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AI Gettin' SaaS-y
00:21:48
The big regulatory guns are out for Grok. Memory chip shortage now hit the Steam Deck. Manus is already coming to your favorite messaging app. Turns out Buy Now Pay Later really works for vacations. And another lengthy AI essay, this time detailing what it’s doing, and has the potential to do to SaaS companies.
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Welcome to the Techbrew. Write home for Tuesday, February 17, 2026. I'm Brad McCullough. Today the big regulatory guns are out for Grok Memory chip shortages now hit the Steam Deck. Manus is already coming to your favorite messaging app. Turns out Buy Now, Pay later really works for vacations and another lengthy AI essay, this time detailing what it's doing and has the potential to do to SaaS companies. Here's what you missed today in the world of tech. Ireland's DPC has launched a large scale inquiry into X over Grok's creation and publication of potentially harmful sexualized images, the latest European probe into that whole kerfuffle. Quoting the ft Ireland's Data Protection Commission, which is responsible for enforcing the EU's General Data Protection Regulation, said late on Monday that it had opened a probe into the creation and publication of potentially harmful sexualized images by GROK that contained or involved the processing of EU user data. The DPC has been engaging with X since media reports first emerged a number of weeks ago concerning the alleged ability of X users to prompt the Rock account on X to generate sexualized images of real people, including children, graham Doyle, DPC Deputy Commissioner, said in a statement on Monday. He added that the commission has commenced a large scale inquiry which will examine X's compliance with some of their fundamental obligations under the GDPR in relation to the matters at hand. X offices in Paris were raided by French and European investigators at the beginning of February as part of a wide ranging investigation into X's algorithms as well as the spread of AI generated sexual abuse material. French prosecutors have summoned Elon Musk and Linda Yaccarino, X's former chief executive, for voluntary interviews in Paris in April. The UK's Information Commissioner's Office also last week announced it was launching a new investigation into X and xai, saying it had serious concerns about Grok's use of personal data and, quote, its potential to produce harmful sexualized image and video content. The EU has already opened a formal investigation into XAI for Grok's spread of sexualized images of women and children under the Block's Digital Services act, which requires big tech platforms to mitigate the spread of illegal and harmful content.
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Is this about to become a rolling big story for the foreseeable future? Valve says the Steam Deck OLED may be intermittently out of stock due to memory and storage shortages. It has been out of stock in the US for a few days now. Quoting the Verge Valve has updated the Steam Deck website to say that the Steam Deck OLED may be out of stock intermittently in some regions due to memory and storage shortages. The PC gaming handheld has been out of stock in the US and other parts of the world for a few days, and thanks to this update, we now know why. The update comes shortly after Valve delayed the Steam Machine, Steam frame and Steam controller from a planned shipping window of early 2026. Because of the memory and storage crunch, we have work to do to land on concrete pricing and launch dates that we confidently announce, being mindful of how quickly the circumstances around both of these things can change, valve said in a post about the announcement from earlier this month. Its goal is to launch that new hardware sometime in the first half of 2026, and the company is working to finalize its plans as soon as possible. Valve's website also notes that the company no longer produces the 256 gigabyte LCD steam deck, a change that the company announced late last year.
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Now I'm going to throw this one in with that one as sort of the inverse of that one. Raspberry PI stock rose as much 42% on Tuesday in a record two day rally amid demand for single board computers to run low cost AI agents like OpenClaw. Quoting Reuters. The stock is still about 50% below a record high hit a year ago, but the rally in the roughly $800 million company has materialized alongside social media buzz that demand for its single board computers could pick up as people buy them to run AI agents such as Openclaw X user Alia Bito Reddit, who has more than 58,000 followers, posted on Monday fun trade IDE RPI, which is raspberry PI claiming buyers have recently begun hoarding the devices because they are far cheaper than $500 plus Apple products. Asked about the share price move, Raspberry PI said there's nothing from the company side beyond what's already in the public domain. In January, Raspberry PI said its 2025 core earnings would be ahead of expectations, but warned its 2026 outlook was clouded by volatility in the supply and pricing of memory.
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Manus has launched Manus agents, allowing users to access Manus directly inside messaging apps, starting with Telegram and coming soon to other platforms, which you would imagine they would do as Manus is now owned by Meta, quoting Silicon Republic after bursting onto the scene last March with its revolutionary AI agent. According to a glowing Forbes report, the Chinese founded Manus is introducing personal agents in messaging apps, starting with Telegram. According to Manus, its in chat agent has full reasoning tools and multi step task execution abilities. Telegram users can conduct research, structure data and make requests entirely through chat. The agent can transcribe voice and understand intent to execute tasks, Manus added. Manus Agents is similar to the Austrian made open source OpenClaw, which was acquired by OpenAI over the weekend. Although this is me jumping in to editorialize, no, they hired the dude anyway. Quoting Again, users can switch between two versions of the AI model, Manus 1.6 Max for tasks requiring deeper reasoning and creativity, and Manus 1.6 Lite for faster everyday tasks. Manus Agents can be initiated on Telegram via a QR code on the platform. The company promises that the AI agent only has access to the messages users send it directly. It cannot see, read or interact with any of your other conversations, groups or contacts, the Meta acquired startup said Manus Agents is available on Telegram across all subscription tiers. In a post on X, Manus co founder Zhang Tao said that the agents will be available on WhatsApp, Line, Slack and Discord Quote very soon. Manus is headquartered in Singapore, but has a Chinese parent company called Butterfly Effect Technology. Late last year, Manus was acquired by Meta for its agency offerings in a deal valued at more than $2 billion. The acquisition came after a $75 million funding round last April that valued the Chinese founded AI startup at $500 million. As per Meta can operate and sell the Manus service as well as integrate it into its own products. However, Manis would still be able to sell its subscriptions through its own app and website. Manus Agents have launched on Telegram first despite Meta owning WhatsApp, a rival messaging platform.
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You know what? Buy Now, Pay later works really well for it turns out vacations. Quoting TechCrunch, Airbnb said on Tuesday that it is launching its Reserve Now, Pay later feature, which lets users secure bookings without immediate payment. Globally. This allows users to cancel their bookings if there is a change of plans without losing money up front. The company launched the feature in the US Last year for domestic travel. Airbnb said that properties with a flexible or moderate cancellation policy are eligible for the upfront reservation. With this option, users get charge closer to their check in date rather than at the time of booking. The feature mirrors Buy Now, Pay later payment plans that have become popular in E commerce, making expensive travel more accessible by spreading out costs. The company noted that since the launch, the feature saw 70% adoption for eligible bookings. During its earnings call for Q4 2025, Airbnb said that the feature helped grow nights booked in the quarter. Reserve Now, Pay later saw significant adoption among eligible guests in Q4. It's also led to longer booking lead times and a mix shift toward larger and tire homes, especially those with four or more bedrooms, contributing to the increase in average daily rate, ellie Mertz, CFO of Airbnb, said during the call. Mertz noted that Airbnb's overall cancellation rate jumped from 16 to 17% for the quarter, and it was higher among customers who use the upfront booking product. However, she said that this was not hugely material relative to the broader cancellations on the platform. Last year, the company surveyed US Travelers along with Focal Data, a London based market research and polling company. Of those surveyed, 60% of participants said that a flexible payment option is important while booking a holiday, and 55% said that they would use a flexible payment option.
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Hey, another essay on AI. This time it's from Nicholas Bustamante, who says that LLMs are dismantling the moats that made vertical SaaS defensible and the market sell off in SaaS names is structurally justified but temporarily exaggerated. Summarizing from his quite lengthy tweet, his argument begins with a simple observation. Vertical software software built specifically for one industry has historically been one of the best businesses in technology. Bloomberg in finance, LexisNexis in legal, Epic in healthcare, Procore in construction, Viva in life sciences. These these companies charge astonishing amounts of money and enjoy retention rates hovering around 95%. Bloomberg terminals cost roughly $25,000 per seat per year. FactSet often exceeds $15,000 per user. Law firms pay thousands per month for research tools customers rarely leave for decades. This model worked because these companies built deep, defensible moats. What large language models are now doing, the author argues, is selectively detonating some of these moats while leaving others intact and understanding which are which is the whole game here. One of the most underestimated sources of defensibility in vertical software, according to Bustamante, was the learned interface. Bloomberg's cryptic keyboard commands, legal research filters, proprietary navigation systems. These weren't intuitive tools. They were languages. Professionals invested years mastering them. That fluency became a switching cost. Saying were a Bloomberg shop wasn't just about data quality. It meant the entire firm had internalized a workflow. Replacing the software meant retraining muscle memory developed over a decade. The interface wasn't cosmetic. It was actually the product. LLMs dissolve that advantage by collapsing every interface into natural language chat. Instead of navigating specialized menus, users simply ask for what they want. The model executes the workflow. The accumulated literacy in a proprietary interface becomes worthless. The cost centers that supported those interfaces, design teams, onboarding staff, customer success managers disappear. If much of the premium pricing rested on interface mastery layered on top of licensed or semi commoditized data, that pricing logic erodes quickly. The same pattern applies to custom workflows and business logic. Vertical software encoded how industries actually functioned. Legal citation networks, financial modeling, assumptions, compliance checks, approval chains. Historically, this logic was embedded in code written by engineers who also understood the domain, a rare and expensive combination. Building that infrastructure took years. LLMs fundamentally changed that equation. Because business logic no longer needs to be hard cod, it can be written as plain language instructions that models execute. A seasoned portfolio manager can encode a discounted cash flow methodology in a markdown document without touching Python. What once required multi year engineering efforts can now be implemented in days. The logic becomes readable, auditable and customizable. And it improves automatically. As the underlying model improves itself, the moat of accumulated workflow complexity shrinks dramatically. Another major pillar of vertical SaaS was making messy public data accessible. SEC filings, case laws, patent databases technically public but practically unusable without specialized parsing and search infrastructure. Companies built enormous scaffolding to structure and query this information. But LLMs now arrive pre trained on these formats. They understand the structure of a 10k, the difference between GAAP and non GAAP metrics, how precedent works and legal reasoning. The model itself becomes the parser. The we made it searchable layer, which justified premium pricing, becomes a commodity capability embedded in the foundation model. The data still exists, but the excess premium collapses. Talent scarcity was another traditional barrier. Building vertical software required engineers who could bridge domain expertise and production code an extremely limited pool of people. LLMs invert that scarcity. Engineering becomes accessible through APIs. Domain experts can translate their knowledge directly into software behavior. The scarce resource shifts from technical implementation to domain expertise, which is far more abundant. The barrier to entry drops sharply. Then there's bundling. The strategy of expanding into adjacent modules to increase switching costs. Also that is weakening. Incumbents historically locked customers in by building ecosystems of complementary tools. But if an AI agent can dynamically orchestrate across multiple providers, the integration layer shifts from the vendor to the agent. Instead of buying the entire Bloomberg suite, an agent could query the cheapest or the best data source for each task. The economic logic of paying for a bundled ecosystem weakens when orchestration becomes trivial. Yet not all moats are collapsing. Some grow stronger proprietary data that cannot be replicated or scraped. Real time trading feeds, exclusive ratings, regulated Credit assessments become more valuable in an AI driven world. If the data is truly scarce, LLMs amplify its importance as a necessary input. The critical distinction is whether the data can be licensed or synthesized elsewhere. If yes, the vendor risks becoming a commodity supplier to AI agents. If no, then the moat might hold. Regulatory and compliance lock in also remains powerful in healthcare, financial reporting and other heavily regulated sectors. Switching systems involves certification hurdles, audit trails and multi year implementations. HIPAA and FDA requirements do not dissolve because a better model exists in these environments. LLM adoption may even lag due to compliance risk reinforcing incumbent positions. Network effects persist as well. Platforms that function as communication layers like Bloomberg's messaging system derive value from participation, not interface design. LLMs do not dissolve those network dynamics. Similarly, software embedded directly in financial transactions, payment processors, settlement systems, loan origination infrastructure remains durable. AI may improve interfaces but does not replace transaction. Rails. System of record status presents a more nuanced case. Being the canonical source of truth for critical business data creates enormous switching costs. LLMs do not immediately threaten this, but agents are quietly building cross platform memory layers. By seeing email documents, messaging and CRM data, agents accumulate a broader contextual record than any single system. Over time, this could erode traditional system of record advantages. Though the shift will be gradual. The cumulative effect is a collapse in barriers to entry where the destroyed moats once dominated in SaaS. Historically, building a competitor to Bloomberg or LexisNexis required hundreds of engineers, massive licensing deals and years of development. Now, small teams leveraging frontier models can replicate much of the functionality in months. Competition does not increase incrementally. It explodes. Instead of three incumbents, there may be hundreds of AI native entrants offering comparable capability at lower cost. Revenue may not vanish overnight due to long enterprise contracts, but valuation multiples compress as markets anticipate erosion in pricing power. The deeper strategic threat comes from a pincer movement from below. AI native startups flood vertical niches from above. Horizontal giants like Microsoft embed AI into their ubiquitous platforms extending into vertical workflows without traditional engineering investment. The stack required to build vertical depth agent frameworks, pluggable data access, domain skills written in text is simple. Software becomes headless with the agent owning the user relationship. The aggregator captures margin data suppliers compete on price. Ultimately, the reckoning is not about vertical SaaS dying wholesale. It's about distinguishing real moats from illusions. Interfaces, encoded workflows and search layers built atop public data are vulnerable. Proprietary data regulatory lock in transaction embedding and network effects remain durable. LLMs do not destroy all defensibility. They expose which advantages were structural and which were artifacts of an era before intelligent agents.
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According to him.
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Nothing more for you today. Talk to you tomorrow.