Electronics Guide

Imagining Distant Futures

Speculation about the future of electronics has ranged from the insightful to the fanciful. In October 1945, Arthur C. Clarke published "Extra-Terrestrial Relays" in Wireless World, describing geostationary communication satellites two decades before Intelsat I flew. Other forecasts fared far worse. Mid-century predictions of flying cars and household robots by the year 2000 overestimated mechanical automation while missing the smartphone, the search engine, and the social network entirely. Long-term speculation about electronics must acknowledge this uneven record while still exploring possibilities that current science suggests may eventually become feasible.

The technologies examined here span timescales from decades to centuries, and some may never prove practical. Engaging with them still serves several purposes. Speculative visions set research agendas and attract talented people to technical fields. Reasoning about theoretical limits separates problems that are merely hard to engineer from those that confront fundamental physics. And considering the societal consequences of transformative technologies before they arrive gives institutions time to prepare. The sections that follow move outward from the device level to materials, computing paradigms, human integration, off-world manufacturing, and finally to the question of whether progress itself could accelerate beyond human comprehension.

Molecular-Scale Electronics

The ultimate miniaturization of electronics uses individual molecules as functional components. Laboratory work has demonstrated single-molecule junctions that rectify current, molecular wires a few nanometers long, and molecules that switch between distinguishable conductance states under an applied voltage or optical stimulus. These experiments establish that charge transport through a single molecule can be measured and modulated. They do not establish that such devices can be manufactured, interconnected, and operated by the billion, which is the actual barrier.

The field carries a cautionary history. Between 2000 and 2002, a series of prominent papers from Bell Laboratories reported molecular-scale and organic transistors with extraordinary properties. An investigation concluded in 2002 that the underlying data had been fabricated, and the papers were retracted. The episode set the field back and left a durable lesson: single-device measurements at the molecular scale are exceptionally difficult to perform cleanly, and extraordinary claims in this area warrant independent replication before they reshape a research agenda.

Self-assembly remains the most credible path toward molecular-scale manufacturing. Rather than positioning individual molecules mechanically, researchers design molecules that spontaneously organize into functional structures. DNA origami, introduced by Paul Rothemund in 2006, folds a long single strand of viral DNA into arbitrary two-dimensional and three-dimensional shapes using short "staple" strands, producing nanoscale scaffolds with binding sites addressable to within a few nanometers. Block copolymers phase-separate into regular lamellae and cylinders at pitches below what optical lithography reaches directly, and directed self-assembly already sees limited use in semiconductor patterning. These approaches recruit chemistry rather than fighting it.

The theoretical case for molecular electronics is strong. A molecule is roughly one to three nanometers across, an order of magnitude smaller than the smallest features in a production transistor. Switching a molecular state can in principle cost far less energy than charging the gate capacitance of a field-effect transistor. Against this stand three stubborn problems. Molecular devices vary enormously from copy to copy, because a single misplaced atom changes the conductance; circuit design assumes matched devices, and molecular ensembles do not supply them. Thermal energy at room temperature is about 26 millielectronvolts, comparable to the energy barriers separating some molecular states, so switching becomes probabilistic rather than deterministic. And connecting a one-nanometer device to a macroscopic world of pads and probes introduces contact resistances that can dominate the molecule's own behavior.

Practical molecular electronics will most likely arrive first in niches where these weaknesses matter less. Molecular sensors that report a binding event through a conductance change already detect single molecules in laboratory settings, and nanopore sequencing, which reads DNA by measuring ionic current through a protein pore, is a commercial descendant of the same idea. Molecular and DNA-based archival memory tolerates slow access in exchange for density. Hybrid systems, in which molecular elements sit atop a conventional silicon substrate that handles addressing, amplification, and error correction, are more plausible than an all-molecular computer. The path from demonstration to product plausibly spans decades, but the underlying physics does not forbid it.

Post-Silicon Computing

Silicon has dominated semiconductor electronics for roughly seventy years, and physical limits now suggest that alternative materials and architectures will eventually be necessary. Two distinct slowdowns are often conflated. Dennard scaling, the property that shrinking a transistor also reduced its power density, broke down around 2005, ending the era of automatic clock-frequency gains. Geometric scaling continues, but node names such as "3 nm" and "2 nm" are marketing labels rather than physical dimensions; the transistors themselves have moved from planar devices to FinFETs to gate-all-around nanosheets, each transition buying density through structure rather than through simple shrinkage. Leakage current, device-to-device variability, interconnect resistance, and heat removal now set the practical ceiling.

Alternative channel materials offer the nearest-term paths beyond silicon. Germanium and III-V compounds such as gallium arsenide and indium gallium arsenide provide higher carrier mobility, allowing faster switching or lower supply voltages, and III-V devices already dominate radio-frequency front ends and optoelectronics. Two-dimensional materials, including molybdenum disulfide and other transition metal dichalcogenides, permit channels a single atomic layer thick with good electrostatic control. Graphene, despite outstanding mobility, lacks an intrinsic bandgap, which makes it poor at turning off and therefore poorly suited to digital logic without substantial modification. Carbon nanotubes remain compelling: a 16-bit microprocessor built from roughly fourteen thousand carbon nanotube transistors was reported in 2019, demonstrating that a full instruction set can run on the technology, though at clock rates and yields far below silicon.

More radical departures may eventually prove necessary. Quantum computing exploits superposition and entanglement to attack specific problems, notably factoring, discrete logarithms, and the simulation of quantum systems themselves; it is not a general speedup for ordinary computation. Neuromorphic computing mimics the brain's co-location of memory and processing and its event-driven, spike-based signaling to achieve high energy efficiency on pattern recognition workloads. Reversible computing avoids erasing information and so evades Landauer's bound, which sets a minimum dissipation of about three zeptojoules for each irreversibly erased bit at room temperature; practical CMOS switching energies remain several orders of magnitude above that floor, so the bound is a distant target rather than an immediate constraint. Analog and in-memory computing, largely abandoned when digital systems proved easier to design and reproduce, are being revisited for neural network inference, where limited precision is acceptable and the energy cost of moving data dominates.

The transition beyond silicon will be gradual and heterogeneous. Applications weigh performance, energy, cost, and reliability differently, and no successor is likely to dominate as comprehensively as silicon has. The plausible future is a portfolio: evolved silicon CMOS for general-purpose control, with specialized accelerators built from photonic, quantum, neuromorphic, or analog elements attached where their advantages are decisive. Advanced packaging, chiplets, and three-dimensional stacking already make such heterogeneity practical, and they may matter more to system performance over the next two decades than any single new switch.

Room-Temperature Superconductors

Superconductors carry direct current with no measurable resistance and expel magnetic flux below a critical temperature, field, and current density. They enable lossless direct-current transmission, extraordinarily strong electromagnets, and switching elements that dissipate very little energy. Every superconductor in practical use still requires cryogenic cooling. Niobium-titanium, the workhorse of magnetic resonance imaging magnets, superconducts below about 9 kelvin and needs liquid helium at 4.2 kelvin, roughly minus 269 degrees Celsius. Magnesium diboride, discovered to superconduct in 2001, works below 39 kelvin. Rare-earth barium copper oxide tapes operate in liquid nitrogen at 77 kelvin, about minus 196 degrees Celsius, and now underpin the high-field magnets built for compact fusion experiments.

The 1986 discovery of superconductivity in a lanthanum-barium-copper-oxide ceramic by Georg Bednorz and Alex Müller, recognized with the Nobel Prize in Physics the following year, transformed the field. Their material superconducted near 35 kelvin, well above the roughly 23 kelvin ceiling of conventional metallic superconductors. Within a year, yttrium-barium-copper-oxide pushed the transition to about 93 kelvin, above the boiling point of liquid nitrogen. That threshold mattered enormously, because liquid nitrogen is cheap, abundant, and easy to handle. Progress then slowed. The mercury-based cuprate Hg-1223 reached about 133 kelvin in 1993, and that figure stood as the ambient-pressure record for more than three decades. In March 2026, Ching-Wu Chu and Liangzi Deng at the University of Houston reported in the Proceedings of the National Academy of Sciences that pressure quenching, in which a sample is compressed, cooled, and then rapidly decompressed to lock in a metastable state, raised the ambient-pressure transition of Hg-1223 to about 151 kelvin, or roughly minus 122 degrees Celsius. Even that record leaves nearly 150 kelvin between the best confirmed material and everyday room temperature.

Hydrogen-rich compounds superconduct at far higher temperatures but only under extreme compression. Lanthanum decahydride shows a transition near 250 kelvin at pressures around 150 to 170 gigapascals, more than a million times atmospheric pressure, which confines samples to microscopic volumes inside diamond anvil cells and rules out any engineering use. Ambient-pressure claims have repeatedly collapsed. A 2023 report of superconductivity at 21 degrees Celsius in nitrogen-doped lutetium hydride was retracted by Nature in November of that year after most of its own authors requested withdrawal and the journal found unresolved concerns about the resistance data. The copper-doped lead apatite known as LK-99, announced the same year, drew intense replication effort, and the consensus conclusion was that it is not a superconductor at all. Whether an ambient-pressure, room-temperature superconductor is even possible remains an open theoretical question.

If such a material were found and could be fabricated into wire, the consequences for electronics would be substantial. Superconducting interconnects would remove the resistive losses that now dominate on-chip wiring delay and power at advanced nodes. Single-flux-quantum logic built on Josephson junctions switches in picoseconds and dissipates orders of magnitude less energy per operation than CMOS, but it has never escaped the cost and complexity of cryogenic plant. Superconducting quantum interference devices already provide the most sensitive magnetometers available, used in magnetoencephalography and materials characterization; removing their cooling requirement would make them far more widely deployable. Superconducting qubits, the leading modality in several quantum computing programs, currently demand dilution refrigerators operating near 10 millikelvin, and a higher-temperature superconductor would not by itself relax that constraint, because qubit coherence depends on thermal noise rather than on the wiring alone.

Beyond electronics, the effects would reach power systems and transportation, though with important qualifications. Transmission and distribution losses run near 5 percent of generated electricity in the United States, and superconducting cables could reduce that, but the cryogenic refrigeration that current cables require consumes power itself, so the net saving is smaller than the resistance figure suggests. Superconducting magnetic energy storage delivers very high power for seconds and suits power-quality and frequency-regulation duty rather than the multi-hour bulk storage that intermittent renewables need. Strong, compact magnets would benefit magnetic levitation transport, medical imaging, particle accelerators, and fusion confinement. The discovery of a practical room-temperature superconductor would rank among the most consequential results in the history of materials science, and no credible method exists for predicting when or whether it will occur.

Biological Computing Systems

Living systems process information with molecular machinery of remarkable efficiency. The human brain consumes roughly 20 watts, about the power of a dim incandescent bulb, yet it performs perceptual and cognitive work that data-center clusters match only partially and at many orders of magnitude greater energy cost. Biological systems also self-assemble, repair damage, and adapt. These properties motivate research into computing that borrows biological components, biological principles, or both.

DNA computing exploits complementary base pairing to evaluate many candidate solutions in parallel. Leonard Adleman's 1994 demonstration encoded a seven-city Hamiltonian path problem in DNA strands and recovered the answer through hybridization and selection, establishing that computation need not be electronic. The approach is slow, error-prone, and consumes its substrate, so it has not scaled to competitive general computation. DNA has proved far more compelling as a storage medium: laboratory work has projected densities on the order of hundreds of petabytes per gram, and synthetic DNA is stable for centuries under mild conditions, which suits cold archival storage where write and read latency of hours is tolerable. Cost of synthesis remains the binding constraint.

Living cells can also be engineered to compute. Synthetic biology has produced genetic circuits implementing logic gates, toggle switches, counters, and analog signal processing inside bacteria and mammalian cells. Such circuits are slow, measured in minutes to hours, and noisy, but they operate where electronics cannot: inside tissue, in a drop of water, distributed across a population. Engineered cells that detect a disease biomarker and release a therapeutic in response, or that report environmental contamination by fluorescing, are active areas of clinical and field research. Hybrid bioelectronic systems, in which living cells supply sensing and specificity while conventional circuits supply readout and control, are the nearest-term practical form.

More speculatively, biological substrates might be cultivated specifically for computation. Brain organoids grown from stem cells develop spontaneous electrical activity and have been interfaced with microelectrode arrays, prompting a research program sometimes called organoid intelligence. Results so far are preliminary, and the ethical questions raised by cultured neural tissue that produces structured activity are serious and unsettled. Whether engineered biological systems will ever rival electronics for general computation is genuinely uncertain; what seems more likely is a durable division of labor, with biology contributing sensing, synthesis, and self-repair, and electronics contributing speed, precision, and programmability.

Brain-Computer Integration

The interface between electronics and the nervous system has moved from speculation to clinical practice. Cochlear implants have restored useful hearing to hundreds of thousands of people. Deep brain stimulators treat the motor symptoms of Parkinson's disease, essential tremor, and dystonia. Investigational implanted systems have allowed people with tetraplegia to control cursors, robotic arms, and speech synthesizers by intention alone. These achievements are early steps toward a far more intimate integration between electronics and cognition, and the gap between where the field stands and where speculation reaches is wide.

Bandwidth is the central limitation. The Utah array, the most widely used long-term human recording device, carries 96 to 128 penetrating electrodes; multi-array systems reach into the hundreds of channels. Neuralink's N1 implant distributes 1,024 electrodes across 64 flexible threads and transmits wirelessly, and other programs pursue endovascular electrode arrays delivered through blood vessels to avoid open craniotomy. Even a thousand channels sample an infinitesimal fraction of roughly 86 billion neurons, and each channel typically resolves activity from only a handful of nearby cells. Signal quality degrades over months to years as glial scarring encapsulates the electrodes and as micromotion damages tissue. Non-invasive electroencephalography avoids surgery entirely but averages over millions of neurons through skull and scalp, yielding information rates measured in bits per second.

Several research directions attack these limits. Neural dust proposes thousands of microscopic motes powered and read out by ultrasonic backscatter, distributing sensing without a large implanted array or percutaneous wiring. Optogenetics uses light to excite or silence genetically modified neurons with cell-type specificity and millisecond timing, offering a route to writing as well as reading neural activity, though the requirement for genetic modification complicates human application. Flexible mesh and polymer electronics, sometimes called neural lace, aim to match the mechanical compliance of brain tissue so that the immune response that degrades rigid implants is reduced. Progress on all three depends as much on materials science and surgical technique as on circuit design.

The long-term possibilities provoke both enthusiasm and unease. Direct neural access to information could change how expertise is acquired. Shared sensory or emotional channels might enable forms of communication that language handles poorly. Restoring sight, movement, and speech to people who have lost them is a nearer and less contested goal, and it is where the clinical evidence is accumulating. The same capabilities, however, raise questions about mental privacy, consent, identity, and the security of a device with write access to a nervous system. Neural data is unusually revealing, and it is not clearly covered by existing privacy law in most jurisdictions. Advanced brain-computer interfaces will require governance frameworks developed alongside the technology, not after it.

Consciousness Uploading Concepts

Among the most speculative ideas in this field is mind uploading, also called whole brain emulation: scanning a brain at sufficient resolution to capture whatever structure determines its function, then running that structure on a computational substrate. No scientific consensus holds that this is possible even in principle. Examining the concept nonetheless clarifies how far current capability sits from the requirement, and what would have to be true for the idea to be coherent.

Connectomics gives concrete numbers for the scanning problem. In October 2024, the FlyWire consortium published the first complete connectome of an adult fruit fly brain: about 139,000 neurons joined by more than 50 million synapses, reconstructed from electron microscopy with machine learning and roughly thirty person-years of human proofreading. In 2025, the MICrONS project released a reconstruction of one cubic millimeter of mouse visual cortex containing on the order of 200,000 cells and more than 523 million detected synapses, paired with functional recordings from the same tissue. A comparable cubic-millimeter fragment of human cerebral cortex, imaged by serial-section electron microscopy, produced about 1.4 petabytes of image data and well over a hundred million synapses. An adult human brain is on the order of a million cubic millimeters. Simple extrapolation from the human fragment puts a whole-brain data set at the zettabyte scale before any analysis begins, and that is the easy part of the estimate.

The harder question is what must be captured. Connectivity alone is almost certainly insufficient. Synaptic strength depends on receptor counts and subtypes, phosphorylation states, and vesicle pools. Neuromodulators such as dopamine, serotonin, and acetylcholine act diffusely and reconfigure the effective function of a fixed wiring diagram. Glial cells participate in signaling. Gene expression changes on timescales relevant to learning. Present electron microscopy destroys the tissue and captures a single frozen instant, and no technique reads molecular state and connectivity together across a whole brain. Simulating whatever is captured is a further problem: even detailed simulations of small cortical volumes consume supercomputer time far out of proportion to the biological tissue involved.

Beyond feasibility lie philosophical questions that no engineering advance resolves. Would a running emulation be conscious, or would it behave as though conscious without any inner experience? Would it be the same person as the original, or a distinct entity with inherited memories? If copies can be made, what obligations attach to each? These questions engage unresolved debates about consciousness and personal identity, and reasonable people disagree about whether they even have determinate answers.

Whether or not full uploading is achievable, partial approaches are already yielding results. Detailed circuit models advance neuroscience and drug discovery without any claim to consciousness. Neural prosthetics that substitute for damaged function, such as hippocampal stimulation intended to support memory encoding, pursue replacement of specific circuits rather than the whole. Connectomics itself was named a method of the year by Nature Methods for 2025, reflecting how quickly the underlying imaging and reconstruction pipelines are maturing. The near-term value of this work does not depend on the far-term speculation that popularized it.

Space-Based Manufacturing

Microgravity offers process conditions unavailable on Earth. Without buoyancy-driven convection or sedimentation, melts and solutions mix by diffusion alone, which can yield more uniform crystals and glasses. Containerless processing using acoustic or electromagnetic levitation eliminates crucible contamination and allows deeper undercooling of melts. The residual pressure in the wake of an orbiting vehicle is lower than any practical terrestrial vacuum chamber achieves. These advantages have attracted persistent interest in orbital manufacturing despite the cost of reaching orbit.

Experiments aboard the Space Shuttle and the International Space Station tested the premise across four decades with mixed but instructive results. Protein crystals grown in microgravity have in many cases diffracted better than terrestrial controls, aiding structure determination for drug design. Some semiconductor crystals grown by float-zone and vapor transport methods showed improved compositional uniformity. Fluoride glass fiber, commonly ZBLAN, drew particular attention: it offers theoretical attenuation far below silica in the mid-infrared, but crystallization during drawing on Earth has always spoiled that potential, and convection is implicated in the defect formation. Early flight experiments were suggestive rather than conclusive, and reproducing the effect proved harder than expected.

Falling launch costs are changing the arithmetic. Reusable boosters have brought the price of delivering mass to low Earth orbit more than an order of magnitude below Space Shuttle-era figures, and further reductions are plausible if fully reusable heavy vehicles enter routine service. Commercial in-orbit production has followed. In 2024, a commercial furnace aboard the International Space Station drew more than ten kilometers of ZBLAN fiber in microgravity, the first production run of its kind at that scale. Pharmaceutical crystallization, semiconductor wafer growth, and specialty optics are the other candidates most often proposed, because they share the necessary profile: high value per kilogram, low mass, and a quality advantage that terrestrial process improvement cannot match. Whether any of them clears the economic bar durably remains unproven, and the retirement of the International Space Station near the end of this decade puts the near-term platform question in play.

Longer-term visions extend to manufacturing from space-derived resources. Lunar regolith is rich in silicon, oxygen, aluminum, and iron, and proposals exist for producing solar cells and structural material in place rather than launching them. Asteroid mining could supply metals and volatiles without a gravity well to climb. Self-replicating or largely autonomous factories could in principle expand capacity using local material and sunlight. Each of these requires advances in autonomy, resource extraction, and closed-loop process control that no one has demonstrated at scale, and the honest timescale is measured in decades at best. The direction of travel, however, is clear enough: electronics manufacturing need not remain confined to terrestrial resources and terrestrial physics.

Technological Singularity Discussions

The technological singularity is a hypothetical point at which technological progress becomes so rapid that its consequences cannot be predicted from the present vantage. Vernor Vinge gave the modern formulation in a 1993 essay, and Ray Kurzweil popularized it further in The Singularity Is Near in 2005. The usual mechanism is artificial intelligence reaching and then exceeding human capability, after which the system improves itself in an accelerating cycle that produces intelligence far beyond human comprehension.

Proponents point to historical trends. Transformative technologies have appeared at shortening intervals. Transistor counts on leading-edge chips grew exponentially for six decades. Machine learning systems have surpassed expert human performance on tasks, from board games to protein structure prediction, that were confidently described as decades away shortly before they fell. Extrapolating these curves suggests eventual artificial general intelligence followed by rapid advance to superintelligence.

Critics raise substantial objections. Exponential trends end; Dennard scaling already did, and cost per transistor has stopped falling reliably at the leading edge. Benchmark progress may not measure the general capability it is taken to measure. Recursive self-improvement could encounter diminishing returns from data, energy, physical experimentation, or the intrinsic difficulty of the remaining problems, rather than compounding. The concept is also partly self-undermining: if a singularity is by definition unpredictable, detailed forecasts about what follows it are hard to justify. Repeated predictions of imminent transformation have slipped, which is evidence of systematic optimism about near-term rates even when long-term direction is right.

Whether or not a singularity occurs, the discussion surfaces issues that matter for electronics specifically. Machine learning already designs circuit layouts, searches materials, and screens candidate molecules, so the tools are becoming participants in their own development. Training and inference are now significant drivers of data-center construction and electricity demand, which feeds back into power electronics, cooling, and grid planning. Questions of control, alignment, evaluation, and governance for capable AI systems require attention on their merits, independent of any view about singularities. Engaging with the idea skeptically is still useful, because it forces explicit reasoning about long-run trajectories that otherwise go unexamined.

Assessing Speculative Claims

Readers encounter speculative technology claims constantly, and a few questions separate the promising from the empty. First, does the claim confront a fundamental limit or an engineering difficulty? Faster-than-light signaling is forbidden; a room-temperature superconductor is merely undiscovered. The two deserve very different priors. Second, at what scale has the effect been demonstrated? A working single device is far from a working circuit, which is far from a manufacturable product; the history of molecular electronics and carbon nanotube logic shows how long that ladder takes to climb.

Third, has the result been independently replicated? The retracted lutetium hydride paper and the LK-99 episode both resolved within months once other laboratories tried, and replication remains the field's most reliable filter. Fourth, what is the economics? Space manufacturing works technically and has for decades; what changed recently was launch cost, not physics. Fifth, what does the claim assume about everything else? A technology that requires a new material, a new fabrication process, and a new architecture simultaneously faces the product of three independent risks, not their sum. Applying these questions consistently will not identify which speculative technologies succeed, but it reliably identifies which arguments are weak.

Implications and Considerations

The technologies examined here share several features. Each extends a current research direction rather than inventing a new physics. Each faces obstacles whose resolution timelines are genuinely unknown. Each would, if realized, change more than electronics. And each raises ethical, social, and governance questions well before it becomes practical, which is precisely when such questions are cheapest to address.

Some of those questions are already concrete. Neural interfaces generate data that reveals mental content, and most privacy regimes were not written with that in mind. Cognitive enhancement, if it works, will be distributed by ability to pay unless policy intervenes. Cultured neural tissue that shows organized activity sits uncomfortably in existing research ethics. Autonomous manufacturing in orbit intersects with space law and debris management. None of these require a technological breakthrough to become pressing; several are pressing now, in early form.

Preparing for uncertain futures favors flexibility over commitment to any single vision. Research portfolios should span multiple approaches, because breakthroughs arrive from unexpected directions and the winning technology is rarely the one that led early. Education should build foundations, such as physics, mathematics, and systems thinking, that outlast any particular device technology. Governance frameworks should be written to accommodate technologies not yet invented, which usually means regulating capabilities and harms rather than named devices. Institutions and individuals that hold possibilities in view while acknowledging uncertainty navigate technological transitions better than those who either dismiss long-term thinking or commit too firmly to one forecast.

Conclusion

Long-term speculation about electronics serves real purposes despite its poor record on specifics. Exploring possibilities widens the sense of what is achievable and directs research toward ambitious goals. Reasoning about theoretical limits distinguishes engineering problems from prohibitions. Considering consequences before arrival enables better preparation. And practicing disciplined speculation builds the intellectual flexibility that an unpredictable future demands.

The subjects covered here span a wide range of confidence and timescale. Post-silicon materials and neuromorphic architectures will almost certainly see meaningful deployment within decades, because commercial work is already underway. Room-temperature superconductivity and molecular-scale logic are plausible but undated, contingent on discoveries that cannot be scheduled. Consciousness uploading may prove impossible in any meaningful sense. Most of the rest falls between, dependent on developments no one can forecast with confidence. What appears reliable is direction rather than destination: electronics will continue to grow more capable, more deeply integrated with biology, more varied in its physical substrates, and less confined to a single planet, continuing a trajectory that began when the first vacuum tubes flickered to life more than a century ago.

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