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AI-powered robots answer US farmers’ call amid diminishing labor force

[Credit: Deepfield Robotics]

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Facing a diminishing workforce, some farmers in the United States are looking to artificial intelligence as a means to adapt to the changing labor landscape. In Florida, for example, Gary Wishnatzki of Wish Farms is rolling out AI-powered robots that are capable of picking his crops.

Wish Farms in FL made a name for itself as a producer of premium strawberries and blueberries. Strawberries are a particularly demanding crop; since every three days, the plant will give off new ripe fruit. If these fruits are not harvested according to their cycle, the strawberry plant dies.

According to Wishnatzki, his farms need around 600 workers to harvest his 600 acres of crops every two or three days. Thus, during season, workers are faced with very few rest days. In a statement to CBS News, Wishnatzki noted that his farms’ labor force, a significant number of whom is from Mexico, has been facing challenges over the past 15 years.

“We’ve seen a shrinking labor force and an aging labor force. People just aren’t showing up anymore to do this work. I started to look at what was really causing it. It was more of a demographic issue than a political issue or anything else. As the Mexican economy continues to improve as more fruit is being grown in Mexico, people aren’t going to be coming anymore,” Wishnatzki said.

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In order to address his farm’s shortage of workers, Wishnatzki looked to automation through AI-powered robots. He co-founded Harvest CROO Robotics, a company aimed at developing a machine that can pick crops using robots and artificial intelligence. A few years later, Harvest CROO has created an AI-powered robot that uses imaging technology to determine which berries are ripe enough to pick. The technology behind the AI-powered robot was described to CBS News by Paul Bisset, Harvest CROO’s CEO.

“We’re collecting 50 to 100 images a plant, and all of those images are fed into our AI system in order to tell us, OK, this is a good berry, this is one we want to go after,” Bisset said.

Even farming giants are turning to Silicon Valley to advance their operations. Farming equipment producer John Deere, for one, has opened an office in San Francisco aimed at developing smart solutions that can be used for the agricultural sector. Alex Purdy, the head of John Deere Labs, stated that the future of farming lies in advanced tech.

“We know that we absolutely need to go and seek out capabilities in machine learning, in deep learning, in robotics and advanced analytics. And Silicon Valley has been receptive,” he said.

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As we noted in a previous report, using technology in agriculture has been the focus of Kimbal Musk through his company, Square Roots. Square Roots converts old shipping containers into advanced vertical farms, which would enable even the most populated cities to have access to fresh, organic food. So far, Kimbal’s initiative continues to gain ground, just as his nonprofit, Big Green, continues to expand its reach into schools across America.

Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Tesla Optimus V3 hand and arm details revealed in new patents

Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.

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Credit: Tesla China

Tesla is planning to soon reveal its latest and greatest version of the Optimus humanoid robot, and a series of new patents for the hands and arms, with the former being, admittedly, one of the most challenging parts of developing the project.

Two new patents, which were coincidentally filed on the same day as the “We, Robot” event back in October 2024, protect Tesla’s mechanically actuated, tendon-driven architecture.

The designs relocate heavy actuators to the forearm, route cables through a sophisticated wrist design, and employ innovative joint assemblies to achieve human-like dexterity while enabling lightweight construction and high-volume manufacturing.

Core Tendon-Driven Hand Architecture

The primary patent, which is titled “Mechanically Actuated Robotic Hand,” details a cable/tendon-driven system.

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Actuators are positioned in the forearm rather than the hand. Each finger features four degrees of freedom (DoF), while the wrist adds two more.

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Three thin, flexible control cables (tendons) per finger extend from the forearm actuators, pass through the wrist, and connect to the finger segments. Integrated channels within the finger phalanges guide these cables selectively—routing behind some joints and forward of others—to enable independent bending without unintended motion.

Patent diagrams illustrate thick cable bundles emerging from the wrist into the palm and fingers, with labeled pivots and routing guides. This setup closely mirrors human forearm-muscle and tendon anatomy, where most hand control originates proximally.

Advanced Wrist Routing Innovation

One of the standout features is the wrist’s cable transition mechanism. Cables shift from a lateral stack on the forearm side to a vertical stack on the hand side through a specialized transition zone.

This geometry significantly reduces cable stretch, torque, friction, and crosstalk during combined yaw and pitch wrist movements — common failure points in simpler tendon systems that cause imprecise or jerky motion.

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By minimizing these issues, the design supports smoother, more reliable multi-axis wrist operation, essential for complex real-world tasks.

Companion Patents on Appendage and Joint Design

Two supporting patents provide additional depth. “Robotic Appendage” covers the overall forearm-to-palm-to-finger assembly, with a palm body movably coupled to the forearm and finger phalanges linked by tensile cables returning to forearm actuators. Tensioning these cables repositions the phalanges precisely.

“Joint Assembly for Robotic Appendage” describes curved contact surfaces on mating structures paired with a composite flexible member. This allows smooth pivoting while maintaining consistent tension, enhancing durability, and simplifying assembly for mass production.

Executive Insights on Hand Development Challenges

Tesla executives have consistently described the hand as the most difficult component of Optimus.

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Elon Musk has called it “the majority of the engineering difficulty of the entire robot,” emphasizing that human hands possess roughly 27–28 DoF with an intricate tendon network powered largely by forearm muscles. He has likened the challenge to something “harder than Cybertruck or Model X… somewhere between Model X and Starship.”

Elon Musk shares ridiculous fact about Optimus’ hand demos

In mid-2025, Musk acknowledged that Tesla was “struggling” to finalize the hand and forearm design. By early 2026, he stated that the company had overcome the “hardest” problems, including human-level manual dexterity, real-world AI integration, and volume production scalability.

He estimated the electromechanical hand represents about 60 percent of the overall Optimus challenge, compounded by the lack of an existing supply chain for such precision components.

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These patents directly tackle the acknowledged pain points: relocating actuators reduces hand mass and inertia for better speed and efficiency; advanced wrist routing and joint geometry address friction and crosstalk; and simplified, stackable parts visible in the diagrams indicate readiness for high-volume manufacturing.

Implications for Optimus Production and Leadership

Collectively, the patents portray the Optimus v3 hand not as a mere prototype, but as a production-oriented system engineered from first principles.

The 22-DoF architecture, forearm-driven tendons, and crosstalk-minimizing wrist deliver a clear competitive edge in dexterity. They align with Musk’s view that high-volume manufacturing is one of the three critical elements missing from most other humanoid projects.

For Optimus to become the most capable humanoid robot, its hand needed to replicate the useful and applicable design of the human counterpart.

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These filings demonstrate that Tesla has transformed years of engineering challenges into patented, elegant solutions — positioning the company strongly in the race toward general-purpose robotics.

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Tesla intertwines FSD with in-house Insurance for attractive incentive

Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.

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tesla interior operating on full self driving
Credit: TESLARATI

Tesla intertwined its Full Self-Driving (Supervised) suite with its in-house Insurance initiative in an effort to offer an attractive incentive to drivers.

Tesla announced that its new Safety Score 3.0 will automatically have a perfect score of 100 with every mile driven with Full Self-Driving (Supervised) enabled.

The change is designed to boost customers’ average safety scores and deliver noticeably lower monthly premiums.

The move marks the clearest link yet between Tesla’s autonomous driving technology and its proprietary insurance product. Tesla Insurance already relies on real-time vehicle data—such as acceleration, braking, following distance, and speed—to calculate a Safety Score between 0 and 100. Higher scores have long translated into cheaper rates.

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Under the previous system, however, even brief manual interventions could drag down the average, frustrating owners who rely heavily on FSD. Version 3.0 eliminates that penalty for supervised autonomous miles, effectively treating FSD-driven segments as the safest possible driving behavior.

The incentive is immediate and financial. Drivers who keep FSD engaged for the majority of their trips will see their overall score rise, potentially shaving hundreds of dollars off annual premiums.

Tesla framed the update as a direct response to customer feedback, many of whom had complained that the old scoring model punished the very behavior it was meant to encourage.

For now, the program applies only to new policies in six states: Indiana, Tennessee, Texas, Arizona, Virginia, and Illinois.

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Existing policyholders are not yet included, a point that drew swift questions from the Tesla community. Many owners in other states, including California and Georgia, expressed hope that the benefit would expand nationwide soon.

The announcement arrives as Tesla continues to roll out FSD Supervised updates and push for regulatory approval of more advanced autonomy. By tying insurance savings directly to FSD usage, the company is putting its own actuarial weight behind the technology’s safety claims.

Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.

Tesla has not disclosed exact premium reductions or the full rollout timeline beyond the six launch states.

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Still, the message is clear: the more drivers trust FSD Supervised, the more Tesla Insurance will reward them. In an era when legacy insurers remain cautious about autonomous tech, Tesla is betting that its own data will prove the safest miles are the ones driven hands-free.

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Tesla finalizes AI5 chip design, Elon Musk makes bold claim on capability

The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.

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Credit: Elon Musk | X

Tesla has finalized its chip design for AI5, as Elon Musk confirmed today that the new chip has reached the tape-out stage, the final step before mass production.

But in a brief reply on X, Musk clarified Tesla’s AI hardware roadmap, essentially confirming that the new chip will not be utilized for being “enough to achieve much better than human safety for FSD.”

He said that AI4 is enough to do that.

Instead, the AI5 chip will be focused on Tesla’s big-time projects for the future: Optimus and supercomputer clusters.

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Musk thanked TSMC and Samsung for production support, noting that AI5 could become “one of the most produced AI chips ever.” Yet, the key pivot came in his direct answer: vehicles no longer need the bleeding-edge silicon.

Existing AI4 hardware, which is already deployed in hundreds of thousands of HW4-equipped Teslas, delivers safety metrics superior to human drivers for Full Self-Driving. AI5 will instead accelerate Optimus robot development and massive Dojo-style training clusters.

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The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.

Now, with AI4 proving sufficient, the company avoids costly retrofits across its fleet while redirecting next-generation compute toward higher-value applications: dexterous robots and exponential training scale.

But is it reasonable to assume AI4 enables unsupervised self-driving? Yes, but with important caveats.

On the hardware side, the claim is credible. Tesla’s FSD stack runs end-to-end neural networks trained on billions of miles of real-world data. Internal safety data reportedly shows AI4-equipped vehicles already outperforming average human drivers by a significant margin in controlled metrics (collision avoidance, reaction time, edge-case handling).

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Dual-redundant AI4 chips provide ample headroom for the driving task, leaving bandwidth for future model improvements without new silicon. Musk’s assertion aligns with Tesla’s pattern of over-provisioning compute early, then optimizing ruthlessly, exactly as HW3 once sufficed before HW4 scaled further.

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Unsupervised autonomy, meaning Level 4 or higher, is not solely a compute problem. Regulatory approval remains the primary gate.

Even if AI4 achieves “much better than human” safety statistically, agencies like the NHTSA demand exhaustive validation, liability frameworks, and public trust.

Tesla’s supervised FSD has shown rapid gains in recent versions, yet real-world edge cases, like construction zones, emergency vehicles, and adverse weather, still require driver intervention in many jurisdictions. Competitors like Waymo operate limited unsupervised fleets, but only in geofenced areas with extensive mapping. Tesla’s vision-only, fleet-scale approach is more ambitious—and harder to certify globally.

In short, Musk’s post is both pragmatic and bullish. AI4 is likely capable of unsupervised FSD from a technical standpoint. Whether regulators and consumers agree, and how quickly, will determine if Tesla’s bet pays off.

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The company’s capital-efficient path keeps existing cars relevant while pouring future compute into robots. If the safety data holds, unsupervised autonomy could arrive sooner than many expect.

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