11 Fun, Reliable Manual Cars Under $10,000







It’s no secret that the manual transmission is going the way of the dodo. In 2021, less than 1% of cars sold in the U.S. were manual transmissions, and it’s stayed around that percentage ever since. Automatics usurped the manual transmission a long time ago, and CVTs are now the new hotness, even if they have their fair share of issues. Manual transmissions still have a cult following among enthusiast drivers who want something more engaging that helps them feel more connected to their cars. 

There are still some automakers with manual transmission cars, and it shouldn’t surprise anyone that they are aimed at the enthusiast market. The best place to find a manual transmission, especially on a budget, is the used market. That is where most manual transmission cars are anyway, and these days, you can get some older models for much cheaper than you can a brand-new car. In many cases, you can even find them for under $10,000, which makes for a great first manual if you want to learn or just want to have some old-school fun. 

So, if you’re in the market for a less expensive manual transmission that should last you for a little while and add some driving enjoyment in the process, you have more choices than you think, and many of them are listed below. 

Honda Civic

The Honda Civic has long been known as a fun car to drive, and it was one of many such cars featured in movies like “The Fast and the Furious,” although those were modified far beyond the car’s stock capabilities. These are also popular and have been for decades, so there are quite a lot of used examples available for under $10,000. 

In fact, you can find them easily for under $10,000 while also having less than 100,000 miles, which leaves a lot of life in the engine and drivetrain components. Most manual Civics in that price range with that mileage are from the early 2010s or late 2000s, although you can find them cheaper if you don’t mind having more miles on them. It seems the cutoff in the modern used market is about 50,000 to 75,000 miles for under $10,000. Those aren’t terribly common, though, and that depends heavily on where you are and the used car prices in your area. 

There isn’t much wrong with these older Civics. They’re known for being quite reliable, easy to repair, and generally easy to live with and drive. There are also some examples of the Civic Si, which is a more fun version of the Civic, for that price, but you have to go back to the mid-2000s to find one. The 2012-15 model years are a little less fun than some others but are still very drivable. 

Mazda MX-5 Miata

When it comes to small cars that are fun to drive, there are few out there that can stack up to the Mazda Miata. It doesn’t matter who you ask; the car has a level of fun not seen in many other vehicles, and part of it is thanks to its tiny size. It’s low to the ground and doesn’t weigh a lot. That lets you throw it around like a go-kart, which easily makes up for the fact that it has never really been the fastest car in its class. Plus, they’re pretty easy to find in a convertible, which adds another element of fun. 

There are plenty of Mazda Miatas for sale for under $10,000, and many of them have under 100,000 miles, leaving the car with plenty of life. You do have to dip into some older models, with the most common years being the early to mid-2000s. Miatas tend to be used as weekend vehicles rather than daily drivers, so you also have increased odds of finding one in better condition than one that’s driven year-round.

They are also reasonably reliable, typically scoring in the top half of the compact car segment, with RepairPal saying that Miatas require fewer major repairs and have a lower ownership cost than the average compact car. 

Mazda Mazda3

If the Miata is a little too small for you, Mazda has another great option with the Mazda3. This little guy comes in a sedan or hatchback configuration and has for most of its production run. The fun factor is nearly the same as the Mazda Miata, where it’s so small that you can throw it around without upsetting the car. However, most reviewers recommend getting the upgraded engine in virtually any generation if you can find it in your budget. 

There should be plenty in your price range if you’re looking for something under $10,000 with under 100,000 miles. Unlike the Miata, you can also find newer examples, with Mazda3s going for that price into the mid-2010s instead of just the 2000s. That isn’t new enough for modern stuff like Android Auto or Apple CarPlay, but it is just one generation behind. These are generally used more as daily drivers, so the mileage is higher overall, but you can still find some in the 80,000-mile range with plenty of life left. 

That’s good news because the Mazda3 is considered pretty reliable by most authorities like RepairPal and J.D. Power. It’s not at the top of its segment, but it’s above average, so you have good odds of getting one that’ll last you a while. We also recommend the hatchback, since you get more storage space.

BMW 3-Series

We’re as surprised as you are that a BMW made it onto the list, and even more surprised that it does so more than once. The BMW 3-Series is BMW’s compact car, so it’s fun for the same reasons that most compact cars are fun. You can throw it around and drive it aggressively with some confidence, and BMW even includes a decently fast engine and an option for all-wheel drive. This car has been around for decades, so there are a ton on the used market. 

For under $10,000 (and 100,000 miles), you’re mostly limited to 3-Series models in the early to mid-2000s, although there are a few that exist for the 2008, 2009, and 2010 model years. If you can swing it, try to get one after 2005, since that generation was particularly notable for being an outstanding generation of the 3-Series in terms of fun. They’re quick, handle well, and still have a solid interior even for their age.

The only downside is that reliability is average at best. If you’re looking for a reliable 3-Series, you’ll want to look for models before 2006 and after 2011, as those years were particularly problematic. So, as a shopper, you have a choice. You can risk it with a more fun 2006-2011 model or sacrifice a bit of fun for a safer bet on an older model. 

BMW Z4

Arguably the more fun option for BMW is the BMW Z4. This is in the same category as the Mazda Miata, which means it’s a small, two-seater go-kart that can take a corner with the best of them. In fact, reviewers say that the car is among the most fun-to-drive cars that money can buy, since BMW also includes a swifter engine than the one included in the Miata. Also like the Miata, it commonly comes in a convertible, giving you some additional top-down fun. In short, there’s very little that’s not fun about the Z4. 

You can find them for a reasonable price as well, but you do have to go back in time a little bit. Early to mid-2000s model years are pretty plentiful, and more are available in newer model years if you don’t mind going over 100,000 miles. All of the models we found had convertibles, so you won’t have to choose whether or not you want one. Reviewers say to potentially avoid the Sport Package if you can, since the ride is more agreeable without it. 

The question mark is, once again, reliability, where the Z4 scores average at best. However, actual owners say that as long as you keep up with maintenance, it can last for a pretty long time, especially if you do your own repairs.

Volkswagen GTI

Volkswagen has long been a bastion automaker for people who enjoy a fun jaunt in their vehicles, and the GTI is arguably the best for this particular list. The GTI is a more performance-oriented version of the Volkswagen Golf, so if you want the best iteration of the Golf for fun, a GTI is where you want to go. Reviewers agree that the ride and handling make it a fun car to drive. It may not be quite as much fun as a Miata or a BMW 3-Series, but it’s certainly more so than your average hatchback.

You can find these pretty readily for under $10,000 and under 100,000 miles. You’ll most likely see them between the 2007 and 2012 model years if you want that particular combination, but you can find other model years if you go higher than 100,000 miles. Since it’s a hatchback, you get that extra storage room as well, which adds some functionality in addition to its fun driving nature. This is easily a car that you can use for a commute. 

Reliability is better than the BMWs, but worse than the Honda Civic and Mazdas on the list. It’s about average, depending on the model year. Drivers are similarly split, with some calling it quite reliable, and others saying that gremlins aren’t terribly uncommon.

Volkswagen Beetle

The Volkswagen Beetle may be one of the most fun cars to ever exist. It’s not as athletic or as quick as some, but there’s a satisfaction that comes with knowing that wherever you go, people are being slugged in their cars. It’s an automotive icon, and if you can still get one, they’re actually pretty nice to drive as well. The base engine is okay and will definitely feel zippy around town, but if you can find one with the 2.0-liter turbo-four, you’ll get some extra zip.

You can still find these online for $10,000 or less, and some of them are in pretty decent shape. For newer models, you’ll likely find them in the early 2010s, several years before Volkswagen discontinued the nameplate. During our travels across the web, we even found one from 1965, a Wunderbug edition that was being sold for a scant $7,500 with only 34,000 miles on it. However, unless you like old cars, the newer models will probably be a little more fun to drive. 

Reliability for the Beetle ranges from average to just above average, depending on the model year. Its reliability scores are fairly good, it’s just that other compact cars do better. You can also find rebuild kits if you’re looking to restore one that’s seen better days.

Ford Mustang

The Ford Mustang has been around for over 60 years, and it’s usually not talked about as a budget vehicle. However, if you look in the right corners and the right dealerships, you can find them for less money than you would think. The Ford Mustang is clearly one of the most fun cars to ever exist, and the reasons are plentiful. The engines are generally quick, the car handles pretty well, and even the engine and exhaust noises bring a bit of thrill. They’re also sold as convertibles, which adds yet another element. 

You likely won’t find a classic Mustang from the 1960s or one of the myriad special editions on sale for $10,000 or less, but there are models that exist in that price range. You get a pretty good choice in terms of model years as well, with examples existing from the mid-1990s to the early 2010s. We even found a couple of convertibles in the mix. The Mustang enjoys the same special treatment as the Miata and others, where this is usually a weekender instead of a daily driver, so the mileage is often quite reasonable for many cheap Mustangs as well. 

Mustangs also tend to be pretty reliable, scoring high marks on multiple authority websites like J.D. Power, although the average cost of ownership is a bit higher than average. 

Mini Cooper

We return to the compact car segment once again with the Mini Cooper. It’s certainly one of the most distinctive cars on the list, and its tiny size makes it great for virtually any parking spot ever made anywhere in the world. Reviewers have long praised the car for its quick acceleration, sharp handling, and generally fun driving characteristics. Drivers tend to agree, touting the car’s handling and general go-kart-like nature as reasons why it’s fun to own and drive one of these. Plus, the interior is apparently quite spacious. 

There are loads of these for under $10,000, even with under 100,000 miles, and even more if you don’t mind higher-mileage cars. Model years have a good range as well, from the mid-2000s to the mid-2010s, giving you a decade of model years to shop around for. There are even a few with under 60,000 miles and at least one for sale as of this writing with under 50,000 miles at that price point. These make for good weekend cars or daily drivers, so you can find some with very high mileage as well. 

Reliability isn’t the Cooper’s strongest point, but authorities like J.D. Power say that the car’s reliability is at least average. Paired with the low mileage, you should get some good use out of a used one if the prior owners took good care of it. 

Fiat 500

The Fiat 500 was built for this list. Fiat discontinued it in 2019, but for a few years it was an inexpensive small car that was fun to drive because small cars are almost universally fun to drive. For reference, the 2018 Fiat 500 maxed out at $21,740, which would make it one of the cheapest new cars in today’s market. Fiat still makes the 500e, an electric variant that costs a lot more than its gas counterpart ever did. It’s still pretty fun to drive, though.

Since it was such an inexpensive car, it’s almost harder to find a Fiat 500 that’s over $10,000 on the used market. There are a metric ton of examples online, with many of them having well under 100,000 miles. They are also available for that price in newer model years than any other car we looked at, with examples ranging all the way up to 2017. So, if you’re a little squeamish about buying a 20- or 30-year-old car, the Fiat 500 is worth a look. 

Its reliability is just okay, according to most authorities. It’s certainly not the worst car in its segment, but it’s also not the best, sitting somewhere in the middle depending on who you ask. Even owners say it’s better for quick jaunts than long, drawn-out road trips.

Chrysler Crossfire

The Chrysler Crossfire is a bit of a wildcard. It’s just starting its character arc as a collectible car, and you may be able to get in on it before prices skyrocket. Chrysler only made the car for four years, and it has an interesting history. The car is best known for sharing parts with Mercedes-Benz, and even used an AMG V8-powered variant. About 34,000 Crossfires were sold in the U.S., making it a somewhat rare car. Reviewers also found the car fun to drive, even with the base V6 engine. 

There aren’t a ton of these for sale for under $10,000 and also under 100,000 miles, but there are a few. If you can pick one up, you get what is essentially a blend of a Chrysler and a Mercedes, which is an unusual piece of automotive history. It’s unlikely to skyrocket in value, but it does look nice, even by today’s standards, not just for a car made in the mid-2000s. The examples we found also had surprisingly low miles, with two of them being under 50,000 miles. 

Reliability here is a bit of a gamble. There isn’t any reliable data showing whether these things stayed together or fell apart once the mileage started getting higher. However, some drivers maintain that this is basically a Mercedes in sheep’s clothing, as the metaphor goes, and that’s definitely going to last a while.

How we chose these cars

These vehicles were selected based on three major criteria. They had to be generally known as being fun to drive, they had to cost under $10,000, and they had to be at least of average reliability or better with proper care and maintenance. Every car on the list above fits all three criteria, and that’s how it made the list. 

There are tons of cars that cost less than $10,000 that are reliable but also may not be in great condition due to high mileage. The Honda Civic is known for excellent reliability, but 200,000 miles is 200,000 miles, so we went the extra mile (pun intended) and made sure that every vehicle above was available for under $10,000 with under 100,000 miles, so the car has a solid chance at lasting a long life. 

We did check around, and there aren’t any fun cars for under $10,000 new, especially with the way auto prices have trended over the last few years. So, every car had to be used as well. Per the norm with used cars, availability may be limited in your area. All the cars above have examples that are under $10,000 and under 100,000 miles. However, that does not mean that they exist where you live, so that’s something to keep in mind. 





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Recent Reviews


In May 2024, we released Part I of this series, in which we discussed agentic AI as an emerging technology enabling a new generation of AI-based hardware devices and software tools that can take actions on behalf of users. It turned out we were early – very early – to the discussion, with several months elapsing before agentic AI became as widely known and discussed as it is today. In this Part II, we return to the topic to explore legal issues concerning user liability for agentic AI-assisted transactions and open questions about existing legal frameworks’ applicability to the new generation of AI-assisted transactions.

Background: Snapshot of the Current State of “Agents”[1]

“Intelligent” electronic assistants are not new—the original generation, such as Amazon’s Alexa, have been offering narrow capabilities for specific tasks for more than a decade. However, as OpenAI’s CEO Sam Altman commented in May 2024, an advanced AI assistant or “super-competent colleague” could be the killer app of the future. Later, Altman noted during a Reddit AMA session: “We will have better and better models. But I think the thing that will feel like the next giant breakthrough will be agents.” A McKinsey report on AI agents echoes this sentiment: “The technology is moving from thought to action.” Agentic AI represents not only a technological evolution, but also a potential means to further spread (and monetize) AI technology beyond its current uses by consumers and businesses. Major AI developers and others have already embraced this shift, announcing initiatives in the agentic AI space. For example:  

  • Anthropic announced an updated frontier AI model in public beta capable of interacting with and using computers like human users;
  • Google unveiled Gemini 2.0, its new AI model for the agentic era, alongside Project Mariner, a prototype leveraging Gemini 2.0 to perform tasks via an experimental Chrome browser extension (while keeping a “human in the loop”);
  • OpenAI launched a “research preview” of Operator, an AI tool that can interface with computers on users’ behalf, and launched beta feature “Tasks” in ChatGPT to facilitate ongoing or future task management beyond merely responding to real time prompts;
  • LexisNexis announced the availability of “Protégé,” a personalized AI assistant with agentic AI capabilities;
  • Perplexity recently rolled out “Shop Like a Pro,” an AI-powered shopping recommendation and buying feature that allows Perplexity Pro users to research products and, for those merchants whose sites are integrated with the tool, purchase items directly on Perplexity; and
  • Amazon announced Alexa+, a new generation of Alexa that has agentic capabilities, including enabling Alexa to navigate the internet and execute tasks, as well as Amazon Nova Act, an AI model designed to perform actions within a web browser.

Beyond these examples, other startups and established tech companies are also developing AI “agents” in this country and overseas (including the invite-only release of Manus AI by Butterfly Effect, an AI developer in China). As a recent Microsoft piece speculates, the generative AI future may involve a “new ecosystem or marketplace of agents,” akin to the current smartphone app ecosystem.  Although early agentic AI device releases have received mixed reviews and seem to still have much unrealized potential, they demonstrate the capability of such devices to execute multistep actions in response to natural language instructions.

Like prior technological revolutions—personal computers in the 1980s, e-commerce in the 1990s and smartphones in the 2000s—the emergence of agentic AI technology challenges existing legal frameworks. Let’s take a look at some of those issues – starting with basic questions about contract law.

Note: This discussion addresses general legal issues with respect to hypothetical agentic AI devices or software tools/apps that have significant autonomy. The examples provided are illustrative and do not reflect any specific AI tool’s capabilities.

Automated Transactions and Electronic Agents

Electronic Signatures Statutory Law Overview

A foundational legal question is whether transactions initiated and executed by an AI tool on behalf of a user are enforceable.  Despite the newness of agentic AI, the legal underpinnings of electronic transactions are well-established. The Uniform Electronic Transactions Act (“UETA”), which has been adopted by every state and the District of Columbia (except New York, as noted below), the federal E-SIGN Act, and the Uniform Commercial Code (“UCC”), serve as the legal framework for the use of electronic signatures and records, ensuring their validity and enforceability in interstate commerce. The fundamental provisions of UETA are Sections 7(a)-(b), which provide: “(a) A record or signature may not be denied legal effect or enforceability solely because it is in electronic form; (b) A contract may not be denied legal effect or enforceability solely because an electronic record was used in its formation.” 

UETA is technology-neutral and “applies only to transactions between parties each of which has agreed to conduct transactions by electronic means” (allowing the parties to choose the technology they desire). In the typical e-commerce transaction, a human user selects products or services for purchase and proceeds to checkout, which culminates in the user clicking “I Agree” or “Purchase.”  This click—while not a “signature” in the traditional sense of the word—may be effective as an electronic signature, affirming the user’s agreement to the transaction and to any accompanying terms, assuming the requisite contractual principles of notice and assent have been met.

At the federal level, the E-SIGN Act (15 U.S.C. §§ 7001-7031) (“E-SIGN”) establishes the same basic tenets regarding electronic signatures in interstate commerce and contains a reverse preemption provision, generally allowing states that have passed UETA to have UETA take precedence over E-SIGN.  If a state does not adopt UETA but enacts another law regarding electronic signatures, its alternative law will preempt E-SIGN only if the alternative law specifies procedures or requirements consistent with E-SIGN, among other things.

However, while UETA has been adopted by 49 states and the District of Columbia, it has not been enacted in New York. Instead, New York has its own electronic signature law, the Electronic Signature Records Act (“ESRA”) (N.Y. State Tech. Law § 301 et seq.). ESRA generally provides that “An electronic record shall have the same force and effect as those records not produced by electronic means.” According to New York’s Office of Information Technology Services, which oversees ESRA, “the definition of ‘electronic signature’ in ESRA § 302(3) conforms to the definition found in the E-SIGN Act.” Thus, as one New York state appellate court stated, “E-SIGN’s requirement that an electronically memorialized and subscribed contract be given the same legal effect as a contract memorialized and subscribed on paper…is part of New York law, whether or not the transaction at issue is a matter ‘in or affecting interstate or foreign commerce.’”[2] 

Given US states’ wide adoption of UETA model statute, with minor variations, this post will principally rely on its provisions in analyzing certain contractual questions with respect to AI agents, particularly given that E-SIGN and UETA work toward similar aims in establishing the legal validity of electronic signatures and records and because E-SIGN expressly permits states to supersede the federal act by enacting UETA.  As for New York’s ESRA, courts have already noted that the New York legislature incorporated the substantive terms of E-SIGN into New York law, thus suggesting that ESRA is generally harmonious with the other laws’ purpose to ensure that electronic signatures and records have the same force and effect as traditional signatures.  

Electronic “Agents” under the Law

Beyond affirming the enforceability of electronic signatures and transactions where the parties have agreed to transact with one another electronically, Section 2(2) of UETA also contemplates “automated transactions,” defined as those “conducted or performed, in whole or in part, by electronic means or electronic records, in which the acts or records of one or both parties are not reviewed by an individual.” Central to such a transaction is an “electronic agent,” which Section 2(6) of UETA defines as “a computer program or an electronic or other automated means used independently to initiate an action or respond to electronic records or performances in whole or in part, without review or action by an individual.” Under UETA, in an automated transaction, a contract may be formed by the interaction of “electronic agents” of the parties or by an “electronic agent” and an individual. E-SIGN similarly contemplates “electronic agents,” and states: “A contract or other record relating to a transaction in or affecting interstate or foreign commerce may not be denied legal effect, validity, or enforceability solely because its formation, creation, or delivery involved the action of one or more electronic agents so long as the action of any such electronic agent is legally attributable to the person to be bound.”[3] Under both of these definitions, agentic AI tools—which are increasingly able to initiate actions and respond to records and performances on behalf of users—arguably qualify as “electronic agents” and thus can form enforceable contracts under existing law.[4]

AI Tools and E-Commerce Transactions

Given this existing body of statutory law enabling electronic signatures, from a practical perspective this may be the end of the analysis for most e-commerce transactions. If I tell an AI tool to buy me a certain product and it does so, then the product’s vendor, the tool’s provider and I might assume—with the support of UETA, E-SIGN, the UCC, and New York’s ESRA—that the vendor and I (via the tool) have formed a binding agreement for the sale and purchase of the good, and that will be the end of it unless a dispute arises about the good or the payment (e.g., the product is damaged or defective, or my credit card is declined), in which case the AI tool isn’t really relevant.

But what if the transaction does not go as planned for reasons related to the AI tool? Consider the following scenarios:

  • Misunderstood Prompts: The tool misinterprets a prompt that would be clear to a human but is confusing to its model (e.g., the user’s prompt states, “Buy two boxes of 101 Dalmatians Premium dog food,” and the AI tool orders 101 two-packs of dog food marketed for Dalmatians).
  • AI Hallucinations: The user asks for something the tool cannot provide or does not understand, triggering a hallucination in the model with unintended consequences (e.g., the user asks the model to buy stock in a company that is not public, so the model hallucinates a ticker symbol and buys stock in whatever real company that symbol corresponds to).
  • Violation of Limits: The tool exceeds a pre-determined budget or financial parameter set by the user (e.g., the user’s prompt states, “Buy a pair of running shoes under $100” and the AI tool purchases shoes from the UK for £250, exceeding the user’s limit).
  • Misinterpretation of User Preference: The tool misinterprets a prompt due to lack of context or misunderstanding of user preferences (e.g., the user’s prompt states, “Book a hotel room in New York City for my conference,” intending to stay near the event location in lower Manhattan, and the AI tool books a room in Queens because it prioritizes price over proximity without clarifying the user’s preference).

Disputes like these begin with a conflict between the user and a vendor—the AI tool may have been effective to create a contract between the user and the vendor, and the user may then have legal responsibility for that contract.  But the user may then seek indemnity or similar rights against the developer of the AI tool.

Of course, most developers will try to avoid these situations by requiring user approvals before purchases are finalized (i.e., “human in the loop”). But as desire for efficiency and speed increases (and AI tools become more autonomous and familiar with their users), these inbuilt protections could start to wither away, and users that grow accustomed to their tool might find themselves approving transactions without vetting them carefully. This could lead to scenarios like the above, where the user might seek to void a transaction or, if that fails, even try to avoid liability for it by seeking to shift his or her responsibility to the AI tool’s developer.[5] Could this ever work? Who is responsible for unintended liabilities related to transactions completed by an agentic AI tool?

Sources of Law Governing AI Transactions

AI Developer Terms of Service

As stated in UETA’s Prefatory Note, the purpose of UETA is “to remove barriers to electronic commerce by validating and effectuating electronic records and signatures.” Yet, the Note cautions, “It is NOT a general contracting statute – the substantive rules of contracts remain unaffected by UETA.”  E-SIGN contains a similar disclaimer in the statute, limiting its reach to statutes that require contracts or other records be written, signed, or in non-electronic form (15 U.S.C. §7001(b)(2)). In short, UETA, E-SIGN, and the similar UCC provisions do not provide contract law rules on how to form an agreement or the enforceability of the terms of any agreement that has been formed.

Thus, in the event of a dispute, terms of service governing agentic AI tools will likely be the primary source to which courts will look to assess how liability might be allocated. As we noted in Part I of this post, early-generation agentic AI hardware devices generally include terms that not only disclaim responsibility for the actions of their products or the accuracy of their outputs, but also seek indemnification against claims arising from their use. Thus, absent any express customer-favorable indemnities, warranties or other contractual provisions, users might generally bear the legal risk, barring specific legal doctrines or consumer protection laws prohibiting disclaimers or restrictions of certain claims.[6]

But what if the terms of service are nonexistent, don’t cover the scenario, or—more likely—are unenforceable? Unenforceable terms for online products and services are not uncommon, for reasons ranging from “browsewrap” being too hidden, to specific provisions being unconscionable. What legal doctrines would control during such a scenario?

The Backstop: User Liability under UETA and E-SIGN

Where would the parties stand without the developer’s terms? E-SIGN allows for the effectiveness of actions by “electronic agents” “so long as the action of any such electronic agent is legally attributable to the person to be bound.” This provision seems to bring the issue back to the terms of service governing a transaction or general principles of contract law. But again, what if the terms of service are nonexistent or don’t cover a particular scenario, such as those listed above. As it did with the threshold question of whether AI tools could form contracts in the first place, UETA appears to offer a position here that could be an attractive starting place for a court. Moreover, in the absence of express language under New York’s ESRA, a New York court might apply E-SIGN (which contains an “electronic agent” provision) or else find insight as well by looking at UETA and its commentary and body of precedent if the court isn’t able to find on-point binding authority, which wouldn’t be a surprise, considering that we are talking about technology-driven scenarios that haven’t been possible until very recently.

UETA generally attributes responsibility to users of “electronic agents”, with the prefatory note explicitly stating that the actions of electronic agents “programmed and used by people will bind the user of the machine.” Section 14 of UETA (titled “Automated Transaction”) reinforces this principle, noting that a contract can be formed through the interaction of “electronic agents” “even if no individual was aware of or reviewed the electronic agents’ actions or the resulting terms and agreements.” Accordingly, when automated tools such as agentic AI systems facilitate transactions between parties who knowingly consent to conduct business electronically, UETA seems to suggest that responsibility defaults to the users—the persons who most immediately directed or initiated their AI tool’s actions. This reasoning treats the AI as a user’s tool, consistent with the other UETA Comments (e.g., “contracts can be formed by machines functioning as electronic agents for parties to a transaction”).

However, different facts or technologies could lead to alternative interpretations, and ambiguities remain. For example, Comment 1 to UETA Section 14 asserts that the lack of human intent at the time of contract formation does not negate enforceability in contracts “formed by machines functioning as electronic agents for parties to a transaction” and that “when machines are involved, the requisite intention flows from the programming and use of the machine” (emphasis added).

This explanatory text has a couple of issues. First, it is unclear about what constitutes “programming” and seems to presume that the human intention at the programming step (whatever that may be) is more-or-less the same as the human intention at the use step[7], but this may not always be the case with AI tools. For example, it is conceivable that an AI tool could be programmed by its developer to put the developer’s interests above the users’, for example by making purchases from a particular preferred e-commerce partner even if that vendor’s offerings are not the best value for the end user. This concept may not be so far-fetched, as existing GenAI developers have entered into content licensing deals with online publishers to obtain the right for their chatbots to generate outputs or feature licensed content, with links to such sources. Of course, there is a difference between a chatbot offering links to relevant licensed news sources that are accurate (but not displaying appropriate content from other publishers) versus an agentic chatbot entering into unintended transactions or spending the user’s funds in unwanted ways. This discrepancy in intention alignment might not be enough to allow the user to shift liability for a transaction from a user to a programmer, but it is not hard to see how larger misalignments might lead to thornier questions, particularly in the event of litigation when a court might scrutinize the enforceability of an AI vendor’s terms (under the unconscionability doctrine, for example). 

Second, UETA does not contemplate the possibility that the AI tool might have enough autonomy and capability that some of its actions might be properly characterized as the result of its own intent. Looking at UETA’s definition of “electronic agent,” the commentary notes that “As a general rule, the employer of a tool is responsible for the results obtained by the use of that tool since the tool has no independent volition of its own.” But as we know, technology has advanced in the last few decades and depending on the tool, an autonomous AI tool might one day have much independent volition (and further UETA commentary admits the possibility of a future with more autonomous electronic agents). Indeed, modern AI researchers have been contemplating this possibility even before rapid technological progress began with ChatGPT.

Still, Section 10 of UETA may be relevant to some of the scenarios from our bulleted selection of AI tool mishaps listed above, including misunderstood prompts or AI hallucinations. UETA Section 10 (titled “Effect of Change or Error”) outlines the possible actions a party may take when discovering human or machine errors or when “a change or error in an electronic record occurs in a transmission between parties to a transaction.” The remedies outlined in UETA depend on the circumstances of the transaction and whether the parties have agreed to certain security procedures to catch errors (e.g., a “human in the loop” confirming an AI-completed transaction) or whether the transaction involves an individual and a machine.[8]  In this way, the guardrails integrated into a particular AI tool or by the parties themselves play a role in the liability calculus. The section concludes by stating that if none of UETA’s error provisions apply, then applicable law governs, which might include the terms of the parties’ contract and the law of mistake, unconscionability and good faith and fair dealing.

* * *

Thus, along an uncertain path we circle back to where we started: the terms of the transaction and general contract law principles and protections. However, not all roads lead to contract law. In our next installment in this series, we will explore the next logical source of potential guidance on AI tool liability questions: agency law.  Decades of established law may now be challenged by a new sort of “agent” in the form of agentic AI…and a new AI-related lawsuit foreshadows the issues to come.


[1] In keeping with common practice in the artificial intelligence industry, this article refers to AI tools that are capable of taking actions on behalf of users as “agents” (in contrast to more traditional AI tools that can produce content but not take actions). However, note that the use of this term is not intended to imply that these tools are “agents” under agency law.

[2] In addition, the UCC has provisions consistent with UETA and E-SIGN providing for the use of electronic records and electronic signatures for transactions subject to the UCC. The UCC does not require the agreement of the parties to use electronic records and electronic signatures, as UETA and E-SIGN do.

[3] Under E-SIGN, “electronic agent” means “a computer program or an electronic or other automated means used independently to initiate an action or respond to electronic records or performances in whole or in part without review or action by an individual at the time of the action or response.”

[4] It should be noted that New York’s ESRA does not expressly provide for the use of “electronic agents,” yet does not prohibit them either.  Reading through ESRA and the ESRA regulation, the spirit of the law could be construed as forward-looking and seems to suggest that it supports the use of automated systems and electronic means to create legally binding agreements between willing parties. Looking to New York precedent, one could also argue that E-SIGN, which contains provisions about the use of “electronic agents”, might also be applicable in certain circumstances to fill the “electronic agent” gap in ESRA. For example, the ESRA regulations (9 CRR-NY § 540.1) state: “New technologies are frequently being introduced. The intent of this Part is to be flexible enough to embrace future technologies that comply with ESRA and all other applicable statutes and regulations.”  On the other side, one could argue that certain issues surrounding “electronic agents” are perhaps more unsettled in New York.  Still, New York courts have found ESRA consistent with E-SIGN.  

[5] Since AI tools are not legal persons, they could not be liable themselves (unlike, for example, a rogue human agent could be in some situations). We will explore agency law questions in Part III.

[6] Once agentic AI technology matures, it is possible that certain user-friendly contractual standards might emerge as market participants compete in the space. For example, as we wrote about in a prior post, in 2023 major GenAI providers rolled out indemnifications to protect their users from third-party claims of intellectual property infringement arising from GenAI outputs, subject to certain carve-outs.

[7] The electronic “agents” in place at the time of UETA’s passage might have included basic e-commerce tools or EDI (Electronic Data Interchange), which is used by businesses to exchange standardized documents, such as purchase orders, electronically between trading partners, replacing traditional methods like paper, fax, mail or telephone. Electronic tools are generally designed to explicitly perform according to the user’s intentions (e.g., clicking on an icon will add this item to a website shopping cart or send this invoice to the customer) and UETA, Section 10, contains provisions governing when an inadvertent or electronic error occurs (as opposed to an abrogation of the user’s wishes).

[8] For example, UETA Section 10 states that if a change or error occurs in an electronic record during transmission between parties to a transaction, the party who followed an agreed-upon security procedure to detect such changes can avoid the effect of the error, if the other party who didn’t follow the procedure would have detected the change had they complied with the security measure; this essentially places responsibility on the party who failed to use the agreed-upon security protocol to verify the electronic record’s integrity.

Comments to UETA Section 10 further explain the context of this section: “The section covers both changes and errors. For example, if Buyer sends a message to Seller ordering 100 widgets, but Buyer’s information processing system changes the order to 1000 widgets, a “change” has occurred between what Buyer transmitted and what Seller received. If on the other hand, Buyer typed in 1000 intending to order only 100, but sent the message before noting the mistake, an error would have occurred which would also be covered by this section.”  In the situation where a human makes a mistake when dealing with an electronic agent, the commentary explains that “when an individual makes an error while dealing with the electronic agent of the other party, it may not be possible to correct the error before the other party has shipped or taken other action in reliance on the erroneous record.”



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