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The Imbalance in Automobility Transformation

Legacy Auto’s Desperation vs. Tesla’s Dominance.

## Abstract.

In the accelerating automobility transformation, legacy automakers like Ford—grappling with $12 billion in EV losses since 2023, including $2.2 billion in H1 2025 and projections up to $5.5 billion for the year—desperately seek Tesla’s technological lifelines, yet Tesla has scant incentive to license its Full Self-Driving (FSD) system.

This report unveils the Darwinian imbalance: Tesla’s unassailable edge in 4.5 billion FSD miles (adding millions daily), propelling intelligent vehicles (IVs) to 10x safer than humans; poised to eliminate over 1 million annual global road deaths, 50 million injuries, and $4 trillion in economic damage annually.

Bolstered by vertical integration, unboxed manufacturing for sub-$30,000 Cybercabs at unprecedented rates, a 70,000+ connector Supercharger network, and robotaxi economics unlocking a $10 trillion market by 2029, Tesla dominates—hastening an 80% decline in private ownership by 2030 per Tony Seba, fostering shared fleets, urban digital twins, and integrated energy systems for sustainable communities worldwide.

Discover why legacy desperation fuels Tesla’s triumph in reshaping transportation.

[Get The Imbalance in Automobility Transformation White Paper](https://cdn.shopify.com/s/files/1/1295/2229/files/The_Imbala…756222023)

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Nvidia turns to silicon photonics to supercharge next-gen AI clusters

Earlier this year, the company confirmed that its next-generation rack-scale AI platforms will abandon pluggable optical modules in favor of co-packaged optics. At the Hot Chips conference, Nvidia shared new details about its upcoming photonic interconnect products – Quantum-X and Spectrum-X Photonics – scheduled for launch in 2026 for InfiniBand and Ethernet, respectively.

Can large language models figure out the real world? New metric measures AI’s predictive power

In the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying principles were understood.

Although they were inspired by Kepler’s laws, they went much further, and made it possible to apply the same formulas to everything from the trajectory of a cannon ball to the way the moon’s pull controls the tides on Earth—or how to launch a satellite from Earth to the surface of the moon or planets.

Today’s sophisticated have gotten very good at making the kind of specific predictions that resemble Kepler’s orbit predictions. But do they know why these predictions work, with the kind of deep understanding that comes from basic principles like Newton’s laws?

New laser technique boosts power by individually controlling light modes

From precision machining to advanced microscopy, the demand for higher-power, ultrafast lasers continues to grow. Traditionally, researchers have relied on single-mode fibers to build these lasers, but they face a fundamental physical limit on energy output. To break through this bottleneck, we have turned to multimode fibers, which can carry many light modes—essentially different shapes of light—at once, a technique known as spatiotemporal mode-locking (STML).

However, getting these different modes to work together in harmony has been a significant challenge. In our latest research, published in Optics Letters, we have developed a new technique that allows us to precisely and independently control each of these transverse modes, leading to a dramatic boost in and versatility.

The core problem we faced is known as intermodal dispersion. In a multimode fiber, different light modes travel at slightly different speeds. This velocity mismatch causes the laser pulses to spread out and separate in time and space, preventing the formation of stable, high-power pulses. Previous STML techniques typically used a method called spatial filtering to compensate for this dispersion, but this approach limits the number of modes that can be locked together, thereby capping the potential power enhancement.

A promising approach for the direct on-chip synthesis of boron nitride memristors

Two-dimensional (2D) materials, thin crystalline substances only a few atoms thick, have numerous advantageous properties compared to their three-dimensional (3D) bulk counterparts. Most notably, many of these materials allow electricity to flow through them more easily than bulk materials, have tunable bandgaps, are often also more flexible and better suited for fabricating small, compact devices.

Past studies have highlighted the promise of 2D materials for creating advanced systems, including devices that perform computations emulating the functioning of the brain (i.e., neuromorphic computing systems) and chips that can both process and store information (i.e., in-memory computing systems). One material that has been found to be particularly promising is (hBN), which is made up of boron and nitrogen atoms arranged in a honeycomb lattice resembling that of graphene.

This material is an excellent insulator, has a wide bandgap that makes it transparent to visible light, a good mechanical strength, and retains its performance at high temperatures. Past studies have demonstrated the potential of hBN for fabricating memristors, that can both store and process information, acting both as memories and as resistors (i.e., components that control the flow of electrical current in ).

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