hands-on training among respondents, just below machine learning. Developers understand the distinction between media buzz around AI replacing humans in well-paying jobs and the possibility of humans in better quality jobs when AI and machine learning technologies mature. Low code/no code for the same reason probably doesn’t deserve to be rated so low, but it’s clear that developers are not interested in learning more about it.
What can tech of the future learn from the journey of open source?
Open source software is the overall choice for most positive and most proven scores in sentiment compared to the set of technologies we polled our users about. That’s a win any way you slice it. Open source is not new, but this survey is and one has to consider that open source was not always on top. The top tags for open source for non-commercially backed technologies on Stack Overflow are Python, Java, and Ruby, while the larger network has many sites dedicated to specific open source technologies: Ask Ubuntu, Unix & Linux, Blender, Drupal, Raspberry Pi, and more.
How did open source defy the assumed necessity of paid support in order to become regarded as the proven technology it is today? A big part of the history of open source that overlaps with the user base for our survey is the love of collaboration: open source and Stack Overflow’s public platform and Stack Overflow for Teams product don’t exist without it!
Collaboration isn’t just an unpaid internship type of gig, it’s important at the office. In our last pulse survey about jobs, almost one in four developers say collaboration is what retains employees and there is reason to believe that spirit of collaboration comes from experience with open source; in the same survey, 27% of developers said contributing to open source at work made a job more appealing.
Open source isn’t just about collaboration, and neither is the developer experience. Another reason to believe open source has persevered as a proven technology today is that it is a platform for learning. Proprietary software and programming languages are not going to be the first choice for educational institutions with low thresholds for cost, and definitely not for self-taught programmers completing online courses. Learning is the backbone of coding, something we can see on Stack Overflow, as well. Python, an open-source licensed programming language, has been one of the top tags for questions as of late and all of those questions being answered equate to tangible learnings. New tech talent going through traditional education paths or self-moderated online learning modules are coming into the workforce with skills in open-source tech and sometimes through open-source platforms. From our 2022 Developer Survey, Python is almost tied as the most popular language for people learning to code. Open-source removes friction in the learning process for developers, and what’s not to love about that.
Perhaps one of the biggest contributors to the growth and goodwill behind any of the technologies listed here is salary. Over half of respondents in our December survey agreed a better salary is still the largest motivator when considering a new opportunity (54%). The potential to grow one’s personal capital by learning and using open source most certainly is a large contributor to the positive and proven sentiment we see in this survey. Software engineering is projected to have the largest average growth in salary in the next 10 years by the BLS. This is likely due to the large return on the relatively low investment of time when it comes to entering the developer industry.
One of the main takeaways from our last developer survey found that blockchain developers garnered a comparable salary to people managers but with less experience. Given the sentiment scores in this survey, it’s very interesting to watch the progression of blockchain over time. While small, question share on Stack Overflow almost tripled from 2021 to 2022 (0.05% to 0.14%) despite nearly a quarter of respondents to our Web3 pulse survey believing blockchain was all hype or a scam. Post-FTX scandal, it’s clear that most developers do not feel blockchain is positive or proven, however there is still desire to learn as more respondents want training with blockchain than cloud computing. There’s a reason to believe in the direct positive impact of a given technology when it pays the bills.
AI-assisted technology should take notes from the way open-source technology has cultivated collaboration, learning, and take-home pay if it wants to make gains in sentiment around positive impact and be regarded as a proven technology. We can see the possibility of this already with machine learning, which overlaps and is a component of AI; machine learning is the third-highest scoring for both proven technologies and positive impact tech. Machine learning is already utilizing open-source programming languages and libraries, and the median salaries of data scientists/machine learning engineers worldwide are mid-range according to the developer survey. The trajectory of machine learning shows that there is a path for AI to become more proven and positive and the core characteristics of open source lay the foundation for AI to achieve that goal.
Organizations and business leaders everywhere should heed these lessons. Open source shows that collaboration and learning around emerging tech is key to wider adoption. If you’re part of an organization trying to implement new technology, Stack Overflow for Teams can help by encouraging the healthy cycle of knowledge sharing, collaboration, learning, and ultimately, knowledge reuse. While the topics, technologies, and sentiments discussed in this survey will shift over time, we’re confident that the role of collaboration and learning will not. We look forward to seeing more discussion and more learning take place on our platform in the years to come.
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