Top 5 Emerging Programming Languages Every Developer Should Watch
From Mojo's Python-superset approach to AI to Zig's radical reimagining of systems programming, these five emerging languages are solving problems that established languages cannot. Here is why they matter and who should learn them.
The 68,000x benchmark is specific to mandelbrot set computation, comparing Mojo with SIMD and parallelization against single-threaded CPython. For typical application code, the speedup is more modest (10-100x) but still significant. The key advantage is not raw speed but the elimination of the "prototype in Python, rewrite in C++" workflow that slows down AI/ML development.
How do I start learning one of these languages?
Conclusion
The five languages on this list exist because the software industry's demands have shifted. AI requires languages that speak to GPUs natively. Security mandates require memory-safe alternatives to C and C++. Distributed systems require fault-tolerant concurrency. And modern developers expect integrated tooling and clear documentation from day one. You do not need to learn all five. Pick the one that aligns with where your career is heading, build something real with it, and evaluate whether it solves problems that your current tools cannot. The best time to learn an emerging language is before the job market demands it.
No. Python and JavaScript will remain dominant for years. These emerging languages solve specific problems where established languages fall short. Learn Python or JavaScript as your foundation, then add an emerging language based on the domain you want to specialize in. Mojo explicitly builds on Python skills, so Python knowledge transfers directly.
In terms of job postings, none of these languages rival Python, JavaScript, or Java yet. However, Zig has production deployments at Uber and powers the Bun runtime. Mojo is gaining traction in AI/ML teams at companies using Modular's platform. Gleam benefits from the existing Erlang/Elixir job market. For career investment, choose based on the domain you want to work in rather than current job listings.
Carbon is not designed to replace C++ but to evolve it. The goal is gradual migration through bidirectional interop, similar to how Kotlin coexists with Java. Even if Carbon succeeds, existing C++ codebases will run alongside Carbon code for decades. Google has been transparent that Carbon is experimental and not ready for production use.
Programming languages rarely emerge from nothing. They appear because existing languages fail to solve a specific category of problems well enough. C appeared because assembly was too tedious for large programs. Java appeared because C++ was too dangerous for networked applications. Python appeared because Perl was too cryptic for scientific computing. Rust appeared because C and C++ could not guarantee memory safety without sacrificing performance.
Now, in 2026, a new generation of languages is emerging, and each one exists precisely because something important is broken in the status quo.
"The limits of my language mean the limits of my world." -- Ludwig Wittgenstein, philosopher
The TIOBE Index for February 2026 still shows the familiar top five: Python, C, C++, Java, and C#. But the most interesting story is not at the top. It is in the rising entries, the languages climbing 10, 20, or 50 positions per year. These are the languages that solve problems the incumbents cannot, and they will reshape how software is built over the next decade.
According to the Stack Overflow 2025 Developer Survey, 84% of developers now use AI coding tools, and the languages with the best AI tooling integration tend to attract the fastest-growing communities. The emerging languages below are designed from the ground up with modern tooling, modern hardware, and modern developer expectations in mind.
The Five Languages Worth Watching
Language
Year Created
Creator
Primary Use Case
TIOBE 2026 Rank
Key Innovation
Mojo
2023
Chris Lattner (Modular)
AI/ML infrastructure
Top 100 (rising)
Python superset with systems-level performance via MLIR
Zig
2015
Andrew Kelley
Systems programming
#46 (0.19%)
C interop with no hidden control flow and comptime
Gleam
2024 (v1.0)
1. Mojo: Python's Performance Problem, Solved
Python is the most popular programming language in the world. It dominates data science, machine learning, web development, and scripting. But Python has a fundamental performance problem: it is 10 to 100 times slower than C or C++ for compute-intensive tasks. This is why most "Python" machine learning code actually runs C, C++, or CUDA under the hood through libraries like NumPy, PyTorch, and TensorFlow.
Mojo, created by Chris Lattner (the architect behind LLVM, Clang, and Swift), takes a radically different approach. Instead of building a bridge between Python and low-level code, Mojo makes Python itself fast.
"Mojo is not meant to replace Python. It is meant to give Python programmers a path to systems-level performance without leaving the language they already know." -- Chris Lattner, creator of Mojo, LLVM, and Swift
Feature
Python
Mojo
Why It Matters
Execution model
Interpreted (CPython)
Compiled via MLIR
Up to 68,000x faster for compute tasks
Type system
Dynamic only
Dynamic + optional static typing
Who Should Learn Mojo
Mojo is built for AI/ML engineers, data scientists, and anyone who writes Python but needs C-level performance. If you have ever written a Python prototype and then rewritten it in C++ for production, Mojo eliminates that step. The language is also uniquely positioned for GPU programming without CUDA, as its MLIR foundation enables hardware-agnostic acceleration.
What Makes Mojo Different
Mojo's secret is MLIR (Multi-Level Intermediate Representation), the compiler infrastructure originally developed at Google for TensorFlow and now a core part of LLVM. Unlike traditional compilers that target one type of hardware, MLIR can optimize code for CPUs, GPUs, TPUs, and custom AI accelerators from a single source. This means Mojo can generate optimized machine code for virtually any hardware without requiring developers to learn hardware-specific APIs.
2. Zig: The Modern C Replacement
C has been the foundation of systems programming for over 50 years. Operating systems, databases, embedded systems, and game engines are still predominantly written in C. But C's age shows: undefined behavior lurks everywhere, memory bugs account for approximately 70% of security vulnerabilities (according to Microsoft and Google), and the build system (Make, CMake, autotools) is notoriously complex.
Zig, created by Andrew Kelley, is not trying to be a "better C++." It is trying to be a better C: a simple, predictable systems language that gives you complete control without the hidden complexity.
Feature
C
Zig
Why It Matters
Build system
External (Make, CMake)
Built-in build system
No separate configuration language to learn
Hidden control flow
setjmp/longjmp, macros
Zero hidden control flow
Who Should Learn Zig
Zig is for systems programmers who value simplicity over abstraction. If you work on embedded systems, game engines, compilers, databases, or infrastructure software, Zig offers the control of C with dramatically better ergonomics. The Bun JavaScript runtime is written in Zig, proving the language is production-ready for performance-critical systems. Uber has also used Zig to bootstrap their infrastructure on ARM64.
What Makes Zig Different
Zig's "comptime" (compile-time execution) system lets you run arbitrary code at compile time, eliminating the need for macros, code generators, or preprocessors. This single feature replaces C's preprocessor, C++'s template metaprogramming, and Rust's procedural macros with a unified, debuggable mechanism. You write normal Zig code, and the compiler evaluates it before the program runs.
3. Gleam: Type Safety Meets Fault Tolerance
The Erlang virtual machine (BEAM) is one of the most battle-tested runtimes in computing. It powers WhatsApp (processing 100 billion messages per day), Ericsson's telecom infrastructure (99.9999999% uptime), and Discord's real-time messaging. The BEAM excels at concurrency: it can run millions of lightweight processes, each with its own memory, communicating through message passing.
But Erlang's syntax is unusual, and Elixir, while more approachable, is dynamically typed. Gleam brings the BEAM's legendary reliability together with a modern, statically-typed functional language that also compiles to JavaScript.
Feature
Elixir
Gleam
Why It Matters
Type system
Dynamic
Static (Hindley-Milner)
Catch errors at compile time, not in production
Error messages
Runtime exceptions
Clear, helpful compile-time errors
Who Should Learn Gleam
Gleam is ideal for developers building web services that must never go down: messaging platforms, payment systems, real-time collaboration tools, and API backends. If you value the idea of "let it crash" fault tolerance (where individual processes fail and restart without affecting the system) but want compile-time type safety, Gleam is the answer. Its JavaScript compilation target also makes it viable for full-stack development.
What Makes Gleam Different
Gleam has no macros, no exceptions, no null values, and no implicit behavior. Everything is explicit and visible. Combined with the BEAM's ability to run millions of concurrent green threads and hot-swap code in production without downtime, Gleam delivers reliability that most languages cannot match. Version 1.0, released in 2024, marked the language as stable for production use.
4. Carbon: Google's Answer to the C++ Problem
C++ is everywhere. Game engines (Unreal Engine), browsers (Chrome, Firefox), databases (MySQL, MongoDB), and operating systems (Windows kernel) all depend on billions of lines of C++ code. But C++ has accumulated 40+ years of complexity: templates, multiple inheritance, header files, the preprocessor, and an ever-growing specification (C++23 is over 2,000 pages).
Carbon, announced by Google in 2022 and led by Chandler Carruth (a principal engineer who led C++ compiler work at Google), is designed as an evolution of C++ rather than a replacement.
Feature
C++
Carbon
Why It Matters
C++ interop
Native
Full bidirectional interop
Migrate incrementally, not all at once
Syntax complexity
Extremely complex (2,000+ page spec)
Modern, clean syntax
Carbon is still experimental in 2026. Google has been transparent that the language is not production-ready and that the design is still evolving. Its inclusion here is because of the scale of the problem it addresses: no other language has attempted full bidirectional C++ interop as a core design goal. If Carbon succeeds, it provides a migration path for the billions of lines of C++ that power critical infrastructure.
Who Should Learn Carbon (or Watch It)
Right now, Carbon is for language enthusiasts and C++ developers interested in shaping the future of systems programming. The language is not yet suitable for production use. However, if you maintain a large C++ codebase and have struggled with modernization, Carbon's interoperability promise is worth tracking. Google's investment and the team's pedigree (including key LLVM and Clang contributors) suggest this is a serious, long-term effort.
What Makes Carbon Different
Carbon's defining feature is bidirectional C++ interoperability. You can call Carbon from C++ and C++ from Carbon in the same project, enabling gradual migration. This is a lesson learned from Kotlin's success with Java: the languages that win are the ones that let you adopt them incrementally rather than requiring a complete rewrite.
5. Bend: Automatic GPU Parallelism Without the Pain
GPU programming has traditionally required expertise in CUDA, OpenCL, or Vulkan Compute, specialized APIs that are notoriously difficult to learn and debug. Bend, created by the Higher Order Company, takes a fundamentally different approach: you write normal, sequential-looking code, and the compiler automatically parallelizes it across GPU cores.
Feature
CUDA/OpenCL
Bend
Why It Matters
Parallelism model
Explicit (manual thread management)
Automatic (compiler-driven)
Write sequential code, get parallel execution
Hardware abstraction
GPU-specific (CUDA = NVIDIA only)
Hardware-agnostic
Who Should Learn Bend
Bend is for developers who need parallel computing power but do not want to learn GPU programming. Data scientists running simulations, researchers processing large datasets, and anyone who has hit the performance ceiling of single-threaded code can benefit. Bend is early-stage, but its approach to automatic parallelization addresses one of the largest barriers in computing: making parallel hardware accessible to regular developers.
What Makes Bend Different
Bend is built on HVM2 (Higher-order Virtual Machine 2), an interaction-net-based runtime that achieves parallelism through a mathematical property called optimal reduction. Unlike traditional parallel programming where the developer must explicitly manage threads, locks, and synchronization, Bend programs are parallelizable by construction. If your code can logically run in parallel, HVM2 will run it in parallel. No annotations, no thread pools, no mutexes.
Language Comparison: Finding Your Match
Dimension
Mojo
Zig
Gleam
Carbon
Bend
Primary domain
AI/ML infrastructure
Systems programming
Distributed web services
C++ modernization
How to Choose Which Language to Learn
Not every language on this list is the right investment for every developer. The decision depends on your career direction and the problems you need to solve.
Your Current Role
Recommended Language
Reasoning
Python data scientist or ML engineer
Mojo
Direct upgrade path with full Python interop
C/C++ systems programmer
Zig or Carbon
Zig for greenfield projects, Carbon for existing C++ codebases
Backend web developer
The Broader Trend: Why These Languages Exist Now
These five languages share common themes that reflect where software engineering is heading:
Trend
Languages Addressing It
What Changed
AI/ML as mainstream engineering
Mojo, Bend
AI workloads demand GPU and accelerator programming
Memory safety as a requirement
Zig, Carbon, Mojo
Government mandates (CISA, NSA) and enterprise security policies
Distributed systems everywhere
The developer experience revolution is also shaping these languages. Every language on this list ships with an integrated build system, formatter, and package manager. The days of spending hours configuring makefiles and dependency management are ending for new languages.
Mojo brings systems-level performance to Python through MLIR, making it the strongest candidate for AI/ML developers who want to eliminate the Python-to-C++ rewrite cycle
Zig is the modern C replacement with zero hidden control flow, built-in cross-compilation, and production deployments at Uber and in the Bun runtime
Gleam combines the BEAM's legendary fault tolerance with static type safety and JavaScript compilation, making it ideal for distributed web services
Carbon is Google's long-term answer to C++ modernization through bidirectional interoperability, though it remains experimental
Bend introduces automatic GPU parallelization without explicit threading, lowering the barrier to parallel computing
Choose a language based on your career direction and the problems you need to solve, not hype cycles
Louis Pilfold
Distributed web services
Rising
Type-safe functional on Erlang VM with JavaScript compilation
Carbon
2022
Google (Chandler Carruth)
C++ modernization
Experimental
Full C++ interop with modern syntax and memory safety
Bend
2024
Higher Order Company
Massively parallel computing
New
Automatic parallelization on GPUs without explicit threading
Gradual adoption without rewriting existing code
Memory management
Garbage collected
Ownership model (like Rust)
No GC pauses, deterministic performance
Hardware support
CPU only (needs C extensions for GPU)
CPU + GPU + AI accelerators natively
Write once, run on any hardware
Python interop
Native
Full interop (import any Python library)
Zero migration cost for existing Python codebases
Package ecosystem
pip (500,000+ packages)
Uses pip + native Mojo packages
Access to entire Python ecosystem from day one
Every operation is visible in the source code
Memory safety
None (manual management)
Optional safety checks + allocator-aware
Catches errors in debug, zero-cost in release
C interop
Native
Direct import of C headers
Drop-in replacement for C in existing projects
Cross-compilation
Complex toolchain setup
Built-in cross-compilation
Compile for any target from any host with one command
Undefined behavior
Extensive (200+ categories)
Defined behavior everywhere
No "nasal demons," predictable program behavior always
Faster debugging, fewer production incidents
JavaScript target
LiveView (server-rendered)
Compiles to JS with TypeScript definitions
Share code between server and client
Package manager
Hex (mix)
Built-in (gleam add)
One tool for everything
OTP compatibility
Full
Full
Access to 30+ years of battle-tested libraries
Learning curve
Moderate (macros, protocols)
Low (no macros, explicit everything)
Productive immediately
Faster onboarding, fewer parsing ambiguities
Build times
Notoriously slow (templates, headers)
Designed for fast compilation
Faster development iteration cycles
Memory safety
Manual (optional smart pointers)
Safety by default (planned)
Fewer security vulnerabilities
Generics
Templates (duck typing, no constraints)
Checked generics (like Rust traits)
Errors caught at definition, not instantiation
Error handling
Exceptions vs error codes (inconsistent)
Unified error handling model
One consistent pattern across all codebases
Runs on any GPU or multi-core CPU
Learning curve
Very steep (GPU architecture knowledge required)
Minimal (looks like Python/Haskell)
Accessible to non-GPU specialists
Debugging
Extremely difficult (race conditions, deadlocks)
No race conditions by design
Functional purity prevents concurrency bugs
Performance scaling
Excellent (but requires expert optimization)
Near-linear with core count
Performance scales without manual tuning
Interop
C/C++
HVM2 runtime
New ecosystem, still growing
Parallel computing
Performance tier
Near C/C++ speed
C-equivalent
BEAM VM (good, not systems-level)
C++-equivalent (planned)
GPU-accelerated
Learning curve
Low (if you know Python)
Moderate
Low (simple, explicit)
Moderate-high (like C++)
Low-moderate
Production readiness
Growing (Modular ecosystem)
Production-ready
Production-ready (v1.0+)
Experimental
Early-stage
Community size
Growing rapidly
Established niche
Growing rapidly
Early-stage
Small but active
Job market (2026)
Emerging (AI/ML roles)
Niche (systems roles)
Niche (Erlang/Elixir ecosystem)
Not yet
Not yet
Best for career if you are
AI/ML engineer or data scientist
Systems or embedded developer
Backend or distributed systems dev
C++ developer
Researcher or data scientist
Gleam
Fault-tolerant distributed systems with type safety
Full-stack JavaScript developer
Gleam
Compiles to JS, introduces type-safe functional programming
Game developer
Zig
Performance of C, better ergonomics, cross-compilation
Research scientist
Bend or Mojo
Bend for parallel computation, Mojo for AI/ML
DevOps/Infrastructure engineer
Zig
Systems-level CLI tools, small binaries, cross-compilation
Gleam
Microservices, edge computing, and real-time applications
C/C++ modernization pressure
Zig, Carbon
70% of security vulnerabilities are memory-related
Developer experience expectations
All five
Modern tooling, clear error messages, integrated build systems
