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Our findings contribute to research on the role of entrepreneurial marketing and IP assets in VC valuation (Baum and Silverman, 2004; Douglas and Shepherd, 2002; Gruber, 2004; Haeussler et al., 2009; Hellman and Puri, 2000, 2002; Hills, 1984; Hisrich, 1989; Hsu and Ziedonis, 2008; Miloud et al., 2012; Wortman et al., 1989) and research on the value of trademarks (Greenhalgh and Rogers, 2006a,b; Sandner and Block, 2011) in at least four ways.

First, we contribute to the literature on the role of IP in VC financing by showing that the number and breadth of trademark applications in start-ups are positively related to VC valuation. Previous studies on VC valuation provide evidence for the criteria by which VCs evaluate start-ups, such as the quality of the start-up's business plan, venture team, patents, and network as well as alliance capital (e.g., Baum and Silverman, 2004; Franke et al., 2006; Haeussler et al., 2009; Hall and Hofer, 1993; Hsu and Ziedonis, 2008; Macmillan et al., 1985). Our finding extends this literature by demonstrating the impact of trademark activity on VC valuation. This finding also supports and adds to the research on the value of trademarks. Sandner and Block (2011) and Greenhalgh and Rogers (2006a,b) demonstrate that trademarks have a positive effect on the market valuations of large publicly listed technology firms. Our study shows that trademarks are associated with not only the market valuations of large firms but also VCs' financial valuations of start-ups. Furthermore, our findings on the effects of trademarks add to the research on entrepreneurial marketing and its effect on VC evaluations. The literature has repeatedly highlighted the relevance of the market orientations of start-ups for VC financing and valuation decisions. Both Hisrich (1989) and Wortman et al. (1989) note that VCs consistently rate marketing management as an important success factor. Failure rates of start-ups could be greatly reduced through improved analysis of the target market (see also Hills and LaForge, 1992; Hills et al., 2008). More recent research on this topic analyzes the marketing readiness of start-ups as one of the primary dimensions on which VCs base their investment decisions (Douglas and Shepherd, 2002). VCs are more willing to finance start-ups with high marketing readiness because they appear to be more credible and ‘investor ready’ (Wright et al., 2004). Whereas these arguments already indicated the importance of market orientation for VC financing, to the best of our knowledge, we are the first to show that a start-up's advancement in marketing activities as reflected by its trademark activity is a relevant characteristic that is valued by VCs.

Our results demonstrate that IPRs in the form of trademarks relate to social startup development. As such, we find similarities to prior research on traditional startup development (e.g., Helmers and Rogers, 2010; Srinivasan et al., 2008). However, we find that there are particularities in trademarks for the development of hybrid outcomes of social startups.

First, our findings reveal that trademark registrations within the first three years of a social startup’s existence relate to their economic outcomes. Thus, we find that in addition to the receipt of grants and the experience of a social entrepreneur (Lall and Park, 2020; Rey-Martí et al., 2016), IPRs relate to the employment growth of social startups. Social startups seem to benefit from a signaling and appropriation value that has been identified by prior trademark research on traditional startups (e.g., Flikkema et al., 2014; Hsu et al., 2021; Malmberg, 2005). The signaling value of trademarks can help startups obtain access to external financial and nonfinancial support (e.g., Yang et al., 2020); also, trademarks enable them to have stronger commercialization of their products and services (Hsu et al., 2021). Furthermore, the signaling effect of trademarks can serve as a valuable sign for the growth ambitions of a social startup, which in the next step can lead to higher realized growth (e.g., Delmar and Wiklund, 2008). In turn, trademark registrations foster the economic development of social startups. However, our further analysis indicates that the number of registered trademarks is important to indicate top economic outcomes of social startups. This finding is in line with Block et al. (2014), who show that the number of trademarks affects startup valuations by venture capitalists.

The Clorox decision demonstrates that, when interpreting the use requirement to prove the validity of a federal registration, the courts follow a literal interpretation of the Act, which mandates a “sale” or “transportation” of the goods bearing the mark. It is this note’s contention that the stringent interpretation of the open and notorious public use aspect of the sale or transportation requirement is inequitable, as it makes it more difficult for trademark owners who operate in industries with high barriers to entry to prove use. In Clorox, the manner in which the sale or transportation requirement was interpreted required the Board to disregard evidence of bona fide use relating to both the applicant’s internet advertising, via its website, and an interstate shipment of parts of the applicant’s products bearing the mark. The shipment of parts of the applicant’s products from Utah to Mexico would have posed no difficulty satisfying the jurisdictional prerequisite that the use in question have an effect on commerce with foreign nations. Thus, inequity follows where the Act requires invalidation of a federal registration in circumstances where an applicant is making a bona fide use of a mark, and significantly investing in its brand, in order to obtain an initial sale. If sales are typically infrequent in a given industry, a sale is going to be unquestionably harder to make in that industry. It logically follows that a trademark owners’ ability to satisfy the sale or transportation requirement of the Act is more burdensome where the standard of use dictates a sale must be made, but the trademark owner’s evidence of its open and public use of the mark in connection with obtaining a sale (which would likely be building some public association between the mark and the good) is disregarded. Consequently, the use requirement is unequal in its application as the sale or transportation requirement does not factor in that sales are more difficult to attain in certain industries.

The Act’s legislative history expresses the desire of Congress to interpret use in commerce flexibly, and to accommodate use that is typical in a particular industry. However, Congress’ desire for a flexible interpretation focuses on use of a mark made after an initial sale or transportation of the good bearing that mark. The need for a flexible interpretation of use equally applies to uses that may be made of a mark before an initial sale or transportation of the good has been made. Trademark owners would receive greater protection and certainty if the Act stipulated a more flexible commercial standard of use that incorporated open and public pre-sales use of a mark (that is, use occurring before an initial sale or transportation takes place) as sufficient. This is especially important where applicants, like the applicant in Clorox, operate in an industry where products are high-end, custom made goods and therefore sales are, by their inherent nature, more infrequent and difficult to obtain. As the system currently stands, injustice results to the extent that the law requires businesses to invest in a mark to secure a sale, only to have a registration invalidated at a later date merely because the trademark owner had not actually attained any sales.

While it may seem like these new AI tools can conjure new material from the ether, that’s not quite the case. Generative AI platforms are trained on data lakes and question snippets — billions of parameters that are constructed by software processing huge archives of images and text. The AI platforms recover patterns and relationships, which they then use to create rules, and then make judgments and predictions, when responding to a prompt. This process comes with legal risks, including intellectual property infringement. In many cases, it also poses legal questions that are still being resolved. For example, does copyright, patent, trademark infringement apply to AI creations? Is it clear who owns the content that generative AI platforms create for you, or your customers? Before businesses can embrace the benefits of generative AI, they need to understand the risks — and how to protect themselves.

Where Generative AI Fits into Today’s Legal Landscape

Some technologies narrow the gap between the most and least technologically advanced societies. Nuclear weapons, for instance, allow relative pipsqueaks like North Korea to offset the military and economic advantages a superpower and its allies possess. Others widen the divide: In the 19th century, repeating rifles, machine guns and steamships allowed European societies to subjugate vast areas of the world.

In some respects, AI will empower the weak. US officials worry that large language models might help terrorists with crude science kits to build biological weapons. Rogue states, like Iran, might use AI to coordinate drone swarms against US warships in the Persian Gulf. More benignly, AI could expand access to basic healthcare services in the Global South, creating big payoffs in increased life expectancy and economic productivity.

Consider one assertion — that artificial intelligence will make conflict more lethal and harder to constrain. Analysts envision a future in which machines can pilot fighter jets more skillfully than humans, AI-enabled cyberattacks devastate enemy networks, and advanced algorithms turbocharge the speed of decisions. Some warn that automated decision-making could trigger rapid-fire escalation — even nuclear escalation — that leaves policymakers wondering what happened. If war plans and railway timetables caused World War I, perhaps AI will cause World War III.

That AI will change warfare is undeniable. From enabling predictive maintenance of hardware to facilitating astounding improvements in precision targeting, the possibilities are profound. A single F-35, quarterbacking a swarm of semiautonomous drones, could wield the firepower of an entire bomber wing. As the National Security Commission on Artificial Intelligence concluded in 2021, a “new era of conflict” will be dominated by the side that masters “new ways of war.”

“Over the last several years, there have been countless studies, investigations, and litigation by the DOJ and the FTC in the past two administrations that have found anti-competitive conduct by the monopoly distributors of news content,” Danielle Coffey, president and CEO of trade association News Media Alliance, said at the hearing. “This marketplace imbalance will only be increased by [generative AI].”

Generative AI systems—ones that are capable of generating text, images, or other media—must be trained on vast amounts of data. In order to secure access to high-quality text data, prominent AI developer OpenAI has partnered with the Associated Press, a U.S.-based nonprofit news agency, gaining access to part of AP’s archive in exchange for use of OpenAI’s products. OpenAI has a similar partnership with Axel Springer, a German multinational media company, as part of which ChatGPT will summarize articles by Axel Springer-owned news outlets and provide links and attribution.

Media mogul Barry Diller warned on Wednesday that artificial intelligence (AI) could be as “destructive” to news publishers as free online news was in the early aughts.

Speaking at the Sir Harry Evans Global Summit in Investigative Journalism, Diller, who co-founded Fox Broadcasting Company and is now chairman of publishing giant IAC, said he was teaming up with News Corp. CEO Robert Thomson and German publisher Axel Springer to protect news publishers from the threat of AI.

For most experts, the biggest questions concerning AI and copyright relate to the data used to train these models. Most systems are trained on huge amounts of content scraped from the web; be that text, code, or imagery. The training dataset for Stable Diffusion, for example — one of the biggest and most influential text-to-AI systems — contains billions of images scraped from hundreds of domains; everything from personal blogs hosted on WordPress and Blogspot to art platforms like DeviantArt and stock imagery sites like Shutterstock and Getty Images. Indeed, training datasets for generative AI are so vast that there’s a good chance you’re already in one (there’s even a website where you can check by uploading a picture or searching some text). The justification used by AI researchers, startups, and multibillion-dollar tech companies alike is that using these images is covered (in the US, at least) by fair use doctrine, which aims to encourage the use of copyright-protected work to promote freedom of expression. 

When deciding if something is fair use, there are a number of considerations, explains Daniel Gervais, a professor at Vanderbilt Law School who specializes in intellectual property law and has written extensively on how this intersects with AI. Two factors, though, have “much, much more prominence,” he says. “What’s the purpose or nature of the use and what’s the impact on the market.” In other words: does the use-case change the nature of the material in some way (usually described as a “transformative” use), and does it threaten the livelihood of the original creator by competing with their works?

Artificial intelligence (AI) is becoming more prevalent in battlegrounds. Like industries and businesses, the military is also day by day starting to be more AI-focused when it comes to advancement and development. AI-powered military systems can process large volumes of data more efficiently than conventional systems. Additionally, AI enhances fighting systems’ self-control, self-regulation, and self-actuation through innate calculation and decision-making abilities. Practically every military application involves artificial intelligence, and growing military support for innovative and advanced AI technologies is anticipated to increase the demand for AI-driven systems in the military [65]. As illustrated in Figure 10, this part of the paper largely focuses on the AI capabilities important to military operations for simplicity and their applications in defense sectors. The Cuban Missile Crisis during the Cold War, which nearly resulted in the nuclear war, provides the finest illustration of the impact and exceptional significance of autonomous weapon systems (AWS) and target recognition [66]. This one piece of technology–the US Naval Forces’ Autonomous Weapons System and Target Recognition–could have prevented this scenario. The United States and the Soviet Union focused more on deterrence and wartime stability after learning from the outcome of the conflict, such as by making AI-controlled military decisions less predictable and achieving machine-like accuracy. World Wars initiated the application of AI in the defense sector, but it started progressing in situations like the above. Increased investments in uninhabited ground vehicles (UGV) like the Russian Platform-M and AWS like the MQ-9 Reaper (Figure 11(a)) resulted from these advancements. In these technologies, a drone pilot’s sensor system for weapon detection and launch may even be the same as a pilot’s sensor system in the cockpit of an inhabited aircraft [71].

As for the expectations concerning AI in military application over the next decade, some of its techniques will define or re-define key advanced military technologies. AI solutions will be first of all intelligent, exploiting integration of AI knowledge-focused analytic capabilities. Then the AI solutions will be interconnected to utilize the network of virtual and physical domains, including sensors, organizations, individuals and autonomous agents, also exploiting the advantage of blockchain technology for data integrity. They will be distributed to utilize decentralized and ubiquitous largescale sensor networks, storage, and computation. And last but not least, they will be digitally intermingling human, physical and information domains to support novel disruptive effects (NATO Science & Technology Organization, 2020).

“The Pentagon is figuring ways to harness artificial intelligence (AI) for advantages as far-flung as battlespace autonomy, intelligence analysis, record tracking, predictive maintenance and military medicine. AI is a key growth investment area for DoD, with nearly $1 billion allocated in the 2020 budget. The Defense Department’s Joint Artificial

The United States government is leading global efforts to build strong norms that will promote the responsible military use of artificial intelligence and autonomous systems. Last week, the State Department announced that 47 states have now endorsed the "Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy" that the government first launched at The Hauge on Feb. 16.

AI refers to the ability of machines to perform tasks that would otherwise require human intelligence, such as recognizing patterns, learning from experience, drawing conclusions, making predictions or generating recommendations.

The definition of regulation as intervention in the private domain is quite old. Already in the mid-nineteenth century, John Stuart Mill casually used the word “regulation” to describe “governmental intervention in the affairs of society” and laws that implement such intervention.23 Mill argued that “[n]o subject has been more keenly contested in the present age” than “the limits of the province of government.” He posited that the source of controversy was largely an ideological divide between two groups in society—”the supporters of interference [who believe the government should act] wherever its intervention would be useful” and the “laissez-faire school [that believes] the province of government [should be] restricted . . . to the protection of person and property against force and fraud.”24 Reflecting on such beliefs, Mill pointed out that “on every subject on which difference of opinion is possible, the truth depends on a balance to be struck between two sets of conflicting reasons. . .. [W]e do not [always] understand the grounds of our opinion. But when we turn to . . . complicated [subjects], to morals, religion, politics, social relations, and the business of life, three-fourths of the arguments for every disputed opinion consist in dispelling the appearances which favor some opinion different from it.”25 Mill’s account of perceptions of regulation and their formation is timeless.

In addition to the “bans and restrictions” monitored by the customs authorities, businesses face many other restrictions in their day-to-day operations. This includes regulations that fall under the purview of other government agencies as well as company-specific restrictions. An example: Company A instructs its shipping department not to supply hydraulic pumps to customers in Japan, even though that would not be a problem under foreign trade regulations. The reason: Company A has signed an exclusivity agreement with a Japanese company that includes territorial protections in this area.

Lawson '23 [Max; co-chair of the People’s Vaccine Alliance and head of inequality policy at Oxfam International; 12-20-2023; "Treating green tech like COVID vaccines will only benefit the rich"; POLITICO; https://www.politico.eu/article/treating-green-tech-transfer-covid-19-vaccines-will-only-benefit-the-rich-cop-28-dubai-climate-change-oxfam-un/; accessed 7-28-2024, AY]

This year’s United Nations Climate Change Conference COP28 — the global climate summit hosted by a petrostate — has ended with a grossly inadequate set of commitments that won’t come close to ending fossil fuel extraction. And, in its wake, there’s already another scandal on the horizon.

Facial recognition is a threat to privacy, racial justice, free expression, and information security. EFF supports strict restrictions on face recognition use by private companies, and total bans on government use of the technology. Face recognition in all of its forms, including face scanning and real-time tracking, pose threats to civil liberties and individual privacy. “False positive” error rates are significantly higher for women, children, and people of color, meaning face recognition has an unfair discriminatory impact. Coupled with the fact that cameras are over-deployed in neighborhoods with immigrants and people of color, spying technologies like face surveillance serve to amplify existing disparities in the criminal justice system.

Across the nation local communities from San Francisco to Boston have moved to ban government use of facial recognition. In New York, Electronic Frontier Alliance member Surveillance Technology Oversight Project (S.T.O.P.) is at the forefront of this movement. Recently we got the chance to speak with them about their efforts and what people can do to help advance the cause. S.T.O.P. is a New York-based civil rights and privacy organization that does research, advocacy, and litigation around issues of surveillance technology abuse.

Rite Aid has been banned from using facial recognition technology for five years over allegations that its surveillance system was used incorrectly to identify potential shoplifters, especially Black, Latino, Asian or female shoppers.

The settlement with the Federal Trade Commission addresses charges that the struggling drugstore chain didn’t do enough to prevent harm to its customers and implement “reasonable procedures,” the government agency said.

The Senate introduced bipartisan legislation this week that would ban the use of facial recognition technology and the collection of facial biometric data by the Transportation Security Administration in U.S. airports.

The Traveler Privacy Protection Act aims to tackle TSA’s proposed plan to implement facial recognition scans at over 430 U.S. airports within the next several years. The bill was sponsored by Sens. Jeff Merkley, D-Ore., John Kennedy, R-La., Edward Markey, D-Mass., Roger Marshall, R-Kan., Bernie Sanders, I-Vt., and Elizabeth Warren, D-Mass.

Should the EU ban software that can pick a face out of a crowd?

A growing political coalition thinks so — and just received heavyweight support from the third largest group in the EU parliament, where a majority is now in favor of banning facial recognition tech that scans crowds indiscriminately and in real-time.

Drugstore chain Rite Aid has accepted a ban of its use of facial recognition software for five years due to false accusations stemming from the technology that disproportionately affected people of color, the Federal Trade Commission said.

The retailer failed to impose reasonable precautions in its deployment of facial recognition, resulting in thousands of false-positive matches with customers accused of shoplifting and other inappropriate behavior, a legal complaint from the FTC said.