PV

[Siren @sssirxn]

infinite eclipse

[Ming @tslaming]

READ IT TO BELIEVE IT 🚨 TESLA TRANSPORT PROTOCOL: THE GAME CHANGER THAT BREAKS THE TCP/IP SPEED LIMIT ⚡️

While the broader internet relies on TCP/IP, the universal standard that governs global data traffic, Tesla has architected a bespoke solution to meet the unique demands of AI training.

With the publication of patent WO 2024/039793 A1, and underscored by the recent June 2025 continuation filing EP 4573730 A1, we gain insight into the custom networking stack driving Tesla's autonomy ambitions.

The patent details the Tesla Transport Protocol (TTP). This is a hardware-native approach that bypasses the operating system entirely.

By eliminating the software abstraction layer, TTP transforms a distributed network of thousands of GPU tiles into a cohesive, low-latency compute fabric.

This architecture unlocks the single-digit microsecond latency required to train Full Self-Driving models at speeds that conventional networking stacks simply cannot match.

To understand why this invention is necessary, we must first look at the invisible wall hitting current supercomputers.

⚖️ The engineering bottleneck: Software-defined latency

In High-Performance Computing (HPC), the throughput of the entire cluster is often limited not by raw compute power, but by interconnect latency. For decades, the industry has defaulted to TCP/IP (Transmission Control Protocol/Internet Protocol).

To the non-technical observer, TCP/IP acts as the rigorous "traffic rules" of the digital world. Designed for reliability above all else, it ensures data integrity by treating every packet like a registered letter. The system must open, inspect, and acknowledge receipt before processing the next.

While this reliability is critical for the public internet, it introduces unacceptable overhead in a supercomputing environment. The protocol is software-heavy. It forces the Central Processing Unit (CPU) to constantly interrupt computational tasks to manage network traffic.

This introduces latency, a digital reaction delay. While a few milliseconds is negligible for web browsing, it is an eternity for an AI training cluster processing billions of parameters per second.

🏗️ The architectural solution: Tesla Transport Protocol (TTP)

To shatter this bottleneck, Tesla realized they couldn't just optimize the software. They had to delete it. They developed a proprietary flow control system: Tesla Transport Protocol.

The core architectural shift involves offloading network management from the OS kernel directly to silicon.

To understand why this matters, think of the OS kernel as a busy office manager who has to approve every single document that comes in or out of a company. Even if the manager is fast, they are also juggling a thousand other tasks, such as scheduling meetings, managing payroll, and answering phones.

In a supercomputer, this "manager" (the software) becomes overwhelmed by the billions of data packets arriving every second. This causes a traffic jam.

By implementing the Transport Layer directly into the Network Interface Card (NIC), Tesla effectively fires the manager. They build a pneumatic tube system that shoots documents directly to the recipient's desk. The Transport Layer is the logic responsible for ensuring data actually arrives at the right destination.

This "hardware-offload" approach allows the system to manage connection lifecycles and data transfer autonomously. It effectively replaces the stop-and-go nature of software interrupt handling with the continuous, high-speed throughput of dedicated circuitry. Instead of a manager pausing to sign for every package, the packages flow on a conveyor belt that never stops moving.

🧩 Integration: The "Trojan Horse" header strategy

However, creating a new protocol usually creates a new problem: incompatibility with existing cables and switches. Tesla avoided this with a clever disguise. The patent describes a packet header structure that maintains compatibility with existing hardware.

To visualize this, imagine sending a top-secret document through the regular postal service using a specialized "Trojan Horse" envelope. Tesla wraps their data in a standard outer shell where the first 16 bytes mirror a standard Layer 2 Ethernet header.

This allows standard networking equipment, such as off-the-shelf Ethernet switches from Cisco or Arista, to read the "address" and route the packet without realizing it carries anything unusual. This saves Tesla from building expensive, custom-made routing hardware.

Yet, stamped on this standard envelope is a specific code called the EtherType (0x0AC6). This acts like a subtle "VIP" stamp. When a regular computer receives mail, it sends it to the mailroom (the OS software) to be slowly sorted.

But when a Tesla NIC sees this specific stamp, it pulls the packet off the line immediately. It bypasses the mailroom entirely and sends the data directly to the high-speed sorting machine.

This strategy allows Tesla to tunnel a Formula 1-grade protocol through standard, affordable network pipes. It combines the low cost of commodity hardware with the high performance of a supercomputer.

⏱️ Throughput: The 4-stage hardware pipeline

Once the data bypasses the standard stack, the focus shifts to raw processing speed. The patent’s claim of single-digit microsecond latency is achieved through a deterministic 4-stage hardware pipeline.

To understand why this is revolutionary, compare a standard software process to a single chef in a kitchen. The chef grabs an order, chops vegetables, and cooks the meat sequentially. If a phone rings (an interrupt), they stop working to answer it, creating unpredictable delays.

Tesla’s hardware pipeline functions more like a bucket brigade or a factory assembly line. Every single time the chip’s internal clock ticks, work is passed instantly to the next station.

In the first stage (Q0), the logic acts as traffic control, instantly picking the single most urgent stream to process. It immediately passes this to the second stage (Q1), which pulls the file on that connection, reading the status tag to verify the link is healthy.

The third stage (Q2) acts as the brain, executing decision logic in a nanosecond to determine if a packet needs a replay or is safe to send. Finally, the fourth stage (Q3) commits the move by updating the internal memory pointers, readying the system for the next cycle.

This pipeline creates a continuous "conveyor belt" of packet processing. It eliminates jitter, the tiny, unpredictable stutters that happen when software gets distracted. In this system, data moves with the relentless, metronomic precision of a Swiss watch.

🤖 State Management: The hardware Finite State Machine

But raw speed is only half the equation. The system also needs to manage the lifecycle of these high-speed connections without clogging the system. Efficient connection management is handled by a hardware Finite State Machine (FSM).

To understand this, think of a logic circuit like a rigid turnstile that can only be in one specific position at a time, such as locked, unlocking, or open, based on strict physical triggers. There is no ambiguity and no thinking involved, just immediate reaction to input.

Crucially, this system solves one of the biggest inefficiencies in standard networking known as the "zombie connection" problem. In the traditional TCP world, closing a connection is like a painfully long goodbye at a doorway.

Even after both sides agree to disconnect, the system enters a TIME_WAIT state. It keeps the memory slot reserved for several minutes, just in case a lost packet shows up late. In a supercomputer running millions of connections, these "ghosts" clog up valuable memory resources.

TTP eliminates this lingering entirely. It introduces a ruthless "Intermediate Close" state. The moment an acknowledgement of closure is received, the hardware instantly kills the link. It doesn't wait for stragglers.

It effectively flips the "Vacant" sign immediately, allowing the system to instantly recycle that memory slot for a new connection. This ensures that the expensive high-speed memory is always working, never waiting.

🔄 Error Correction: The "lossy" replay mechanism

While efficient connection management keeps the highway clear, the system must also decide how to handle the inevitable accidents: lost data. Most internet protocols operate on a "lossless" philosophy, meaning they are obsessed with perfection.

If a single packet of data is dropped, the entire operation grinds to a halt until that packet is recovered. While this ensures accuracy, it is a massive drag on speed. TTP operates on a "lossy" philosophy, acknowledging that in a hyperscale environment processing exabytes of data, dropping a few packets is inevitable and shouldn't stop the show.

Think of the difference between downloading a critical file versus streaming a live video. When downloading a file, you need every single bit perfect, so you wait. When streaming video, if a few pixels are missing in one frame, the video keeps playing because speed is more important than absolute perfection in that microsecond.

Tesla applies a similar logic to supercomputing but adds a high-speed safety net to catch the critical pieces.

Rather than stalling the entire pipeline to ensure perfect order, the protocol keeps blasting data forward. If a receiving node detects a gap, such as a missing page in a book, it sends a Negative Acknowledgement (NACK) back to the sender. This signal essentially says, "I missed page 45, keep going, but send me a copy of 45 when you can."

To fulfill this request instantly, the transmitting hardware maintains a linked-list in its high-speed memory. This acts like a library index card system, allowing the hardware to instantly locate the exact memory address of the missing packet.

It then "replays" just that specific chunk of data without ever stopping the main transmission stream. This allows Tesla to maintain blistering speeds while still patching up errors on the fly.

🧠 Congestion Control: Physical backpressure

Beyond handling errors, the system faces an even more fundamental physical challenge: preventing data floods. Flow control is the essential mechanism that prevents a fast sender from flooding a slow receiver and crashing the system.

In standard networking, this acts like a complex bureaucracy where computers constantly negotiate "window sizes," trying to mathematically predict how much data they can handle next. TTP replaces this predictive negotiation with a simple, immutable mechanic: Physical Backpressure.

To visualize the difference, imagine a warehouse loading dock that has exactly 10 parking bays. The traditional TCP/IP approach operates like a warehouse manager spending all day on the phone with trucking companies. They are constantly estimating unloading speeds and scheduling arrivals to prevent the lot from overflowing. This process is administrative, slow, and prone to miscalculation.

Tesla, by contrast, essentially installs a mechanical boom gate at the entrance. If all 10 bays are full, the gate physically locks. There is no phone call, no math, and no prediction involved. The system operates on a rigorous "one-in, one-out" basis. The moment a truck leaves a bay (an acknowledgement is received), the gate automatically unlocks to admit exactly one new vehicle.

This system relies on the on-chip SRAM (Static Random Access Memory), which is limited in size but incredibly fast. By binding transmission speeds directly to the physical availability of empty slots in memory, Tesla prevents data jams instantly and mechanically. This ensures that zero processor cycles are wasted on bureaucracy.

⏲️ Synchronization: The global hardware link timer

With traffic flowing smoothly, the final challenge lies in policing the grid for idle connections without wasting energy. Monitoring timeouts—the limit on how long a computer waits for a response before giving up—for thousands of connections usually requires thousands of software timers. Managing this many timers is a massive drain on processing power.

Tesla addresses this with a global hardware link timer that decouples timekeeping from individual connections. To visualize this, imagine a parking enforcement officer monitoring a long street of parked cars.

The traditional method would be akin to hiring a separate officer with a stopwatch for every single car, staring at it to see if it stays too long. This is incredibly expensive and wasteful. Tesla’s solution functions like the "chalking tires" method.

The system utilizes a round-robin scanner, which acts as a single digital officer walking down the line of cars in a continuous loop. It employs a "Timer Bit" strategy, which acts like the chalk mark on a tire.

As the scanner passes a connection, it places a digital mark by setting the bit to 1. If the connection is active and sending data, it essentially "drives away" and returns, rubbing off the chalk mark by clearing the bit back to 0.

When the scanner returns to that spot on its next loop, it checks the tire. If the chalk mark is still there, it knows the car hasn't moved for the entire duration of the loop. The connection is declared "timed out" and closed.

This approach creates O(1) complexity, a computer science term meaning the effort required doesn't explode as you add more work. Whether there are 10 cars or 10,000, the officer just keeps walking the same efficient loop, allowing a single physical circuit to police thousands of links with negligible processing overhead.

🆚 Architectural Comparison: TCP/IP vs. TTP

When we view these mechanisms together, the fundamental difference between the old world and the new becomes stark. The divergence between TCP/IP and TTP represents a shift from a "one-size-fits-all" public utility to a highly specialized racing machine.

TCP/IP was architected for the internet, functioning much like a chaotic public highway system. It is designed to handle everything from mopeds to semi-trucks, but this versatility comes at a steep price. It requires traffic lights, stop signs, and police officers to manage the flow.

Every time a packet arrives, the CPU must pause its work to act as a traffic cop. It has to check the "driver's license" and direct the vehicle.

Conversely, TTP is purpose-built for the controlled environment of a data center, functioning like a private high-speed rail line. It treats network packets not as mail to be sorted, but as raw electrical signals to be processed by dedicated circuitry. There are no traffic lights, no other cars, and the tracks are welded together for a single purpose: speed.

This structural difference exposes a massive efficiency gap caused by "context switching." In a TCP environment, every time the CPU has to handle network traffic, it must pause its main calculation work, save its progress, switch to "traffic cop" mode, and then switch back.

Imagine a mathematician trying to solve a complex equation but being interrupted by a phone call every few seconds. The time spent putting down the pencil, answering the phone, and trying to remember where they left off represents this context switching tax. It introduces millisecond-level delays that accumulate into significant wasted time.

TTP erases this waiting time entirely. By enforcing flow control through physical memory constraints and utilizing hardware state machines, it removes the "mathematician's phone" from the equation.

This allows the compute cores to focus 100% on the math while the data flows automatically in the background. It achieves latencies effectively limited only by the speed of light through the fiber.

🚀 The future is bright: AI5 chip and the revival of Dojo 3

This patent is not just a legacy document for the original D1 chip. It is the strategic unlock for Tesla's renewed 2026 roadmap. Following the completion of the AI5 processor design, Tesla has officially restarted work on the massive Dojo 3 supercomputer. TTP is the invisible nervous system that makes this scaling possible.

While the original Dojo proved the concept, Dojo 3 aims for a scale that is orders of magnitude larger. It requires connecting millions of AI5 cores to function as a single training brain. TTP allows this massive distributed system to operate without the crushing "chatter" of standard networking protocols.

The immediate impact is on the rollout of Unsupervised FSD. While existing cars run on AI4, training the next-generation "end-to-end" neural networks requires crunching exabytes of video data. TTP enables Dojo 3 to ingest this fleet data at wire speed. This allows engineers to solve the rare "long tail" edge cases that still prevent full autonomy.

Beyond cars, this architecture is the backbone for Optimus. The humanoid robot requires a fusion of vision, language, and complex physics simulations. This multimodal training demands even higher bandwidth than driving. TTP ensures that the Dojo clusters can handle this dense data flow without bottlenecks.

Finally, this technology secures Tesla's strategic independence. By controlling the entire stack, from the TTP transport layer to the AI5 silicon, Tesla decouples itself from the supply chain constraints of third-party GPU vendors like NVIDIA. This allows them to scale their compute capacity on their own terms, potentially aiming for future frontiers like space-based AI inference clusters.

[Ming @tslaming]

BREAKING 🚨 TESLA HAS PATENTED A "MATHEMATICAL CHEAT CODE" THAT FORCES CHEAP 8-BIT CHIPS TO RUN ELITE 32-BIT AI MODELS AND REWRITES THE RULES OF SILICON 🐳

How does a Tesla remember a stop sign it hasn’t seen for 30 seconds, or a humanoid robot maintain perfect balance while carrying a heavy, shifting box?

It comes down to Rotary Positional Encoding (RoPE)—the "GPS of the mind" that allows AI to understand its place in space and time by assigning a unique rotational angle to every piece of data.

Usually, this math is a hardware killer. To keep these angles from "drifting" into chaos, you need power-hungry, high-heat 32-bit processors (chips that calculate with extreme decimal-point precision).

But Tesla has engineered a way to cheat the laws of physics. Freshly revealed in patent US20260017019A1, Tesla’s "MIXED-PRECISION BRIDGE" is a mathematical translator that allows inexpensive, power-sipping 8-bit hardware (which usually handles only simple, rounded numbers) to perform elite 32-bit rotations without dropping a single coordinate.

This breakthrough is the secret "Silicon Bridge" that gives Optimus and FSD high-end intelligence without sacrificing a mile of range or melting their internal circuits. It effectively turns Tesla’s efficient "budget" hardware into a high-fidelity supercomputer on wheels.

📉 The problem: the high cost of precision

In the world of self-driving cars and humanoid robots, we are constantly fighting a war between precision and power. Modern AI models like Transformers rely on RoPE to help the AI understand where objects are in a sequence or a 3D space.

The catch is that these trigonometric functions (sines and cosines) usually require 32-bit floating-point math—imagine trying to calculate a flight path using 10 decimal places of accuracy.

If you try to cram that into the standard 8-bit multipliers (INT8) used for speed (which is like rounding everything to the nearest whole number), the errors pile up fast. The car effectively goes blind to fine details.

For a robot like Optimus, a tiny math error means losing its balance or miscalculating the distance to a fragile object. To bridge this gap without simply adding more expensive chips, Tesla had to fundamentally rethink how data travels through the silicon.

🛠️ Tesla's solution: the logarithmic shortcut & pre-computation

Tesla’s engineers realized they didn't need to force the whole pipeline to be high-precision. Instead, they designed the Mixed-Precision Bridge.

They take the crucial angles used for positioning and convert them into logarithms. Because the "dynamic range" of a logarithm is much smaller than the original number, it’s much easier to move that data through narrow 8-bit hardware without losing the "soul" of the information.

It’s a bit like dehydrating food for transport; it takes up less space and is easier to handle, but you can perfectly reconstitute it later.

Crucially, the patent reveals that the system doesn't calculate these logarithms on the fly every time. Instead, it retrieves pre-computed logarithmic values from a specialized "cheat sheet" (look-up storage) to save cycles.

By keeping the data in this "dehydrated" log-state, Tesla ensures that the precision doesn't "leak out" during the journey from the memory chips to the actual compute cores. However, keeping data in a log-state is only half the battle; the chip eventually needs to understand the real numbers again.

🏗️ The recovery architecture: rotation matrices & Horner’s method

When the 8-bit multiplier (the Multiplier-Accumulator or MAC) finishes its job, the data is still in a "dehydrated" logarithmic state. To bring it back to a real angle theta without a massive computational cost, Tesla’s high-precision ALU uses a Taylor-series expansion optimized via Horner’s Method.

This is a classic computer science trick where a complex equation (like an exponent) is broken down into a simple chain of multiplications and additions.

By running this in three specific stages—multiplying by constants like 1/3 and 1/2 at each step—Tesla can approximate the exact value of an angle with 32-bit accuracy while using a fraction of the clock cycles.

Once the angle is recovered, the high-precision logic generates a Rotation Matrix (a grid of sine and cosine values) that locks the data points into their correct 3D coordinates.

This computational efficiency is impressive, but Tesla didn't stop at just calculating faster; they also found a way to double the "highway speed" of the data itself.

🧩 The data concatenation: 8-bit inputs to 16-bit outputs

One of the most clever hardware "hacks" detailed in the patent is how Tesla manages to move 16-bit precision through an 8-bit bus. They use the MAC as a high-speed interleaver—effectively a "traffic cop" that merges two lanes of data.

It takes two 8-bit values (say, an X-coordinate and the first half of a logarithm) and multiplies one of them by a power of two to "left-shift" it.

This effectively glues them together into a single 16-bit word in the output register, allowing the low-precision domain to act as a high-speed packer for the high-precision ALU to "unpack".

This trick effectively doubles the bandwidth of the existing wiring on the chip without requiring a physical hardware redesign. With this high-speed data highway in place, the system can finally tackle one of the biggest challenges in autonomous AI: object permanence.

🧠 Long-context memory: remembering the stop sign

The ultimate goal of this high-precision math is to solve the "forgetting" problem. In previous versions of FSD, a car might see a stop sign, but if a truck blocked its view for 5 seconds, it might "forget" the sign existed.

Tesla uses a "long-context" window, allowing the AI to look back at data from 30 seconds ago or more.

However, as the "distance" in time increases, standard positional math usually drifts. Tesla's mixed-precision pipeline fixes this by maintaining high positional resolution, ensuring the AI knows exactly where that occluded stop sign is even after a long period of movement.

The RoPE rotations are so precise that the sign stays "pinned" to its 3D coordinate in the car's mental map. But remembering 30 seconds of high-fidelity video creates a massive storage bottleneck.

⚡ KV-cache optimization & paged attention: scaling memory

To make these 30-second memories usable in real-time without running out of RAM, Tesla optimizes the KV-cache (Key-Value Cache)—the AI's "working memory" scratchpad.

Tesla’s hardware handles this by storing the logarithm of the positions directly in the cache. This reduces the memory footprint by 50% or more, allowing Tesla to store twice as much "history" (up to 128k tokens) in the same amount of RAM.

Furthermore, Tesla utilizes Paged Attention—a trick borrowed from operating systems. Instead of reserving one massive, continuous block of memory (which is inefficient), it breaks memory into small "pages".

This allows the AI5 chip to dynamically allocate space only where it's needed, drastically increasing the number of objects (pedestrians, cars, signs) the car can track simultaneously without the system lagging.

Yet, even with infinite storage efficiency, the AI's attention mechanism has a flaw: it tends to crash when pushed beyond its training limits.

🔒 Pipeline integrity: the "read-only" safety lock

A subtle but critical detail in the patent is how Tesla protects this data. Once the transformed coordinates are generated, they are stored in a specific location that is read-accessible to downstream components but not write-accessible by them.

Furthermore, the high-precision ALU itself cannot read back from this location.

This one-way "airlock" prevents the system from accidentally overwriting its own past memories or creating feedback loops that could cause the AI to hallucinate. It ensures that the "truth" of the car's position flows in only one direction: forward, toward the decision-making engine.

🌀 Attention sinks: preventing memory overflow

Even with a lean KV-cache, a robot operating for hours can't remember everything forever. Tesla manages this using Attention Sink tokens.

Transformers tend to dump "excess" attention math onto the very first tokens of a sequence, so if Tesla simply used a "sliding window" that deleted old memories, the AI would lose these "sink" tokens and its brain would effectively crash.

Tesla's hardware is designed to "pin" these attention sinks permanently in the KV-cache. By keeping these mathematical anchors stable while the rest of the memory window slides forward, Tesla prevents the robot’s neural network from destabilizing during long, multi-hour work shifts.

While attention sinks stabilize the "memory", the "compute" side has its own inefficiencies—specifically, wasting power on empty space.

🌫️ Sparse tensors: cutting the compute fat

Tesla’s custom silicon doesn't just cheat with precision; it cheats with volume. In the real world, most of what a car or robot sees is "empty" space (like clear sky).

In AI math, these are represented as "zeros" in a Sparse Tensor (a data structure that ignores empty space). Standard chips waste power multiplying all those zeros, but Tesla’s newest architecture incorporates Native Sparse Acceleration.

The hardware uses a "coordinate-based" system where it only stores the non-zero values and their specific locations. The chip can then skip the "dead space" entirely and focus only on the data that matters—the actual cars and obstacles.

This hardware-level sparsity support effectively doubles the throughput of the AI5 chip while significantly lowering the energy consumed per operation.

🔊 The audio edge: Log-Sum-Exp for sirens

Tesla’s "Silicon Bridge" isn't just for vision—it's also why your Tesla is becoming a world-class listener. To navigate safely, an autonomous vehicle needs to identify emergency sirens and the sound of nearby collisions using a Log-Mel Spectrogram approach (a visual "heat map" of sound frequencies).

The patent details a specific Log-Sum-Exp (LSE) approximation technique to handle this. By staying in the logarithm domain, the system can handle the massive "dynamic range" of sound—from a faint hum to a piercing fire truck—using only 8-bit hardware without "clipping" the loud sounds or losing the quiet ones.

This allows the car to "hear" and categorize environmental sounds with 32-bit clarity. Of course, all this high-tech hardware is only as good as the brain that runs on it, which is why Tesla's training process is just as specialized.

🎓 Quantization-aware training: pre-adapting the brain

Finally, to make sure this "Mixed-Precision Bridge" works flawlessly, Tesla uses Quantization-Aware Training (QAT).

Instead of training the AI in a perfect 32-bit world and then "shrinking" it later—which typically causes the AI to become "drunk" and inaccurate—Tesla trains the model from day one to expect 8-bit limitations.

They simulate the rounding errors and "noise" of the hardware during the training phase, creating a neural network that is "pre-hardened". It’s like a pilot training in a flight simulator that perfectly mimics a storm; when they actually hit the real weather in the real world, the AI doesn’t "drift" or become inaccurate because it was born in that environment.

This extreme optimization opens the door to running Tesla's AI on devices far smaller than a car.

🚀 The strategic roadmap: from AI5 to ubiquitous edge AI

This patent is not just a "nice-to-have" optimization; it is the mathematical prerequisite for Tesla’s entire hardware roadmap. Without this "Mixed-Precision Bridge", the thermal and power equations for next-generation autonomy simply do not work.

It starts by unlocking the AI5 chip, which is projected to be 40x more powerful than current hardware. Raw power is useless if memory bandwidth acts as a bottleneck.

By compressing 32-bit rotational data into dense, log-space 8-bit packets, this patent effectively quadruples the effective bandwidth, allowing the chip to utilize its massive matrix-compute arrays without stalling.

This efficiency is critical for the chip's "half-reticle" design, which reduces silicon size to maximize manufacturing yield while maintaining supercomputer-level throughput.

This efficiency is even more critical for Tesla Optimus, where it is a matter of operational survival. The robot runs on a 2.3 kWh battery (roughly 1/30th of a Model 3 pack).

Standard 32-bit GPU compute would drain this capacity in under 4 hours, consuming 500W+ just for "thinking".

By offloading complex RoPE math to this hybrid logic, Tesla slashes the compute power budget to under 100W. This solves the "thermal wall", ensuring the robot can maintain balance and awareness for a full 8-hour work shift without overheating.

This stability directly enables the shift to End-to-End Neural Networks. The "Rotation Matrix" correction described in the patent prevents the mathematical "drift" that usually plagues long-context tracking.

This ensures that a stop sign seen 30 seconds ago remains "pinned" to its correct 3D coordinate in the World Model, rather than floating away due to rounding errors.

Finally, baking this math into the silicon secures Tesla's strategic independence. It decouples the company from NVIDIA’s CUDA ecosystem and enables a Dual-Foundry Strategy with both Samsung and TSMC to mitigate supply chain risks.

This creates a deliberate "oversupply" of compute, potentially turning its idle fleet and unsold chips into a distributed inference cloud that rivals AWS in efficiency.

But the roadmap goes further. Because this mixed-precision architecture slashes power consumption by orders of magnitude, it creates a blueprint for "Tesla AI on everything".

It opens the door to porting world-class vision models to hardware as small as a smart home hub or smartphone. This would allow tiny, cool-running chips to calculate 3D spatial positioning with zero latency—bringing supercomputer-level intelligence to the edge without ever sending private data to a massive cloud server.

[Elon Musk @elonmusk]

@Ryanair Should I buy Ryan Air and put someone whose actual name is Ryan in charge?

[ELON CLIPS @ElonClipsX]

Elon Musk: Lots of hard work followed by death is usually what happens when people start companies.

“The reason I say starting a company is like eating glass and staring at the abyss is that in order to make a company successful, it requires doing a lot of painful work, work that is not fun, working very long hours, and then, maybe your company survives, maybe it does not. And the vast majority of startups fail. Lots of hard work followed by death is usually what happens when people start companies. That's why I say if people need words of encouragement to start a company, they should not start a company.”

WELT Economic Summit, January 28, 2025

[PV @hog0403]

@theboymanager @StJsn1

[Interesting AF @interesting_aIl]

Why did school never make it this simple?

[Washingtons ghost @washghost1]

Wish the newer trucks looked like this

[Words @wordstionary]

✨

[Perception @AI_Perception]

S Y N A P S E

[Tesla @Tesla]

A Letter to Our Shareholders on the 2025 CEO Interim Award

Dear Fellow Tesla Shareholders,

Today we announce an important first step in compensating Elon Musk for his extraordinary work at Tesla. As you know, Elon has not received meaningful compensation for eight years since the 2012 CEO Performance Award was last earned in 2017. Despite overwhelming support from you in 2018 and again in 2024, our legal efforts continue in the Delaware courts to reinstate the 2018 CEO Performance Award. Despite these legal challenges, we can all agree that Elon has delivered the transformative and unprecedented growth that was required to earn all milestones of the 2018 CEO Performance Award. This growth has translated into immense value generated for Tesla and all our shareholders.

To recognize what Elon has accomplished and the extraordinary value he delivered to Tesla and our shareholders, we believe we must take action to honor the bargain that was struck in 2018. After all, “a deal is a deal.” Thus, as evidence that Tesla is committed to honoring its promises in the 2018 CEO Performance Award and intends to compensate its CEO for his future services commensurate with his contributions to our company and shareholders, we have recommended this award as a first step, “good faith” payment to Elon.

Delaware litigation continues to loom over us after seven years.

As we told you last year, the 2018 CEO Performance Award resulted in a $2.3 billion stock-based compensation charge to Tesla but brought about $735 billion of increased market capitalization. Despite delivering such extraordinary returns, that award continues to be in legal limbo despite two separate shareholder votes supporting it by large margins. Furthermore, we have no clear timeline for resolution, as we are still waiting not only for a ruling, but a hearing date to be heard in front of the Delaware Supreme Court.  Rewarding Elon for what he has done and continues to do for Tesla is the right thing to do.

Retaining Elon Is More Important Than Ever Before

Today, Tesla is at a critical inflection point that has the potential to create continued extraordinary value for you, the shareholders. Through Elon’s unique vision and leadership, Tesla is transitioning from its role as a leader in the electric vehicle and renewable energy industries to grow towards becoming a leader in AI, robotics and related services. To succeed, it requires a leader who combines strategic foresight, adaptability, and relentless execution to outperform competition and inspire the team. Elon has demonstrated these unmatched leadership abilities time and time again with his unparalleled track record of delivering shareholder value since he joined as a founding figure and spearheaded the transformation of our extraordinary company.

And while these impending changes are exciting, the outcomes are not guaranteed. It is imperative to retain and motivate our extraordinary talent, beginning with Elon. The war for AI talent is intensifying, with recent months including multi-billion-dollar acquisitions of companies and nine-figure cash compensation packages for non-founder, individual AI engineers. Even among this group of highly talented individuals, no one matches Elon’s remarkable combination of leadership experience, technical expertise, and, arguably most importantly, decades-long proven track record of building the most revolutionary and profitable businesses across different industries. While we recognize that Elon’s business ventures, interests and other potential demands on his time and attention are extensive and wide-ranging, including his leadership roles at xAI, SpaceX, Neuralink, X Corp., and The Boring Company as well as his other interests, we are confident that this award will incentivize Elon to remain at Tesla and focus his unmatched leadership abilities on further creating shareholder value for Tesla shareholders and attracting and retaining talent at Tesla. To be clear, losing Elon would not only mean the loss of his talents but also the loss of a leader who is a magnet for hiring and retaining talent at Tesla.

The Special Committee believes now is the right time to take decisive action to recognize the extraordinary value that Elon created for Tesla shareholders. As such, the Board (with Elon and Kimbal Musk recusing themselves), has unanimously approved a recommendation from the Special Committee of the Board to grant Elon an award of restricted stock equal to approximately one-third of the compensation he earned under the 2018 CEO Performance Award.

The award provides the following provisions:

• 96 million restricted shares of stock, subject to Elon paying a purchase price upon meeting a two-year vesting term, to be delivered after receipt of antitrust regulatory approval;
• The purchase price will be equal to the split adjusted exercise price of the stock options awarded to Elon under the 2018 CEO Performance Award ($23.34 per share);
• A requirement that Elon serve continuously in a senior leadership role at Tesla during the two-year vesting term;
• A pledging allowance to cover tax payments or the purchase price;
• A mandatory holding period of five years from the grant date, except to cover tax payments or the purchase price (with any sales for such purposes to be conducted through an orderly disposition in coordination with Tesla); and
• If the Delaware courts fully reinstate the 2018 CEO Performance Award, this interim award will be forfeited or returned or a portion of the 2018 CEO Performance Award will be forfeited. To put it simply, there cannot be any “double dip.” Elon will not be able to keep this new award in addition to the options he will be awarded under the 2018 CEO Performance Award should the courts rule in our favor.

The Special Committee, consisting of the two of us, was formed earlier this year to consider how best to retain and incentivize Elon in a manner that aligned with the best interests of the Company. The Special Committee and the Board deliberated carefully over the decision to grant this interim award against the backdrop of the ever-intensifying AI talent war and Tesla’s position at a critical inflection point. We believe it directly addresses a top concern and priority for shareholders and the Board alike: energizing and focusing Elon on Tesla so he can propel Tesla into its next era of growth, while we continue the legal campaign to have the 2018 CEO Performance Award reinstated. This interim award is structured to incrementally increase his voting rights upon grant, which he has repeatedly told us—and shareholders have confirmed—is an important part of incentivizing him to stay focused on the critical work we are doing here at Tesla. We believe this is a vital consideration, and we used the tools currently available to us—our existing equity incentive plan—to grant this award.

We would also like to stress that prior to recommending this award, we reviewed your letters, read your X posts, and considered the direct feedback we have received from many of you in order to align our recommendation with your expressed views. From those communications, we know that one of your top concerns is keeping Elon’s energies focused on Tesla. This award is a critical first step toward achieving that goal, although it is limited by the capacity of our current equity incentive plan. As such, we are also working on next steps to address that issue. Still, while our work remains ongoing, we feel it is important to communicate directly and transparently with you all, our shareholders and Tesla’s owners.

The Special Committee continues our work to address a longer-term CEO compensation strategy, which we plan to put to a shareholder vote at the November 6 annual meeting.

Thank you for your continued support of Tesla and stay tuned for more information as we get closer to our shareholder meeting.

Very truly yours,

Robyn Denholm & Kathleen Wilson-Thompson

Members of the Special Committee of the Board of Directors

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