An experiment measured developer productivity when switching from C to C++. While C++ compilation took twice as long, its stronger type system caught errors earlier. This resulted in C programmers recompiling twice as often, making the overall time spent compiling roughly equal for both languages.

Adding multicore support was not just about re-implementing the garbage collector. A significant challenge was designing a memory model that provides clear, useful guarantees to programmers in a type-safe language. This is much harder than in C/C++, where resorting to "undefined behavior" is an acceptable out.

Contrary to the belief that abstraction adds overhead, C++ can achieve "negative overhead." High-level constructs give the compiler more information than raw C code, allowing it to perform aggressive optimizations that result in faster executables.

Stroustrup pushes back on the narrative that C++ is inherently memory-unsafe. He cites studies showing over 90% of buffer overflows occur when developers use legacy C patterns like raw pointers instead of modern C++ features like RAII, vectors, and spans, which are designed to prevent such errors.

The creation of the Rust programming language was a direct response to fundamental weaknesses in C++. Mozilla needed a way to eliminate entire classes of security vulnerabilities (memory safety) and safely leverage multi-core processors (concurrency), which were intractable problems in its massive C++ codebase.

While speed benchmarks are flashy, a model's memory usage is the true determinant of its viability. In real-world applications, AI models must share limited resources with other processes, making a low memory footprint more critical than a marginal speed advantage for successful deployment.

Bjarne Stroustrup advises against being "too clever." Manual optimizations from the 1990s are often "pessimizations" today because they constrain modern compilers, preventing them from applying more sophisticated optimizations tailored to new CPU architectures, caches, and memory access patterns.

Go's garbage collector led to unpredictable memory usage. In Dropbox's storage system, a node OOMing would trigger a massive re-replication workload, which could cause other nodes to OOM, leading to a system-wide "congestion collapse". Rust's memory management provided the predictability needed to prevent these catastrophic failures.

Early, foundational language ideas like Lisp and Prolog came from academic settings. Today, major innovations like safe systems programming (Rust) and widespread garbage collection (Java) are driven by large companies. This shift reflects the massive engineering effort now required to launch a successful new language.