On September 22, BMW CEO Milan Nedeljković said in an interview that some Chinese cars are selling in Europe at prices that are “hard to explain.”

But then he changed tack—BMW does not support the EU continuing to impose additional tariffs on Chinese cars.

Wary of you, yet unwilling to sanction you. This seemingly contradictory stance exposes the most authentic mindset of Europe’s auto industry today: these companies are both competitors to Chinese brands and deeply embedded participants in China’s manufacturing system.

As an AI recommendation index tracking platform covering five major mainstream models—ChatGPT, Gemini, Claude, Grok, and Perplexity—IPSmoto conducted a systematic analysis of tens of thousands of German-language Q&A data points from the German market.

This time, we are not just looking at who AI “recommends,” but focusing on a label that is crucial for Chinese companies going global—“value for money” (Preis-Leistung): How exactly does AI understand “cheap” or “low price”? On what basis does it conclude that a car offers “value for money”? And what sources does it draw on for that conclusion?

-01- BMW’s “Dual Identity”: Competitor and Beneficiary

To understand this contradiction, we first need to see BMW’s China footprint clearly:

• BMW produces all-electric MINIs in China and exports them to Europe—the cost advantage of Chinese manufacturing is an important support for BMW’s global profit margins.

• The EU currently imposes additional tariffs on China-made battery electric vehicles, and BMW is itself one of those affected by these measures.

• Chinese plug-in hybrids have not yet been included in the same tariff regime, but this has become a new focus of discussion in European policy circles.

In other words, if tariff barriers continue to expand, the first to get hurt may be European automakers themselves. That is why BMW prefers political consultation and strategic management such as voluntary price adjustments, rather than supporting a trade war.

-02- IPSmoto’s Test: AI’s Understanding of “Value for Money” Is a Reasoning Chain

AI’s understanding of “value for money” is not a single sentence; it is a reasoning chain.

Finding 1: When AI Says “Value for Money,” It Is Backed by Structured Scoring Across Seven Dimensions

By analyzing tens of thousands of AI conversation data points, we found that when recommending high-value-for-money models, AI repeatedly draws on seven dimensions.

Table: IPS-driven analysis, based on an in-depth analysis of AI recommendations for value-for-money cars in Germany.

Rank	Core Factor	What It Means	Typical Example
1	Price / Entry Threshold	Starting price, actual transaction price, leasing and discounts	Tesla Model 3 Standard Range: approx. €36,990–43,000; BYD Seal: approx. €43,000–45,000; Hyundai Ioniq 6: from approx. €43,900
2	Range (WLTP)	Distance that can be driven on a single charge	Model 3: up to approx. 750 km; Ioniq 6: up to approx. 614 km; BYD Seal: approx. 520–570 km
3	Efficiency / Energy Consumption per 100 km	Electricity consumption per 100 km, directly determining running costs	Model 3: approx. 13–14 kWh/100 km; Ioniq 6 has a drag coefficient of just 0.21, making it one of the most energy-efficient models
4	Charging Technology and Speed	Fast-charging power, 10%–80% charging time, charging network	Ioniq 6 uses an 800V system and charges to 80% in about 18 minutes; Tesla’s Supercharger network is densely covered; BYD Seal takes about 37 minutes
5	Standard Equipment Richness	Features included without extra optional equipment	Tesla includes many features as standard (heated/ventilated seats, glass roof, audio system); BYD Seal is almost “fully loaded”
6	Software and OTA Update Capability	Infotainment system, driver assistance, remote upgrades	Tesla’s software is widely regarded as the industry benchmark; BYD’s software “has not yet fully reached the level of Tesla or Hyundai”
7	Brand Service Network and Resale Value	Dealer/service location density, residual value stability, warranty	German brands have dense networks and high resale values; Hyundai offers an 8-year/5-year warranty; Chinese brands’ service networks are “still under construction”

*Note: When AI reaches a “value for money” conclusion, it usually also proactively asks about the user’s budget, annual mileage, whether they often drive long distances, whether they value brand/service network, and whether they are buying new or used—indicating that these are also implicit variables in its recommendation logic.

Finding 2: In AI’s Eyes, “Why Chinese Cars Are Cheap” Is Explainable and Verifiable

Take the BYD Seal as an example: what AI repeatedly emphasizes is not that “it sells for less than everyone else,” but that “the ratio of price, range, and features is excellent”: a near-full standard equipment list eliminates option-related price markups, the Blade Battery simultaneously lowers safety and cost concerns, and the interior is even described as more premium than that of the Model 3.

In other words, the low prices of Chinese cars have a traceable origin in AI’s corpus—configuration parity, battery technology, and supply-chain efficiency, rather than subsidies of unknown origin.

Finding 3: Behind Almost Every “Cheap” Recommendation, AI Adds a “But”

After praising the features, AI explicitly points out: software and driver assistance “have not yet fully reached the level of Tesla or Hyundai,” charging depends on third-party networks, and service locations are “still being built”…

The other side of cheapness is that the trust account has not yet been built.

In one sentence: Chinese brands win on “on-paper value for money at purchase” (price + features), while German brands win on “whole-life value for money” (service + resale value + quality + trust).

-03- What Sources Does AI Actually Cite When Judging “Value for Money”?

This is the most critical part of this analysis. We searched all Q&A related to “high value for money” in the corpus, extracted the URLs and source types actually cited in the answers of major models, and reconstructed the source structure AI uses when judging value for money:

Key conclusion: “Price” and “range/energy consumption” are the two main pillars of value-for-money judgments, and their data almost entirely come from price comparison platforms + professional media; evidence for German brands’ service and resale advantages mostly comes from authoritative institutions such as ADAC and J.D. Power; evidence for Chinese brands’ feature and price advantages mostly comes from professional reviews and price comparison platforms; user communities are an important source for AI to supplement “soft experience.”

-04- AI Marketing Action Guide for Overseas-Bound Automakers: Get the “Value for Money” Label Right and Choose the Right Sources

Back to the BMW CEO’s question—“prices so low they are hard to explain.” The data precisely shows: price itself is not the sin; failing to explain it is.

Based on this, we offer five actionable GEO strategies:

Strategy 1: Don’t Just Talk About “Cheap”; Translate “Cheap” into a Verifiable Cost Structure

In the AI corpus, almost all recommended Chinese models share a common trait: their low prices always have a product-level explanation—configuration parity (a full-feature standard list eliminates option markups), battery technology (the Blade Battery reduces both safety and cost concerns), and supply-chain efficiency.

AI marketing takeaway: Whenever brand content touches on price, it must simultaneously give the “why”: not “we are cheaper,” but “at the same price, we offer X more features,” “what the battery cost structure looks like,” and “what the measured energy consumption data shows.” Let AI reproduce your cost logic verbatim when it crawls your content.

Strategy 2: Build Content Assets in Reverse According to a “Five-Step Evidence Chain”

AI relies on five types of sources to form value-for-money conclusions. Going-global companies should fill in content ring by ring along the evidence chain:

• Price anchoring ring: ensure real transaction prices and leasing/financing offers are accurate, timely, and competitive on price comparison platforms such as carwow.de, mobile.de, and finn.com.

• Spec comparison ring: secure hands-on test coverage from German media such as insideevs.de and Auto Bild, turning range, energy consumption, and charging speed into hard data media can cite.

• Authority validation ring: proactively submit vehicles for ADAC and Euro NCAP testing and participate in satisfaction studies such as J.D. Power—endorsements that models like Gemini especially value.

• Word-of-mouth supplement ring: cultivate authentic reputation in Reddit r/Elektroautos and German-language Facebook owner communities, which AI repeatedly cites when judging “soft experience.”

• Overall conclusion ring: provide first-hand parameters such as the official configurator and WLTP range, ensuring that official website parameters can be read directly by AI.

Strategy 3: Proactively Fill In the “But” in AI’s Mouth

Data shows that after praising Chinese cars’ features, AI almost always mentions shortcomings: software and driver assistance, reliance on charging networks, and service location coverage.

If brands do not manage these shortcomings themselves, AI will fill the gap by citing negative word of mouth from communities.

AI marketing takeaway: For every “but” AI commonly raises, there must be a content asset response—an OTA roadmap, a list of charging partner networks, and service location expansion plans. Let the “but” be followed by the brand’s explanation and progress, not silence.

Strategy 4: Shift from “On-Paper Value for Money” to “Whole-Life Value for Money”

Chinese brands currently win on on-paper value for money at purchase, but AI already presents whole-life comparisons (service + resale value + trust) at the same time. The further overseas expansion goes, the more dangerous it is to rely solely on a price label—price can help a brand enter the market, but it can also trigger policy scrutiny over “subsidies” or “dumping.”

AI marketing takeaway: Content strategy should proactively address whole-life topics such as resale value, service networks, and warranty policies, so that when AI answers “Is this car worth it?”, it has ample corpus to rewrite Chinese brands from “cheap” to “worth it.”

-05- Final Thoughts

The BMW CEO’s statement is a microcosm of the European auto industry’s ambivalence: it wants to protect its market, yet cannot do without China’s manufacturing capacity; it questions low prices, yet dares not lock itself out with tariffs.

For Chinese companies going global, the signal is clear: the window for driving volume through price is narrowing, while the window for building “non-substitutability” through product strength, localized service, and technological value is opening.

And in an era when AI is redefining the consumer decision journey, the battlefield of this competition has already extended into every AI Q&A.

When AI can recommend Chinese cars on only one dimension, “why they are cheap” is left for policymakers and consumers to guess; but when “cheap” is backed by verifiable product strengths such as features, batteries, and energy consumption, low price is no longer a weakness—it is competitiveness itself.

Next time you ask AI “What is the best electric sedan in Germany?”, pay attention: does it recommend a Chinese car because it is “cheap” or because it is “worth it”?