2021: A Year in Tech Review

A lot has happened in the tech industry in 2021. As CTO of IZENTE, I have a unique perspective on how these changes are impacting the tech world.  I will highlight some of the important changes that happened, and what they will mean for scientific businesses.

1. Java 17 Release

A lot of businesses have been stuck using Java 8 (2014) or Java 11 (2018) for various reasons (stability, packaging, lack of interesting enough features).  However we should expect to see rapid uptake of the latest LTS version Java 17.  Clients and programmers are much more excited about the latest release due to long demanded features like sealed classes, the jpackage tool, records, and massive improvements in the garbage collection.

We have already heard some interest expressed in moving python workloads to Java 17.  So in 2022, don’t be surprised to hear more about Java.

2. Rust 2021 Edition

Another programming language update we saw was Rust’s 2021 edition.  Rust doesn’t really have versions like other languages, instead it uses an edition model where language features are basically frozen, but still compatible with newer compilers.  The latest update solves some pain points, but fails to provide the number one most requested change from clients: a stable inline assembly mechanism.

We have heard a lot of interest in moving legacy projects to Rust from C and C++, but most companies are still waiting on desperately needed language features to stabilize before they can make the move.

3. Rise of AI Moderation

This year we have seen many rapid improvements in the capabilities of AI systems, and some stabilization of AI SAAS’s like OpenAI’s GPT-3 models. Already we have worked with customers to implement moderation and chat systems based on these products that would have been unthinkable even two years ago.

In testing most of these products are good enough to eliminate large amounts of human work.

4. Data Science on a Deathbed

Most companies in 2021 had either a Data Science or Data Engineer role of some type.  However, most insights delivered by these professionals left businesses a bit disappointed.  In an informal internal survey conducted earlier this year by a partner, they found only 22% of managers were happy with the results they were getting from their internal data teams.  This tracks with our own experience talking with clients.

Coming into the next year, we are expecting a lot of businesses to either outsource their Data Science needs or to start reducing headcount.

5. Facial Recognition

Another of the most in demand technologies this year was Facial Recognition. Especially for off-center or partially obscured visuals.  Out of the box technologies have a large amount of difficulty in these situations, but most clients need this.

Coming into 2022, expect more small companies providing white box solutions for facial recognition.

I hope this has been an informative article. If you work in a scientific industry and need help with a software project, please feel free to reach out to

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