When surveillance capitalism enters the garage

These days, the focus in the automotive industry is on batteries. The public is debating the conditions for the transition from internal combustion to electric. However, the real transformation is taking place in the processes of data collection, processing, and transmission. This is because the automobile is no longer just a vehicle; it has moved beyond technical parameters like speed, route, and braking, and has evolved into a sensor network that measures the driver's attention, behavior, and preferences. This network works not only for safety but also for new business models, insurance pricing, and algorithmic scoring systems. In other words, the issue is now beyond the engine; it is about data ownership and the new power relations arising from this data.

FROM TELEMETRY TO BEHAVIOR

Automobiles were collecting data before, but it was mostly limited to engine control, fault diagnosis, or simple telemetry. The automobile of the data age, however, monitors the driver's behavior, route, attention level, and other connected devices; it collects behavioral, location/route, perception, and cabin data.

Most of the data is collected for safety reasons; lane keeping, emergency braking, or autonomous driving depend on it. However, safety is no longer the goal in automotive; it is a side benefit. The value emerges when data becomes a business model. The driver produces the data, the car packages and processes it, and turns it into economic value. Moreover, the driver often does not know what is being collected, cannot see what is being processed, and does not receive a share of the resulting added value.

At this point, the question of 'who owns what' arises. The manufacturer says, "I developed the car, the telemetry is mine." The driver thinks the data belongs to them. Insurance companies, on the other hand, may demand behavioral data for pricing. This is the activation of what Zuboff calls behavioral surplus—that is, raw data ceasing to be a 'record of the past' and becoming a 'prediction of the future.' Because economic value no longer accumulates in the data itself, but in the insights derived from that data. In the automotive ecosystem, this means creating models of how the driver will behave in the future:

• Route, acceleration, and sudden braking data are reflected in insurance premium assessments.

• Attention data triggers safety systems.

• Driver behavior can affect credit and financing terms.

• It is predicted which driver will pay for which function.

• It is analyzed which user segment will generate software-based revenue.

• It is measured which usage profile is suitable for upselling.

• Drivers who use certain routes at certain times can be exposed to commercial offers.

• Regional usage densities determine the planning of service networks.

These predictions can determine not only pricing but also the services the driver can access, insurance conditions, and even safety protocols in the future. In short, the driver's future becomes dependent on how their behavioral data is interpreted in predictive models.

This picture is surveillance capitalism adapted to the automotive industry. The first examples of the model were discussed on social media platforms; today, it is being carried over to automotive. The critical difference between social media and automotive is the medium: social media was a communication environment based on voluntary participation, while the automobile is a tool embedded in our daily lives. Therefore, participation is no longer a platform choice but an infrastructural necessity; data production is more of a behavioral output than a communicative activity.

REGULATION: TECHNOLOGY IS AHEAD, LAW IS RUNNING BEHIND

When data begins to generate economic power, a legal framework becomes inevitable. In Europe, the GDPR and especially the Data Act are establishing a common ground. In Turkey, the KVKK (Personal Data Protection Law) outlines the framework. In the US, regulation is progressing on a state-by-state basis, and it is difficult to achieve unity in automotive because the lobbying power of technology companies is strong. In summary, the global picture is fragmented.

The implementation of the Data Act is important for automotive data because the regulation enables third-party access to machine data. This weakens the vehicle manufacturer's ecosystem while increasing competition in the maintenance and repair market. On the insurance side, tying driving behavior to premiums can turn data ownership into pricing power. In other words, behavioral data becomes decisive for maintenance costs, insurance premiums, service diversity, and the sector's aftermarket profitability. In short, data is no longer a technical output; it is a strategic resource that operates on competitive advantage and income distribution.

TESLA, BMW, AND BYD

If we examine the transformation through the examples of Tesla, BMW, and BYD:

Tesla is the pioneer of the data-centric autonomous driving model. Raw camera data is collected through its vehicle fleet, processed, and autonomous driving models are updated. Driver attention is tracked via an in-cabin camera. The vehicle's behavior can be changed remotely with autopilot updates. Furthermore, Tesla's insurance company turns driving behavior into a risk score and determines premiums.

BMW represents the closed ecosystem + subscription model. Some technical and comfort functions are locked via software and unlocked with a monthly subscription. Here, the principle of ownership changes: the driver owns the hardware but does not have the right to access the function. This is a model that changes the car's post-sale revenue stream based on user behavior: margins are created from software, not metal.

BYD, on the other hand, is the representative of the state-supported data ecosystem in China. The fleet generates a large data pool that measures driving behavior, route, charging cycle, and energy consumption together. This data is connected not only to commercial predictions (predictive maintenance, fleet optimization, charging infrastructure planning) but also to city-scale decision-making processes. Thus, behavioral data becomes an infrastructural input. The user is included in prediction models through the data they produce; the insight guides both the company's service design and the state's urban planning. In the BYD model, surveillance operates in a collective and infrastructural way beyond individuality; this extends surveillance capitalism from the platform field to the city scale.

All three examples point to the same rupture: power no longer accumulates in the production chain, but in the data chain.

Although the debate in the short term seems to be progressing under the heading of 'electric or internal combustion?', the truly decisive transformation will take place in data, not in the battery. Because in automotive, value is now produced from predictive capacity, not hardware:

• Driving behavior is linked to insurance pricing,

• Usage cycle to maintenance-repair planning,

• Route habits to commercial offers,

• Attention and cabin data to safety protocols,

• And usage profile to subscription and function-locking models.

Therefore, the real question is: who owns this data, who is the decision-maker, and who is the winner? The answer to this question will determine not only the ownership debate but also risk models, financing conditions, competition among maintenance networks, and how software-based revenues will be shared.

The steering wheel may remain with the driver for a while longer, but economic power will be established over the data.