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AI Agents Are a Threat to Travel Businesses. Airbnb Could Still Be a Winner.
For the lodging platform’s stock to thrive in the age of bots, it has to turn repeat customers into loyal ones.
I scaled my Airbnb business from 4 cabins to over 500 properties. Here's what I learned.
Samuel Sévigny cofounded the short-term rental property management company Chaletô with his friends. He shares tips on how to scale a business.
How to run a successful Airbnb: Short-term rental strategies
Rule No. 3: Don't try to appeal to everyone. Mike Savage has built a successful business around understanding what works with short-term rentals.
How to get up to $10,000 for your farm stay startup
Airbnb partners with American Farmland Trust to provide grants to producers who want to add farm stays to their business. Learn more at Oct. 14 webinar.
Airbnb Host Tries To Scam Guest With An AI-Created Image Of A Disgusting Toilet
There was a tiny flaw in the plan: it was bollocks
Surprising things about staying in a tiny home Airbnb on wheels
I spent two nights in a 250-square-foot tiny home on wheels during a trip to Miami. I was surprised by how spacious it felt.
Airbnb names Pepijn Rijvers chief business officer
The former Tripadvisor and Booking.com leader succeeds Dave Stephenson as Airbnb furthers its hotel strategy.
Airbnb announces Pepijn Rijvers as Chief Business Officer
Airbnb announces Pepijn Rijvers as Chief Business Officer
Win In Business By Showing Customers Why Your Product Is Better
A well-designed product gets you into the game. But winning in business means getting others to see your product's merit.
World Cup tourism significantly boosts McKinney Airbnb host's business
Billionaires warned New York would scare off business. Anthropic and Airbnb just made their biggest bets on the city yet
Not only are they expanding their footprint, they’re also doubling headcount. Anthropic said it plans to have more than 1,000 employees by end of year.
Why high earners are rushing to use this vacation-rental tax break
The Big Beautiful Bill made a tax loophole permanent for Airbnb investors. Here's how they're getting the IRS to fund their downpayments.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
What Is Airbnb For, Exactly?
The home-sharing app recently added car rentals, groceries and — perhaps most surprisingly — hotels. Brian Chesky, the co-founder and chief executive, is always thinking about how to redesign it.
Family spent a year in Spain; 3 countries later, they found a new home
Victor Trac traveled with his family while working remotely, renting Airbnbs, and testing out potential hometowns along the way.
Albany County Legislature approves tax on short-term rentals
Legislators approved a new “AirBnb Tax” this week.
‘Renovation Aloha’ Stars Reportedly Fined Thousands for Illegal Airbnbs in Hawaii
Tristyn and Kamohai Kalama are facing legal issues over properties they own.
Airbnb's CEO says AI writes 60% of the company's code
CEO Brian Chesky added that managers are also getting their hands dirty with coding or using Claude Code.
I rented my ranch on Airbnb after losing millions. It taught me resilience.
Brent Phillips bought a Texas ranch for himself and his family. When he lost millions in a stock crash, he began renting it out on Airbnb.
El Pasoan hosting nearly a dozen BTS fans who couldn't find hotels, Airbnb
EL PASO, Texas (KVIA) -- Astrid Lind is a local El Paso resident who kindly made an El Paso miracle for nearly a dozen BTS fans coming from different parts of Mexico. Lind found a Facebook page where several BTS fans were looking for a place to stay this weekend in El Paso for the
How a custom $84,962 tiny home became a luxury Airbnb
Sakura Kishiro, 26, bought a 400-square-foot tiny home and designed "every square inch." It became a popular vacation rental — and a source of income.
Airbnb Externship Shows 3L Isabel Samayoa How Legal Strategy Drives Business Growth - UC Law San Francisco (Formerly UC Hastings)
An externship at Airbnb provided 3L Isabel Samayoa exposure to areas such as contracts, licensing, risk management, and growth strategy, helping her develop the skills and experience to thrive in her future career as a corporate transactions attorney. 3L student Isabel Samayoa reflects on her work with Airbnb’s in-house legal team through UC Law SF’s Corporate Counsel Externship Program. Supported Airbnb’s legal team on commercial contracts, open-source licensing, external partnerships, product counseling, and legal research. Observed how legal advice aligns with business strategy and risk management. Gained exposure to strategic decision-making while preparing for a career in corporate transactions. Q: What did you do in this corporate externship? A: I supported the Airbnb legal team across several functions, including commercial contracts, open-source licensing matters, strategic external partnerships, legal research, real estate matters, internal AI integration, and product counseling. I also gained exposure to corporate structure counseling, litigation, patent counseling, and the company’s broader business strategy. Q: What did you take away from this experience? A: One of the most valuable parts of this experience was learning how a business actually operates. I had never worked on such a large team in the private sector, so I had little exposure to how companies run internally. I learned how lawyers can provide valuable advice to support business goals, seeing how leadership evaluates risk and develops growth strategies. Understanding those priorities allows me to calibrate my legal advice to clients, balancing the client’s goals against the level of risk they are willing to take. Q: How does this experience support your career goals? A: Exploring the anatomy of a successful company was incredibly valuable because I plan to pursue a career as a corporate transactions lawyer. Being in the room with the teams discussing important decisions allowed me to see firsthand the factors that contribute to that success and how lawyers help guide those decisions. Q: Would you recommend UC Law SF and this externship? Why? A: I would absolutely recommend this opportunity. While the team worked on high-stakes matters at a highly successful company, they still created an environment where I felt welcomed. They trusted me with meaningful work and supported me every step of the way. This helped me build confidence in my analytical abilities. Q: What’s your favorite UC Law SF memory so far? A: Some of my favorite UC Law SF memories, aside from my time at Airbnb, involve mentorship. I have benefited from mentorship and have also had the chance to mentor other law students. Watching the students I mentored go on to mentor others has been particularly rewarding. I think mentorship is integral to the legal profession, and it has been inspiring to see that culture thrive at UC Law SF and continue beyond the classroom. The Evidence of Success series highlights UC Law SF students as they share how the College’s opportunities equip them with the experience, skills, and confidence to excel in the legal profession.
Airbnb war policy leaves some Middle East travelers without refunds
Maria Wainer had to cancel her trip to the US after much of the Middle East limited flights last week. Airbnb still denied her a refund.
NYC doesn't have enough hotel rooms for FIFA World Cup, must suspend Airbnb restrictions: business leaders
“There are not enough beds [for the World Cup]... and without additional capacity, hotel prices will surge….and spending will leak to New Jersey and the suburbs,” a coalition of business groups...
Nonprofit transforms former Airbnb office in Old Town into family-focused business incubator
I quit Airbnb and delisted my home after hosting nightmare guests
Wendy Martin's Airbnb property in Dayton, Ohio, made her money and she enjoyed hosting — until three bad experiences with guests made her quit.
BIZBEAT: Entertainment-themed Airbnb opens doors in Jefferson City
A new gaming and entertainment haven is now open for business in Jefferson City, and visitors even have the option to stay the night.
Kvation Founder Speaks on Business, Failure, and Mentorship
In an interview on business mentorship, entrepreneur Yossi Schtroks, founder and owner of Kvation, the fast-growing “Airbnb of the kosher world,” to discuss how a painful business collapse led him to rebuild with focus, mentorship, and stronger emunah.
America's economy is getting swallowed up by middlemen — and it's driving people crazy
The real winners in today's economy are the middlemen — rich intermediary companies like Walmart, Airbnb, and Lyft that connect buyers and sellers.
