The Undervalued Tech Worker

By Michael Feldman

November 27, 2008

In our supposedly tech-driven economy, it’s common to hear about computer professionals who have lost their jobs and are unable to find new work in their field. And this was occurring even before the recession. Is the IT industry really that much at odds with its own labor market? Surprisingly, yes.

In a recent InfoWorld advice column hosted by Bob Lewis, a reader talks about an increasingly hostile tech labor marketplace — not only for workers with “legacy” skill sets, but even for those with more recent experience:

[I]t’s not just the COBOL and Fortran programmers, the OS/360 and SCOPE dinosaurs. It’s also the software architects; data-base architects; system and network administrators; PHP, Python, Ruby on Rails, and Objective-C software engineers; and heavy metal engineers who were presenting papers at national and international conferences one day, and pariah the next.

The reader follows up with a familiar observation about the industry’s indifference to providing employment continuity for the workforce:

The industry [executives have] made it clear. [They are] not interested in re-training the current workforce, which is likely adequate for its needs. No, it wants fresh bodies, preferably young or beholden ones willing to accept entry-level wages for long hours and who are either burdened with few family obligations or willing to pass them over… for the most part, companies are unwilling to re-train experienced programmers to fill available slots…

I’ve written about this on a few occasions, myself, in the context of the H-1B visa program for non-U.S. workers. But something else struck me when I read Lewis’ response:

Since I try to avoid recommending solutions that require legislation, and also try to avoid moralizing in my writing, I recommend courses of action based on this being how the world works right now. People are products in the employment marketplace. If someone can’t find a job, that means for one reason or another that person isn’t a competitive product. The problem might be marketing, packaging, pricing, or a perceived lack of quality. Whatever it is, this is no different from any other marketplace — it’s up to the seller to package, price and market a product people want to buy.

Lewis says he’s not unsympathetic to the techie’s plight; he’s just trying to be honest. And he makes a good a point.

But casting people as products is not only demoralizing, it’s wrong-headed, and it reflects some unfortunate attitudes in the IT community. Specifically, the conventional wisdom is that maximizing ROI takes precedence over maximizing innovation. While that philosophy may work in a more mature industry that isn’t subject to a lot of technological turnover, like say bubble gum manufacturing, in the computing business it’s just short-sighted.

Since tech workers are the ones that design hardware, write software, and provide services, under-investing in them has some regrettable effects. The most visible example of this is the permanent “software crisis,” which is currently playing out in the industry’s attempt to apply parallel programming to the new raft of multicore and multiprocessor platforms. Moore’s Law continues to double raw processing power every 18 months or so, but only a fraction of that is realized at the application level. But wasting cheap CPU cycles seems to make more sense than applying more human ingenuity to the problem.

To be fair, firms like Intel and Microsoft, along with help from the government, are investing a ton of money in parallel programming R&D, but most companies are willing to let this be somebody else’s problem. The answer for the industry is going to require the adoption of new software platforms and training (or retraining) workers. And that’s going to filter down to everyone.

The relocation of computing into the cloud is another challenge that’s going to require a lot of new software development, infrastructure buildout, and a whole new industry to service it. Hardware is the easy part. It’s the extra labor that’s going to be the bottleneck. If the IT community convinces itself and its customers that computing will be essentially free once it moves into the cloud, there will be little incentive to invest in human resources to make it happen.

I’m not suggesting that simply retraining old techies is going to be a magic bullet. But there has to be some realization that the industry cannot rely solely on cheap processors, “free” software, and disposable IT workers to create innovation. Ultimately, IT is a labor-intensive industry. The purpose of computer systems is not to eliminate jobs, it’s to create value and increase productivity.

At the Supercomputing Conference and Expo last week, there was a panel discussion on disruptive technologies for exascale systems. It was revealing that the four technologies highlighted were all hardware-focused: flash storage, photonic communications, 3D chip stacking, and quantum computing. It’s easy to become seduced by these inventions. Once they’re designed and implemented, they can be mass-produced, with little human intervention. As expensive as semiconductor fabs are, they can work 24/7 and don’t require health insurance and retirement benefits.

But clever software can make even great hardware humble. D-Wave CTO Geordie Rose, the panel’s quantum computing advocate, argued that new algorithms can have a much bigger payoff than more powerful silicon. He noted that using Pollard’s rho algorithm from 1977, it would take 12 years to factor a 90-digit number on a modern-day 400 teraflop Blue Gene supercomputer. But using the newer quadratic seive algorithm, it would take just 3 years to perform the same operation on a 1977 Apple II computer. When you consider the multi-million dollar investment that went into the Blue Gene supercomputer compared to the probable investment that went into developing the new algorithm, you can get some sense of the industry’s misplaced priorities.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire