Why Parents Should Help Kids Learn Hardware, Not Just Software — The Career Case for Silicon

Every conversation about AI careers leads, eventually, to software. Machine learning engineers, data scientists, AI trainers — the careers most parents hear about are all built on code. Code is important. But code runs on hardware, and hardware is the constraint that determines what’s possible.

The AI models powering everything from ChatGPT to self-driving cars run on specialized chips: NVIDIA’s H100 GPUs, Google’s Tensor Processing Units, Apple’s Neural Engine. Those chips don’t get designed by software engineers. They get designed by hardware engineers who understand how to translate computational requirements into physical silicon — transistors, logic gates, memory cells, interconnects — at nanometer scale.

The United States is in the middle of a deliberate effort to rebuild domestic chip manufacturing capacity after decades of offshoring. The CHIPS and Science Act, signed into law in August 2022, directed $52.7 billion toward domestic semiconductor manufacturing, research, and workforce development. Intel announced plans for two new fabs in Ohio. TSMC is building in Arizona. Samsung is investing in Texas. Every one of these facilities needs engineers — and the pipeline of engineers who understand hardware at this level is severely constrained.

For parents thinking about their kids’ futures in technology, hardware is worth a serious look. Not instead of software. In addition to it.

What Chip Design Actually Involves

“Chip design” covers a wide range of specialized roles. Understanding the basic workflow helps parents make sense of what different engineers actually do.

A modern processor chip might contain 50–100 billion transistors — microscopic switches, each smaller than a virus. Designing something of that complexity requires a highly structured division of labor:

RTL (Register Transfer Level) engineering is where chip designers spend most of their time. RTL is the description of a chip’s behavior at the level of how data moves between registers (storage elements) on each clock cycle. Engineers write RTL using Hardware Description Languages — primarily Verilog or VHDL. If you write RTL, you’re describing what the hardware should do; the subsequent design steps translate that description into actual physical implementation. RTL engineers need to understand both the architecture of the chip (what it’s supposed to compute) and the digital logic that implements it (how to build that computation from basic building blocks).

Logic synthesis is the automated process that converts RTL code into a “netlist” — a description of how specific logic gates should be connected. EDA (Electronic Design Automation) tools from Synopsys, Cadence, and Siemens do this work, but engineers must understand what the tools are doing to constrain them correctly and interpret their output.

Physical design translates the logical description into a physical layout — where specific cells go on the chip, how they’re connected by metal wires, how the chip is divided into regions with different characteristics. Physical design engineers work in nanometer units. At advanced nodes (TSMC’s 3nm process, which Apple’s A17 Bionic uses), individual features are a handful of nanometers across. Getting the physical design right — timing, power, area — is a complex optimization problem.

Verification is, by headcount, the largest function in most chip design teams. Chip bugs cannot be patched after manufacturing the way software bugs can be patched after release. A silicon “re-spin” — manufacturing a corrected version of a chip — costs tens of millions of dollars and takes 3–6 months. So verification engineers work to exhaustively prove that the chip design is correct before any silicon is made. Intel’s Pentium FDIV bug in 1994 — a floating-point division error — cost the company $475 million in write-offs. The lesson has not been forgotten.

What the Data Shows

The semiconductor industry’s workforce gap is documented in detail by the Semiconductor Industry Association (SIA).

Role	Median Annual Salary (2024)	Typical Education	Demand Outlook
RTL / Logic Design Engineer	$130,000–$180,000	BS EE or CE	High — CHIPS Act hiring underway
Senior RTL Engineer	$180,000–$250,000	BS/MS EE or CE	Very high
Physical Design Engineer	$140,000–$200,000	BS/MS EE	High
Verification Engineer	$130,000–$190,000	BS/MS EE or CS	Very high
DFT (Design for Test) Engineer	$130,000–$180,000	BS/MS EE	High
VLSI Research Scientist	$180,000–$300,000	PhD EE	High
Principal / Staff Chip Architect	$250,000–$400,000+	MS/PhD EE	Extremely high

Sources: SIA/Oxford Economics Workforce Report (2023); Levels.fyi semiconductor salary data (2024); BLS Electrical and Electronics Engineers (2024).

A 2023 joint report by the Semiconductor Industry Association and Oxford Economics projected that by 2030, the US semiconductor industry will have 67,000 unfilled positions if current workforce development trends continue. That projection was published before full CHIPS Act investments began materially increasing fab construction — meaning actual demand could exceed that projection.

NVIDIA’s market capitalization, briefly the highest of any company in the world in 2024, reflects what happens when demand for AI hardware outstrips supply. NVIDIA’s H100 GPU — the chip data centers use to train large AI models — sold for $30,000–$40,000 per unit in 2023, and customers waited months for allocation. The people who design the next generation of those chips are paid accordingly. NVIDIA’s senior chip architects have total compensation packages regularly exceeding $500,000.

How Hardware and Software Are Converging

The clean separation between hardware and software that existed in the 1990s and 2000s is collapsing. The engineers who understand both sides of that boundary are becoming the most valuable technical professionals in the industry.

Apple is the clearest example. The company’s competitive advantage in iPhone performance is largely its ability to design custom silicon — the A-series and M-series chips — optimized specifically for Apple’s software. The engineers who designed the M4 chip’s Neural Engine (the dedicated AI accelerator) had to understand both how the chip would be fabricated and how the machine learning frameworks running on it would use its computational resources. That requires hardware and software knowledge simultaneously.

Google’s Tensor Processing Unit (TPU) program is similar. The TPU was designed from scratch to accelerate TensorFlow operations — a software framework — and required teams where hardware architects and ML framework engineers worked closely enough to effectively speak each other’s language.

NVIDIA’s CUDA ecosystem — the software platform that makes GPU programming accessible to ML researchers — was designed by engineers who understood the hardware deeply enough to expose it in a useful programming model. The CUDA team sits at the hardware-software boundary.

This convergence is what’s sometimes called “hardware-software co-design,” and it’s increasingly where the hardest and most valuable engineering work happens. A software engineer who doesn’t understand hardware constraints can’t contribute meaningfully to this work. A hardware engineer who doesn’t understand how their chip will be used by ML frameworks can’t design it optimally. The combination is what employers are actually looking for.

Why Hardware Expertise Differentiates in Ways Software Alone Cannot

A parent might reasonably ask: if chip design is so specialized and takes so long to learn, why not just focus on software? Software skills are widely applicable. Hardware skills seem narrower.

The answer is scarcity. Far more people learn to write software than learn to design hardware. A competent Python programmer competes with millions of other competent Python programmers. A competent RTL engineer with 5 years of experience competes with a much smaller pool.

The other answer is that hardware knowledge compounds over time in ways that software knowledge doesn’t always. A software developer’s skills can become obsolete when a programming paradigm shifts. The physics of transistors, the principles of digital logic, the constraints of timing closure — these don’t change when a new JavaScript framework comes out. The foundational knowledge of hardware engineering has a longer half-life.

As I’ve seen firsthand in consumer electronics work, the engineers who become truly indispensable are almost always the ones who can trace a problem from the highest software layer — an app that’s running slowly — all the way down to the hardware resource bottleneck causing it. That tracing requires knowing both levels well. Most engineers know one level well and the other level superficially.

What This Career Path Actually Looks Like

The typical entry path is an Electrical Engineering or Computer Engineering BS, with coursework in digital logic design, computer architecture, VLSI design, and HDL programming. Many programs require students to complete a chip design project — designing a simple processor in an HDL, simulating it, and sometimes taping it out (submitting it for physical fabrication) through programs like Google’s Open MPW Shuttle.

Graduate school (MS or PhD) substantially increases the ceiling. Staff and principal engineering roles at NVIDIA, Apple, Intel, and AMD — the roles that command $300,000–$500,000+ total compensation — typically require either a graduate degree or an unusually deep track record. But MS programs in EE or Computer Architecture at strong programs (Stanford, MIT, Georgia Tech, University of Michigan, UC Berkeley) typically produce graduates who enter at salaries around $150,000–$180,000.

The internship pipeline is critical. Apple, NVIDIA, Intel, Qualcomm, and AMD all run robust internship programs for hardware engineering students. These internships are competitive — they often convert to full-time offers, and the companies recruit actively from a relatively small set of programs. Grades in digital design and computer architecture coursework matter for these applications more than in software engineering recruiting, because the pool is smaller and the coursework is more directly predictive of job performance.

The geographic concentration of chip design work is significant. The San Francisco Bay Area (Intel, AMD, NVIDIA, Apple, Qualcomm’s design center, dozens of fabless startups), Austin (Samsung Semiconductor, Apple’s chip design center, NXP), and increasingly the Phoenix area (TSMC, Intel) are where most of the design work happens. Remote hardware engineering roles are less common than remote software roles — the tools, test equipment, and collaboration patterns lean toward in-person.

What Parents Should Do

Treat electronics as a serious interest, not just a phase

When a kid tears apart an old device to see how it works, or spends hours on YouTube watching explanations of how CPUs work, or becomes obsessed with building circuits — these are early signals worth nurturing. Buy them a basic electronics kit. Help them get an oscilloscope (used ones are inexpensive). Let them break things. The hands-on intuition about how circuits behave is something that takes years to develop and is hard to teach from a textbook alone.

Get them into digital logic early

The conceptual foundation of chip design is digital logic: AND gates, OR gates, NOT gates, flip-flops, multiplexers. These can be taught at a very conceptual level to a curious 10-year-old, and at a serious depth by 14 or 15. Nand2Tetris (nand2tetris.org) is a free project and course that takes students from a single NAND gate all the way to a working computer — writing the assembler, the compiler, and a simple operating system. It’s one of the best technical education resources available online, it’s free, and it directly teaches the foundations of computer hardware.

Point them toward AP Physics and beyond

Digital circuits are physical. Current, voltage, timing, and signal integrity are grounded in physics. Students heading toward EE or CE programs will find that solid physics preparation — AP Physics C ideally, but AP Physics 1 and 2 at minimum — makes their early college coursework significantly more manageable.

Show them the CHIPS Act workforce opportunity

The CHIPS and Science Act’s workforce development provisions include funding for university programs, community college training, and apprenticeships specifically in semiconductor manufacturing and design. The NSF is funding new academic programs in semiconductor engineering. Several community colleges in states receiving fab investment (Arizona, Ohio, New York, Texas) are creating associate degree programs that can feed directly into technician roles at new fabs. For kids who are interested in hardware but not planning for a four-year university, these pathways are worth knowing about.

Connect the hardware story to AI — it makes it concrete

One of the clearest ways to make hardware relevant to a teenager in 2025 is through the AI chip story. ChatGPT runs on NVIDIA H100 GPUs. Apple’s iPhone can run AI features locally because of the Neural Engine in the A-series chip. The reason AI models have improved so dramatically is partly algorithmic — but also substantially driven by better hardware. Helping a kid see that the AI capabilities they interact with every day are only possible because engineers designed specific hardware to support them makes the abstract career concrete.

What to Watch Over the Next 3 Years

CHIPS Act fabs will come online and begin hiring at scale. TSMC’s Arizona Fab 21 Phase 1 began production of 4nm chips in 2024. Phase 2, targeting 3nm, is under construction. Intel’s Ohio fab complex is under construction. These facilities need thousands of process engineers, equipment engineers, and yield engineers — and the industry has been clear that domestic talent is not sufficient to fill the roles. Workforce development programs attached to these investments will create funded training pathways that didn’t exist three years ago.

AI hardware specialization will drive new chip architectures. The demand for AI accelerators — chips designed specifically for ML inference and training workloads — is creating a wave of chip design activity at companies large and small. NVIDIA, AMD, Intel, Google, Amazon, Apple, Microsoft, Qualcomm, and dozens of startups (Groq, Cerebras, Tenstorrent, SambaNova) are all designing specialized AI chips. The engineers who understand both the hardware architecture and the computational requirements of AI workloads are the ones designing these chips.

Open-source hardware will lower the learning barrier. RISC-V — an open-source instruction set architecture — has reduced the barriers to learning chip design by providing a freely available architecture that students and researchers can implement without licensing fees. The RISC-V Foundation has over 3,000 member organizations. Universities are building RISC-V processor design into their curricula. Kids who learn digital design using RISC-V are working with tools and concepts directly relevant to what industry uses.

Frequently Asked Questions

Is chip design the same as electrical engineering?

Related but distinct. Electrical engineering is a broad discipline that includes power systems, signal processing, communications, analog circuit design, and digital design. Chip design (digital IC design) is one specialty within EE, typically covered in the computer engineering and VLSI tracks. Most chip designers have an EE or CE degree, but not all EE graduates go into chip design.

How early can kids realistically start learning hardware?

Conceptually, from around age 10. Binary numbers, basic logic gates, and simple circuits are all accessible to curious 10–12-year-olds. The Nand2Tetris course is challenging but doable for a motivated 14–15-year-old. Electronics kits (Arduino, BBC micro:bit, Raspberry Pi) give younger kids hands-on experience with real hardware at an accessible level. The Engineering Mindset YouTube channel explains hardware concepts clearly for general audiences including teenagers.

Are chip design jobs affected by AI automation?

Some EDA tool tasks are being automated by AI, and synthesis and verification tools are becoming more capable. But the creative architectural work — deciding how a chip should be organized, what computational structures to use, what trade-offs to make between area, power, and performance — requires human judgment that current AI tools cannot replicate. The verification function is being partially augmented by formal methods and AI-assisted simulation, but the judgment about what scenarios to test and how to interpret results remains human work. Hardware engineering is notably less threatened by AI automation than many software disciplines.

What’s the difference between fabless chip design and working at a foundry?

Fabless companies (NVIDIA, AMD, Qualcomm, Apple’s silicon team) design chips but outsource manufacturing to foundries. Foundries (TSMC, GlobalFoundries, Samsung Semiconductor) manufacture chips designed by others. A chip design engineer at a fabless company works on architecture and RTL. A process engineer at a foundry works on the manufacturing process itself — how to reliably print 3nm features on silicon. The CHIPS Act is funding both types of operations. Both need engineers.

My kid loves Minecraft and building games. Is that relevant to hardware?

More than it looks. The spatial reasoning and systems thinking required to build complex structures in Minecraft — understanding how components interact, how to manage resources, how to optimize for constraints — is structurally similar to what physical design engineers do when laying out a chip. It’s not a direct skill transfer, but the underlying cognitive style overlaps. Kids who love building complex things in systematic ways often find hardware design intellectually natural.

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About the author

Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years of experience building consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.

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Sources

1. Semiconductor Industry Association & Oxford Economics. (2023). Chipping Away: Assessing and Addressing the Labor Market Gap Facing the US Semiconductor Industry. https://www.semiconductors.org/chipping-away-assessing-and-addressing-the-labor-market-gap-facing-the-us-semiconductor-industry/

2. U.S. Congress. (2022). CHIPS and Science Act of 2022. Public Law 117-167. https://www.congress.gov/bill/117th-congress/house-bill/4346

3. U.S. Bureau of Labor Statistics. (2024). Occupational Outlook Handbook: Electrical and Electronics Engineers. U.S. Department of Labor. https://www.bls.gov/ooh/architecture-and-engineering/electrical-and-electronics-engineers.htm

4. Nisan, N., & Schocken, S. (2021). The Elements of Computing Systems: Building a Modern Computer from First Principles (2nd ed.). MIT Press. https://www.nand2tetris.org

5. TSMC. (2024). Arizona Fab 21 Construction Update. TSMC Newsroom. https://investor.tsmc.com/english/encrypt/files/encrypt_file/reports/2024-01/fab21-update.pdf

6. Intel Corporation. (2024). Intel Foundry and Ohio Campus Investment. Intel Newsroom. https://www.intel.com/content/www/us/en/newsroom/news/intel-ohio-campus.html

7. RISC-V International. (2024). RISC-V Member Organizations. https://riscv.org/members/