Last month, The New York Times claimed that tech giants OpenAI and Google have waded right into a copyright grey space by transcribing the huge quantity of YouTube movies and utilizing that textual content as further coaching information for his or her AI fashions regardless of phrases of service that prohibit such efforts and copyright regulation that the Times argues locations them in dispute. The Times additionally quoted Meta officers as saying that their fashions will be unable to sustain until they observe OpenAI and Google’s lead. In dialog with reporter Cade Metz, who broke the story, on the New York Times podcast The Daily, host Michael Barbaro referred to as copyright violation “AI’s Original Sin.”
At the very least, copyright seems to be one of many main fronts to date within the battle over who will get to revenue from generative AI. It’s by no means clear but who’s on the suitable facet of the regulation. In the exceptional essay “Talkin’ ’Bout AI Generation: Copyright and the Generative-AI Supply Chain,” Cornell’s Katherine Lee and A. Feder Cooper and James Grimmelmann of Microsoft Research and Yale be aware:
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Copyright regulation is notoriously sophisticated, and generative-AI techniques handle to contact on a terrific many corners of it. They elevate problems with authorship, similarity, direct and oblique legal responsibility, honest use, and licensing, amongst a lot else. These points can’t be analyzed in isolation, as a result of there are connections in every single place. Whether the output of a generative AI system is honest use can depend upon how its coaching datasets have been assembled. Whether the creator of a generative-AI system is secondarily liable can depend upon the prompts that its customers provide.
But it appears much less vital to get into the high quality factors of copyright regulation and arguments over legal responsibility for infringement, and as a substitute to discover the political economic system of copyrighted content material within the rising world of AI companies: Who will get what, and why? And relatively than asking who has the market energy to win the tug of battle, we must be asking, What establishments and enterprise fashions are wanted to allocate the worth that’s created by the “generative AI supply chain” in proportion to the function that numerous events play in creating it? And how will we create a virtuous circle of ongoing worth creation, an ecosystem by which everybody advantages?
Publishers (together with The New York Times itself, which has sued OpenAI for copyright violation) argue that works corresponding to generative artwork and texts compete with the creators whose work the AI was skilled on. In explicit, the Times argues that AI-generated summaries of stories articles are an alternative to the unique articles and harm its enterprise. They need to receives a commission for his or her work and protect their present enterprise.
Meanwhile, the AI mannequin builders, who’ve taken in large quantities of capital, want to discover a enterprise mannequin that can repay all that funding. Times reporter Cade Metz supplies an apocalyptic framing of the stakes and a binary view of the attainable consequence. In his interview in The Daily, Metz opines
a jury or a choose or a regulation ruling in opposition to OpenAI may essentially change the way in which this know-how is constructed. The excessive case is these firms are not allowed to use copyrighted materials in constructing these chatbots. And which means they’ve to begin from scratch. They have to rebuild every thing they’ve constructed. So that is one thing that not solely imperils what they’ve at this time, it imperils what they need to construct sooner or later.
And in his unique reporting on the actions of OpenAI and Google and the inner debates at Meta, Metz quotes Sy Damle, a lawyer for Silicon Valley enterprise agency Andreessen Horowitz, who has claimed that “the only practical way for these tools to exist is if they can be trained on massive amounts of data without having to license that data. The data needed is so massive that even collective licensing really can’t work.”
“The only practical way”? Really?
I suggest as a substitute that not solely is the issue solvable however that fixing it may create a brand new golden age for each AI mannequin suppliers and copyright-based companies. What’s lacking is the suitable structure for the AI ecosystem, and the suitable enterprise mannequin.
Unpacking the Problem
Let’s first break down “copyrighted content.” Copyright reserves to the creator(s) the unique proper to publish and to revenue from their work. It doesn’t defend info or concepts however a singular “creative” expression of these info or concepts. Unique artistic expression is one thing that’s elementary to all human communication. And people utilizing the instruments of generative AI are certainly typically utilizing it as a means to improve their very own distinctive artistic expression. What is definitely in dispute is who will get to revenue from that distinctive artistic expression.
Not all copyrighted content material is created for revenue. According to US copyright regulation, every thing revealed in any kind, together with on the web, is routinely copyrighted by the creator for the lifetime of its creator plus 70 years. Some of that content material is meant to be monetized both by promoting, subscription, or particular person sale, however that isn’t all the time true. While a weblog or social media put up, YouTube gardening or plumbing tutorial, or music or dance efficiency is implicitly copyrighted by its creators (and might also embody copyrighted music or different copyrighted elements), it’s meant to be freely shared. Even content material that’s meant to be shared freely, although, has an expectation of remuneration within the type of recognition and a spotlight.
Those intending to commercialize their content material often point out that in a roundabout way. Books, music, and films, for instance, bear copyright notices and are registered with the copyright workplace (which confers further rights to damages within the occasion of infringement). Sometimes these notices are even machine-readable. Some on-line content material is protected by a paywall, requiring a subscription to entry it. Some content material is marked “noindex” within the HTML code of the web site, indicating that it shouldn’t be spidered by engines like google (and presumably different internet crawlers). Some content material is visibly related to promoting, indicating that it’s being monetized. Search engines “read” every thing they’ll, however official companies usually respect alerts that inform them “no” and don’t go the place they aren’t supposed to.
AI builders absolutely acknowledge these distinctions. As the New York Times article referenced at the beginning of this piece notes, “The most prized data, A.I. researchers said, is high-quality information, such as published books and articles, which have been carefully written and edited by professionals.” It is exactly as a result of this content material is extra beneficial that AI builders search the limitless means to prepare on all out there content material, no matter its copyright standing.
Next, let’s unpack “fair use.” Typical examples of honest use are quotations, replica of a picture for the aim of criticism or remark, parodies, summaries, and in newer precedent, the hyperlinks and snippets that assist a search engine or social media consumer to determine whether or not to eat the content material. Fair use is mostly restricted to a portion of the work in query, such that the reproduced content material can’t function an alternative to the unique work.
Once once more it’s mandatory to make distinctions that aren’t authorized however sensible. If the long-term well being of AI requires the continuing manufacturing of rigorously written and edited content material—because the foreign money of AI data actually does—solely essentially the most short-term of enterprise benefit could be discovered by drying up the river AI firms drink from. Facts aren’t copyrightable, however AI mannequin builders standing on the letter of the regulation will discover chilly consolation in that if information and different sources of curated content material are pushed out of enterprise.
An AI-generated evaluate of Denis Villeneuve’s Dune or a plot abstract of the novel by Frank Herbert on which it’s primarily based is not going to hurt the manufacturing of recent novels or motion pictures. But a abstract of a information article or weblog put up may certainly be a ample substitute. If information and different types of high-quality, curated content material are vital to the event of future AI fashions, AI builders must be wanting onerous at how they’ll affect the longer term well being of those sources.
The comparability of AI summaries with the snippets and hyperlinks supplied previously by engines like google and social media websites is instructive. Google and others have rightly identified that search drives site visitors to websites, which the websites can then monetize as they’ll, by their very own promoting (or promoting in partnership with Google), by subscription, or simply by the popularity the creators obtain when folks discover their work. The incontrovertible fact that when given the selection to decide out of search, only a few websites select to achieve this supplies substantial proof that, a minimum of previously, copyright house owners have acknowledged the advantages they obtain from search and social media. In reality, they compete for larger visibility by SEO and social media advertising.
But there may be actually cause for internet publishers to worry that AI-generated summaries is not going to drive site visitors to websites in the identical means as extra conventional search or social media snippets. The summaries supplied by AI are much more substantial than their search and social media equivalents, and in circumstances corresponding to information, product search, or a seek for factual solutions, a abstract could present an affordable substitute. When readers see an AI reply that references sources they belief, they might nicely take it at face worth and transfer on. This must be of concern not solely to the websites that used to obtain the site visitors however to those who used to drive it. Because in the long run, if folks cease creating high-quality content material to ingest, the entire ecosystem breaks down.
This isn’t a battle that both facet must be wanting to “win.” Instead, it’s a chance to suppose by how to strengthen two public items. Journalism professor Jeff Jarvis put it nicely in a response to an earlier draft of this piece: “It is in the public good to have AI produce quality and credible (if ‘hallucinations’ can be overcome) output. It is in the public good that there be the creation of original quality, credible, and artistic content. It is not in the public good if quality, credible content is excluded from AI training and output OR if quality, credible content is not created.” We want to obtain each targets.
Finally, let’s unpack the relation of an AI to its coaching information, copyrighted or uncopyrighted. During coaching, the AI mannequin learns the statistical relationships between the phrases or pictures in its coaching set. As Derek Slater has identified, very similar to musical chord progressions, these relationships could be seen as “basic building blocks” of expression. The fashions themselves don’t include a replica of the coaching information in any human-recognizable kind. Rather, they’re a statistical illustration of the chance, primarily based on the coaching information, that one phrase will observe one other or in a picture, that one pixel might be adjoining to one other. Given sufficient information, these relationships are remarkably sturdy and predictable, a lot in order that it’s attainable for generated output to carefully resemble or duplicate components of the coaching information.
It is actually price realizing what content material has been ingested. Mandating transparency in regards to the content material and supply of coaching datasets—the generative AI provide chain—would go a great distance in the direction of encouraging frank discussions between disputing events. But specializing in examples of inadvertent resemblances to the coaching information misses the purpose.
Generally, whether or not fee is in foreign money or in recognition, copyright holders search to withhold information from coaching as a result of it appears to them that could be the one means to stop unfair competitors from AI outputs or to negotiate a charge to be used of their content material. As we noticed from internet search, “reading” that doesn’t produce infringing output, delivers visibility (site visitors) to the originator of the content material, and preserves recognition and credit score is mostly tolerated. So AI firms must be working to develop options that content material builders will see as beneficial to them.
The current protest by longtime Stack Overflow contributors who don’t need the corporate to use their solutions to prepare OpenAI fashions highlights an extra dimension of the issue. These customers contributed their data to Stack Overflow; giving the corporate perpetual and unique rights to their solutions. They reserved no financial rights, however they nonetheless consider they’ve ethical rights. They had, and proceed to have, the expectation that they’ll obtain recognition for his or her data. It isn’t the coaching per se that they care about, it’s that the output could not give them the credit score they deserve.
And lastly, the Writers Guild strike established the contours of who will get to profit from by-product works created with AI. Are content material creators entitled to be those to revenue from AI-generated derivatives of their work, or can they be made redundant when their work is used to prepare their replacements? (More particularly, the settlement stipulated that AI works couldn’t be thought of “source material.” That is, studios couldn’t have the AI do a primary draft, then deal with the scriptwriter as somebody merely “adapting” the draft and thus get to pay them much less.) As the settlement demonstrated, this isn’t a purely financial or authorized query however one in all market energy.
In sum, there are three components to the issue: what content material is ingested as a part of the coaching information within the first place, what outputs are allowed, and who will get to revenue from these outputs. Accordingly, listed here are some pointers for the way AI mannequin builders ought to deal with copyrighted content material:
- Train on copyrighted content material that’s freely out there, however respect alerts like subscription paywalls, the robots.txt file, the HTML “noindex” key phrase, phrases of service, and different means by which copyright holders sign their intentions. Make the trouble to distinguish between content material that’s meant to be freely shared and that which is meant to be monetized and for which copyright is meant to be enforced.
There is a few progress in the direction of this objective. In half due to the EU AI Act, it’s probably that inside the subsequent 12 months each main AI developer could have carried out mechanisms for copyright holders to decide out in a machine-readable means. Already, OpenAI permits websites to disallow its GPTBot internet crawler utilizing the robots.txt file, and Google does the identical for its web-extended crawler. There are additionally efforts just like the Do Not Train database, and instruments like Cloudflare Bot Manager. OpenAI’s forthcoming Media Manager guarantees to “enable creators and content owners to tell us what they own and specify how they want their works to be included or excluded from machine learning research and training.” This is useful however inadequate. Even on at this time’s web these mechanisms are fragile and complicated, change continuously, and are sometimes not nicely understood by websites whose content material is being scraped.
But extra importantly, merely giving content material creators the suitable to decide out is lacking the true alternative, which is to assemble datasets for coaching AI that particularly acknowledge copyright standing and the targets of content material creators, and thus grow to be the underlying mechanism for a brand new AI economic system. As Dodge, the hypersuccessful recreation developer who’s the protagonist of Neal Stephenson’s novel Reamde famous, “You had to get the whole money flow system figured out. Once that was done, everything else would follow.”
- Produce outputs that respect what could be recognized in regards to the supply and the character of copyright within the materials.
This isn’t dissimilar to the challenges of stopping many different kinds of disputed content material, corresponding to hate speech, misinformation, and numerous different kinds of prohibited info. We’ve all been instructed many occasions that ChatGPT or Claude or Llama 3 isn’t allowed to reply a specific query or to use explicit info that it could in any other case give you the option to generate as a result of it could violate guidelines in opposition to bias, hate speech, misinformation, or harmful content material. And, in reality, in its feedback to the copyright workplace, OpenAI describes the way it supplies comparable guardrails to preserve ChatGPT from producing copyright-infringing content material. What we’d like to know is how efficient they’re and the way extensively they’re deployed.
There are already methods for figuring out the content material most carefully associated to some kinds of consumer queries. For instance, when Google or Bing supplies an AI-generated abstract of an internet web page or information article, you sometimes see hyperlinks beneath the abstract that time to the pages from which the abstract was generated. This is finished utilizing a know-how referred to as retrieval-augmented era (RAG), which generates a set of search outcomes which might be vectorized, offering an authoritative supply to be consulted by the mannequin earlier than it generates a response. The generative LLM is alleged to have grounded its response within the paperwork supplied by these vectorized search outcomes. In essence, it’s not regurgitating content material from the pretrained fashions however relatively reasoning on these supply snippets to work out an articulate response primarily based on them. In brief, the copyrighted content material has been ingested, however it’s detected in the course of the output part as a part of an general content material administration pipeline. Over time, there’ll probably be many extra such methods.
One hotly debated query is whether or not these hyperlinks present the identical degree of site visitors because the earlier era of search and social media snippets. Google claims that its AI summaries drive much more site visitors than conventional snippets, but it surely hasn’t supplied any information to again up that declare, and could also be basing it on a really slender interpretation of click-through charge, as parsed in a current Search Engine Land evaluation. My guess is that there might be some winners and a few losers as with previous search engine algorithm updates, not to point out additional updates, and that it’s too early for websites to panic or to sue.
But what’s lacking is a extra generalized infrastructure for detecting content material possession and offering compensation in a common goal means. This is among the nice enterprise alternatives of the following few years, awaiting the sort of breakthrough that pay-per-click search promoting introduced to the World Wide Web.
In the case of books, for instance, relatively than coaching on recognized sources of pirated content material, how about constructing a guide information commons, with an extra effort to protect details about the copyright standing of the works it comprises? This commons might be used as the idea not just for AI coaching however for measuring the vector similarity to present works. Already, AI mannequin builders use filtered variations of the Common Crawl Database, which supplies a big proportion of the coaching information for many LLMs, to scale back hate speech and bias. Why not do the identical for copyright?
- Pay for the output, not the coaching. It could appear like an enormous win for present copyright holders after they obtain multimillion-dollar licensing charges for using content material they management. First, solely essentially the most deep-pocketed AI firms might be in a position to afford preemptive funds for essentially the most beneficial content material, which can deepen their aggressive moat with regard to smaller builders and open supply fashions. Second, these charges are probably inadequate to grow to be the muse of sustainable long-term companies and inventive ecosystems. Once you’ve licensed the rooster, the licensee will get the eggs. (Hamilton Nolan calls it “selling your house for firewood.”) Third, the fee is usually going to intermediaries and isn’t handed on to the precise creators.
How “payment” works may rely very a lot on the character of the output and the enterprise mannequin of the unique copyright holder. If the copyright house owners want to monetize their very own content material, don’t present the precise outputs. Instead, present pointers to the supply. For content material from websites that depend upon site visitors, this implies sending both site visitors or, if not, a fee negotiated with the copyright proprietor that makes up for the proprietor’s decreased means to monetize its personal content material. Look for win-win incentives that can lead to the event of an ongoing, cooperative content material ecosystem.
In some ways, YouTube’s Content ID system supplies an intriguing precedent for the way this course of may be automated. According to YouTube’s description of the system,
Using a database of audio and visible recordsdata submitted by copyright house owners, Content ID identifies matches of copyright-protected content material. When a video is uploaded to YouTube, it’s routinely scanned by Content ID. If Content ID finds a match, the matching video will get a Content ID declare. Depending on the copyright proprietor’s Content ID settings, a Content ID declare ends in one of many following actions:
- Blocks a video from being considered
- Monetizes the video by operating adverts in opposition to it and generally sharing income with the uploader
- Tracks the video’s viewership statistics
(Revenue is just generally shared with the uploader as a result of the uploader could not personal the entire monetizable components of the uploaded content material. For instance, a dance or music efficiency video could use copyrighted music for which fee goes to the copyright holder relatively than the uploader.)
One can think about this sort of copyright enforcement framework being operated by the platforms themselves, a lot as YouTube operates Content ID, or by third-party companies. The downside is clearly harder than the one dealing with YouTube, which solely had to uncover matching music and movies in a comparatively fastened format, however the instruments are extra subtle at this time. As RAG demonstrates, vector databases make it attainable to discover weighted similarities even in wildly completely different outputs.
Of course, there’s a lot that would wish to be labored out. Using vector similarity for attribution is promising, however there are regarding limitations. Consider Taylor Swift. She is so widespread that there are a lot of artists attempting to sound like her. This units up a sort of adversarial state of affairs that has no apparent resolution. Imagine a vector database that has Taylor in it together with a thousand Taylor copycats. Now think about an AI-generated tune that “sounds like Taylor.” Who will get the income? Is it the highest 100 nearest vectors (99 of that are low cost copycats of Taylor)? Or ought to Taylor herself get many of the income? There are attention-grabbing questions in how to weigh similarity—simply as there are attention-grabbing questions in conventional search about how to weigh numerous elements to give you the “best” end result for a search question. Solving these questions is the modern (and aggressive) frontier.
One possibility may be to retrieve the uncooked supplies for era (versus utilizing RAG for attribution). Want to generate a paragraph that appears like Stephen King? Explicitly retrieve some illustration of Stephen King, generate from it, after which pay Stephen King. If you don’t need to pay for Stephen King’s degree of high quality, high quality. Your textual content might be generated from lower-quality bulk-licensed “horror mystery text” as your driver. There are some relatively naive assumptions on this superb, particularly in how to scale it to thousands and thousands or billions of content material suppliers, however that’s what makes it an attention-grabbing entrepreneurial alternative. For a star-driven media space like music, it positively is smart.
My level is that one of many frontiers of innovation in AI must be in methods and enterprise fashions to allow the sort of flourishing ecosystem of content material creation that has characterised the online and the net distribution of music and video. AI firms that determine this out will create a virtuous flywheel that rewards content material creation relatively than turning the business into an extractive useless finish.
An Architecture of Participation for AI
One factor that makes copyright appear intractable is the race for monopoly by the massive AI suppliers. The structure that lots of them appear to think about for AI is a few model of “one ring to rule them all,” “all your base are belong to us,” or the Borg. This structure isn’t dissimilar to the mannequin of early on-line info suppliers like AOL and the Microsoft Network. They have been centralized and aimed to host everybody’s content material as a part of their service. It was solely a query of who would win essentially the most customers and host essentially the most content material.
The World Wide Web (and the underlying web itself) had a essentially completely different thought, which I’ve referred to as an “architecture of participation.” Anyone may host their very own content material, and customers may surf from one website to one other. Every web site and each browser may talk and agree on what could be seen freely, what’s restricted, and what should be paid for. It led to a exceptional enlargement of the alternatives for the monetization of creativity, publishing, and copyright.
Like the networked protocols of the web, the design of Unix and Linux programming envisioned a world of cooperating applications developed independently and assembled right into a larger entire. The Unix/Linux filesystem has a easy however highly effective set of entry permissions with three ranges: consumer, group, and world. That is, some recordsdata are personal solely to the creator of the file, others to a delegated group, and others are readable by anybody.
Imagine with me, for a second, a world of AI that works very similar to the World Wide Web or open supply techniques corresponding to Linux. Foundation fashions perceive human prompts and might generate all kinds of content material. But they function inside a content material framework that has been skilled to acknowledge copyrighted materials and to know what they’ll and might’t do with it. There are centralized fashions which have been skilled on every thing that’s freely readable (world permission), others which might be grounded in content material belonging to a particular group (which may be an organization or different group, a social, nationwide or language group, or every other cooperative aggregation), and others which might be grounded within the distinctive corpus of content material belonging to a person.
It could also be attainable to construct such a world on high of ChatGPT or Claude or any one of many massive centralized fashions, however it’s much more probably to emerge from cooperating AI companies constructed with smaller, distributed fashions, a lot as the online was constructed by cooperating internet servers relatively than on high of AOL or the Microsoft Network. We are instructed that open supply AI fashions are riskier than massive centralized ones, but it surely’s vital to make a clear-eyed evaluation of their advantages versus their dangers. Open supply higher allows not solely innovation however management. What if there was an open protocol for content material house owners to open up their repositories to AI search suppliers however with management and forensics over how that content material is dealt with and particularly monetized?
Many creators of copyrighted content material might be blissful to have their content material ingested by centralized, proprietary fashions and used freely by them, as a result of they obtain many advantages in return. This is very similar to the way in which at this time’s web customers are blissful to let centralized suppliers accumulate their information, so long as it’s used for them and never in opposition to them. Some creators might be blissful to have the centralized fashions use their content material so long as they monetize it for them. Other creators will need to monetize it themselves. But will probably be a lot more durable for anybody to make this selection freely if the centralized AI suppliers are in a position to ingest every thing and to output probably infringing or competing content material with out compensation or with compensation that quantities to pennies on the greenback.
Can you think about a world the place a query to an AI chatbot may generally lead to a direct reply, generally to the equal of “I’m sorry, Dave, I’m afraid I can’t do that” (a lot as you now get instructed once you attempt to generate prohibited speech or pictures, however on this case, due to copyright restrictions), and at others, “I can’t do that for you, Dave, but the New York Times chatbot can.” At different occasions, by settlement between the events, a solution primarily based on copyrighted information may be given straight within the service, however the rights holder might be compensated.
This is the character of the system that we’re constructing for our personal AI companies at O’Reilly. Our on-line know-how studying platform is a market for content material supplied by lots of of publishers and tens of hundreds of authors, trainers, and different consultants. A portion of consumer subscription charges is allotted to pay for content material, and copyright holders are compensated primarily based on utilization (or in some circumstances, primarily based on a set charge).
We are more and more utilizing AI to assist our authors and editors generate content material corresponding to summaries, translations and transcriptions, take a look at questions, and assessments as a part of a workflow that entails editorial and subject-matter knowledgeable evaluate, a lot as once we edit and develop the underlying books and movies. We’re additionally constructing dynamically generated user-facing AI content material that additionally retains monitor of provenance and shares income with our authors and publishing companions.
For instance, for our “Answers” function (inbuilt partnership with Miso), we’ve used a RAG structure to construct a analysis, reasoning, and response mannequin that searches throughout content material for essentially the most related outcomes (comparable to conventional search) after which generates a response tailor-made to the consumer interplay primarily based on these particular outcomes.
Because we all know what content material was used to produce the generated reply, we’re in a position to not solely present hyperlinks to the sources used to generate the reply but additionally pay authors in proportion to the function of their content material in producing it. As Lucky Gunasekara, Andy Hsieh, Lan Le, and Julie Baron write in “The R in ‘RAG’ Stands for ‘Royalties’”:
In essence, the newest O’Reilly Answers launch is an meeting line of LLM employees. Each has its personal discrete experience and ability set, they usually work collectively to collaborate as they absorb a query or question, cause what the intent is, analysis the attainable solutions, and critically consider and analyze this analysis earlier than writing a citation-backed grounded reply…. The web result’s that O’Reilly Answers can now critically analysis and reply questions in a a lot richer and extra immersive long-form response whereas preserving the citations and supply references that have been so vital in its unique launch….
The latest Answers launch is once more constructed with an open supply mannequin—on this case, Llama 3….
The good thing about establishing Answers as a pipeline of analysis, reasoning, and writing utilizing at this time’s main open supply LLMs is that the robustness of the questions it may reply will proceed to enhance, however the system itself will all the time be grounded in authoritative unique knowledgeable commentary from content material on the O’Reilly studying platform.
When somebody reads a guide, watches a video, or attends a reside coaching, the copyright holder will get paid. Why ought to by-product content material generated with the help of AI be any completely different? Accordingly, now we have constructed instruments to combine AI-generated merchandise straight into our fee system. This strategy allows us to correctly attribute utilization, citations, and income to content material and ensures our continued recognition of the worth of our authors’ and lecturers’ work.
And if we are able to do it, we all know that others can too.