Single-threaded programming is already considered a complicated task. The move to multi-threaded programming only increases the complexity and cost involved in software development due to rewriting legacy code, training of the programmer, increased debugging of the program, and ef- forts to avoid race conditions, deadlocks, and other prob- lems associated with parallel programming. To address these costs, other approaches, such as automatic thread ex- traction, have been explored. Unfortunately, the amount of parallelism that has been automatically extracted is gener- ally insufficient to keep many cores busy. This paper argues that this lack of parallelism is not an intrinsic limitation of the sequential programming model, but rather occurs for two reasons. First, there exists no framework for automatic thread extraction that brings to- gether key existing state-of-the-art compiler and hardware techniques. This paper shows that such a framework can yield scalable parallelization on several SPEC CINT2000 benchmarks. Second, existing sequential programming lan- guages force programmers to define a single legal program outcome, rather than allowing for a range of legal out- comes. This paper shows that natural extensions to the se- quential programming model enable parallelization for the remainder of the SPEC CINT2000 suite. Our experience demonstrates that, by changing only 60 source code lines, all of the C benchmarks in the SPEC CINT2000 suite were parallelizable by automatic thread extraction. This process, constrained by the limits of modern optimizing compilers, yielded a speedup of 454% on these applications.