2026-02-10 18:39:17
Anthropic published a lawyer-related plugin on 30 January, and within four to five days, software stocks had lost billions of dollars. The pace of the collapse left everyone shocked, as the whole industry had been operating on a fundamental misperception of how the AI economy works.
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The narrative that dominated the technological field held that AI companies would build baseline models, which would then serve as a foundation for software companies to design specialized industry-specific tools.
The AI companies would gain through licensing their technology, while the software companies would gain through the sale of completed products to end users, thereby creating a Win/Win ecosystem.
This narrative has attracted substantial capital over the past year.
In December 2025, the valuation of Harvey AI increased 3 times over the previous 12 months, after the company raised $160 million, valuing it at $8 billion, up from $3 billion in February 2025.
Legora, in October, raised $150 million with a valuation of $1.8 billion.
Both companies built on top of Claude by Anthropic (and other top AI companies) via industry-specific interfaces and workflows tailored for law firms.
Two reasons made investors feel that these companies had defendable market positions: 1) they had developed a relationship with prominent law firms; 2) they were aware of the needs of legal professionals and how to integrate with established legal software systems. The engine was simply the basic model; the real value seemed to be all that had been engineered around the basic model.
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Anthropic's January 30 release included plugins for Claude Cowork. Notably, one of these plugins automates legal procedures, including NDAs, regulatory compliance monitoring, and legal briefings. These features are exactly what companies like Harvey and Legora charge their clients thousands of dollars for in order to streamline.
The technical complexity of the plugin did not stand out as particularly impressive, as it is mostly structured prompts and workflow instructions that teach Claude how to handle legal documents.
However, analysts have noted that these plugins are significantly stronger than traditional software because they are agentic; they use specialized sub-agents to execute complex, multi-step legal workflows autonomously.
This simplicity, however, caused market panic. Anthropic has managed to build a competent software assistant with comparatively simple tooling; there is no reason it cannot do the same across all the other verticals in which software companies currently operate as time goes on.
Between the 4th and 5th of February, the market took the poll: Thomson Reuters dropped 19 per cent, RELX dropped 15 per cent, Wolters Kluwer dropped 13 per cent, and LegalZoom dropped 18 per cent, while the Nasdaq dropped 1.4 per cent, and Indian IT-services powerhouses Infosys and Wipro both dropped more than 5 per cent. These were not run-of-the-mill market swings, but an underlying repricing caused by a burst of realization that the premises on which software values were based could be all wrong.
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Morgan Stanley analysts called the development a sign of increasingly fierce competition.
Moreso, there is an underlying worrying trend; if legal software can be disrupted by AI, the same would apply to consulting work and financial services. When companies that were once considered customers of cloud computing are disrupted themselves, the ramifications go well beyond legal technology to the broader technology ecosystem.
The problem is bigger than mere competition, as the software companies felt they were Anthropic's customers, who would license Claude, develop products based on it, and sell those products to their own customers. Anthropic built the entire workflow and made contact with end users, without using any middlemen.
Anthropic released 11 plugins that included sales, finance, marketing, data analysis, and customer support functionalities, each targeting a specific software category, indicating that Anthropic has the framework to move into any vertical market at its discretion.
The companies, which had assumed they were creating sustainable businesses on top of Claude, have found out they were simply renting space that could be repossessed at any time.
The term “SaaSpocalypse” summarizes the existential danger that software firms have been facing.
Can they form the application layer of the AI economy?
If foundation-model companies can create applications just as effectively, the whole SaaS industry will face questions that have previously been dodged.
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The CEO of Harvey, Winston Weinberg, sought to offer reassurance, emphasizing that the global legal market is estimated to be worth 1 trillion and that various actors can survive, thereby avoiding a winner-takes-all atmosphere.
This point had some basis on January 30; however, between February 3-4, it was profoundly doubted by investors who realized that it is not only the question of whether the market is large enough to support several companies, but whether software companies can justify their valuation when the foundation-model companies can replicate their core functionality effortlessly.
The stock market has already provided a first reaction, and now the executives are confronted with some awkward questions about differentiation, defensibility, and long-term viability as they attempt to explain why Anthropic, OpenAI, or Google cannot issue a strand of code that makes entire software companies disappear in one night.
2026-02-10 18:38:49
\ Domain Authority (DA) is one of the most discussed metrics in SEO. While Google has confirmed that it does not use Domain Authority as a direct ranking factor, many websites with higher authority scores tend to perform better in search results.
This creates confusion for many SEO professionals. If Google does not use Domain Authority, why do high-authority websites often rank faster and show more stability? The explanation lies in what Domain Authority represents rather than the metric itself.
Domain Authority is a comparative metric developed by Moz to estimate the overall strength of a website. It is calculated based on several factors, including backlink quantity, backlink quality, and link distribution across the domain.
Although Google does not reference Domain Authority internally, many of the signals used to calculate it overlap with ranking factors that search engines do evaluate, such as link relevance, editorial links, and consistency of external references.
For this reason, Domain Authority is often used as an indicator of website trust rather than a ranking mechanism.
Websites with stronger authority profiles commonly share similar performance characteristics:
These benefits result from long-term credibility signals rather than Domain Authority itself.
Search engines assess context, historical reliability, and source trust when determining rankings. Authority helps reduce uncertainty in this evaluation process.
Backlinks remain one of the strongest contributors to authority development. However, their interpretation has changed over time.
Modern search systems analyze backlinks based on:
A smaller number of high-quality backlinks from relevant sources often provides more SEO value than a large volume of low-quality links.
Content quality plays a major role in determining whether backlinks contribute to authority.
Content that helps build authority usually:
Low-quality or repetitive content is less likely to sustain authority, even with backlinks.
Technical SEO and user experience do not directly increase authority, but they influence how effectively authority signals are retained.
Websites that are:
are more likely to maintain trust signals. Technical problems can weaken the impact of otherwise strong authority signals.
As websites expand, maintaining backlink relevance and quality becomes more challenging. Many SEO teams organize backlink acquisition and content promotion through structured processes instead of manual outreach.
Using centralized systems helps ensure consistency, reduces low-quality patterns, and supports long-term credibility when applied correctly.
The primary goal is to strengthen trust signals rather than manipulate rankings.
Domain Authority should not be evaluated alone.
Stronger indicators of authority growth include:
When these signals improve together, authority is developing regardless of fluctuations in Domain Authority scores.
Domain Authority is not a direct ranking factor, but it remains a useful reference for understanding website trust and strength.
Instead of treating Domain Authority as a target, it should be viewed as the result of consistent credibility, relevant backlinks, quality content, and sound technical foundations. Websites that focus on these fundamentals tend to achieve more stable and sustainable SEO performance over time.
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2026-02-10 18:20:21
APIs are the backbone of modern enterprises, connecting services, data, and business logic across microservices and cloud environments. Traditional perimeter-based security is no longer enough, as remote work, cloud adoption, and constant machine-to-machine communication blur internal and external boundaries. Zero Trust shifts the security model from “trust but verify” to “never trust, always verify,” treating every API request as potentially malicious. By enforcing strong identity verification through OAuth2, OpenID Connect, and mTLS, and applying granular authorization with attribute-based access control and JWT scopes, organizations can ensure that only the right entities access the right resources. API gateways and policy enforcement points provide centralized control, logging, and rate-limiting, while micro-segmentation and continuous inspection prevent lateral movement and detect anomalies in real time. Implementing Zero Trust requires collaboration across development, security, and operations teams but results in resilient, secure APIs that protect sensitive data, limit risk, and enable business agility.
2026-02-10 18:14:16
Engineering is as much a psychological journey as it is a technical one.
I wrote these notes a while back to process my own 'onboarding' journey. After talking to peers at Microsoft and Salesforce, I realized how universal these stages are. If you’ve ever felt like an imposter while staring at 10-year-old legacy code or felt that specific 'Alphabet Soup' headache in your first month, this is for you.
My journey at Amazon wasn’t linear - it came in phases. Very predictable ones, in hindsight…
Here are the 5 phases of going from 'new hire' to 'belonging' in Big Tech:
Yeah! The efforts paid off. I’m working for one of the biggest companies in the world. I made it. I rock.
There’s pride, relief, and a quiet validation that the grind meant something. You soak it in, because you should.
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And then reality hits.
What are all these internal tools everyone is using? \n What is this code that looks like it was written a zillion years ago? \n What is this jargon everyone seems to understand effortlessly?
Every document references another document. Every meeting sounds like alphabet soup. You smile, nod, and secretly write down words to Google later. You start questioning if you’re actually as good as you thought you were.
At some point, frustration peaks and clarity follows.
Enough theory. Enough context gathering. I’ll do what I’m good at. \n Headphones on. IDE open. I focus on code - writing it, reading it, fixing it. Code is familiar. Code doesn’t care where you came from. It’s the one place where merit still feels objective.
Things start moving again.
One day, in a meeting, I casually used a company-specific jargon. Some new folks blanked out. \n I realized it about two minutes later.
That moment stuck.
A new teammate asked for my mentorship. \n I started giving ideas for team projects -and they were taken seriously.
Without realizing it, I had crossed an invisible line. I wasn’t just surviving anymore. I belonged.
This is where perspective shifts.
From I work in one of the biggest companies in the world, to I am a sand particle in the desert , and oddly, that’s freeing. You don’t need to own the desert; you just need to know how to navigate the dunes.
Promotion isn’t about grinding harder. It’s about zooming out. Seeing how pieces connect. Understanding impact beyond your immediate task.
Eight months in, I don’t know everything. \n But I know how to navigate uncertainty, how to learn fast, and how to leverage where I am.
And that, I think, is progress.
A Note from the Future: Looking back at these notes today from my desk at Salesforce, I realize that while the companies change, the "Alphabet Soup" never truly disappears. The only thing that changes is your willingness to pick up a spoon and start eating.
If you’re currently in the middle of "Phase 2," keep your headphones on. The 'click' moment is closer than it feels.
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2026-02-10 18:05:37
\ Most of the difficult conversations I have around data do not start with disagreement; they start with alignment that feels reassuring at first and only later reveals its cost, because everyone in the room wants roughly the same thing: fresher data, fewer delays between signal and action, less manual intervention, and yet the moment you actually begin to design for those outcomes, the assumptions underneath them start to pull in different directions.
The request is usually framed as a question of speed. Can we get closer to real-time? Can we reduce batch windows? Can we trigger something automatically instead of waiting for a report? From the outside, this sounds like a performance problem, or a tooling problem, or sometimes just a matter of scaling compute, and technically that framing is not wrong, because modern data platforms are powerful enough now that latency is rarely the true constraint anymore; it is easy to push logic down, parallelize aggressively, mix structured and semi-structured data, and arrive at numbers fast enough to satisfy almost any SLA you are given.
What tends to get missed is that speed changes the shape of responsibility.
Once data stops being something people inspect and starts being something systems act on, the questions you get back are no longer about execution time or freshness; they are about meaning, ownership, and intent, and those are not questions your warehouse was necessarily designed to answer, especially if its evolution has been driven by a steady accumulation of reasonable optimizations made under different pressures at different points in time.
I have worked on systems where everything was technically correct: models aligned with requirements, transformations well tuned, costs monitored carefully, yet, when a number triggered something downstream and someone asked why the system behaved the way it did, the only honest explanation required unpacking months of architectural trade-offs that were never meant to be visible at that moment, let alone defensible to people who were not part of those original decisions.
That gap between correctness and defensibility is where trust quietly starts to erode.
Near-real-time pipelines make this gap wider, not narrower, because the faster data moves, the less opportunity there is to reconcile context, and the more every modeling choice begins to encode behavior rather than just representation. When you shorten refresh intervals, you are not only changing how quickly data arrives; you are changing what kinds of noise the system is allowed to react to, which anomalies get smoothed out naturally, and which ones suddenly become first-class signals simply because they appear sooner.
Cost pressure amplifies this effect in ways that are easy to underestimate. In credit-metered environments, freshness is not an abstract goal; it is something you pay for continuously, and every decision about warehouse sizing, clustering strategy, caching behavior, or reporting access patterns becomes part of an ongoing negotiation between performance and spend. That negotiation is manageable when humans are the consumers, because people learn how to read slightly stale data, or how to interpret numbers that lag reality by a known margin, but once automated systems start consuming those same outputs, the tolerance for ambiguity drops sharply, even if nobody explicitly acknowledges that shift at the outset.
This is where many teams discover that “trust the pipeline” was never a principle so much as an inheritance.
Pipelines were trusted because they had run for a long time without obvious failure, because the same transformations had been reused across multiple use cases, and because any inconsistencies that did surface were resolved informally, often by the same people who had built the system in the first place. Trust accumulated through familiarity rather than proof, and as long as data remained advisory rather than executable, that model held together well enough.
Distributed ownership complicates this further. Moving toward domain-aligned models and data products solves real organizational problems, but it also fragments semantic authority in subtle ways, because definitions that were once enforced centrally now live behind contracts, conventions, and expectations that require continuous discipline to maintain. Lineage tools can tell you where data came from, but they cannot tell you whether its interpretation has drifted as it crossed domain boundaries, especially when different teams are optimizing for different outcomes under different constraints.
Nothing breaks loudly when this happens. Models are still being built. Pipelines still succeed. Dashboards still render. The drift only becomes visible when two automated processes respond differently to what everyone assumed was the same signal, and by then the question is no longer how the data was produced, but why the system behaved in a way that nobody can fully reconstruct without assembling a timeline after the fact.
Automation exposes a weakness that was always there but rarely punished.
In traditional warehousing, correctness meant that numbers matched the logic agreed upon at design time, and performance meant that those numbers arrived quickly enough to be useful. In systems that drive action, correctness becomes contextual, because a number can be mathematically accurate and still operationally wrong if the assumptions behind it were never meant to be acted on directly. That distinction matters more as automated workflows expand, because the cost of poor data quality is no longer confined to bad analysis or slow decisions; it propagates immediately into behavior, often at a scale that makes manual intervention impractical.
This is why conversations about governance start to feel different once agentic systems enter the picture.
Governance stops being about policy and starts being about traceability, about whether you can explain not just what happened, but how and why a particular output was produced at a particular moment, given the state of the system at that time. Designing for that level of explanation introduces friction, and there is no honest way to pretend otherwise, because verification always costs something, whether that cost shows up as additional modeling discipline, stricter contracts, or slower paths to production.
What experience has taught me, though, is that this friction is cheaper than blind confidence.
Zero-trust, in this sense, is not about distrusting teams or locking systems down; it is about refusing to assume that past stability guarantees future interpretability, especially once data begins to trigger actions rather than inform discussions. It means treating definitions as contracts rather than conventions, writing transformations so they can be read and reasoned about later, and accepting that some optimizations are not worth the semantic opacity they introduce, even if they look attractive in isolation.
As warehouses increasingly function as decision substrates rather than reporting backends, the architectural goal shifts subtly but decisively, away from producing fast answers toward producing answers that can withstand scrutiny after they have already shaped outcomes. That shift does not require abandoning decentralization or real-time capabilities, but it does require acknowledging that speed, ownership, and automation all carry obligations that traditional pipeline thinking was never designed to satisfy on its own.
For a long time, trust in data systems was something you inherited by default. In an environment where systems act on data directly, trust has to be engineered deliberately, because the question that ultimately matters is no longer whether the pipeline ran successfully, but whether you can stand behind its behavior when someone inevitably asks why the system did what it did.
Speed is no longer the hard part. \n Being able to prove your system’s intent is.
2026-02-10 18:00:20
In a bear market, survival is the prerequisite for catching the next wave. However, the ability to put capital to work while surviving is what truly separates the winners from the rest.
The most common dilemma during volatile periods is that funds either endure fluctuations with little growth or sacrifice flexibility for interest, making investors slow to respond when opportunities arise. MEXC Earn's product restructuring addresses this contradiction: through a "Deposit-Earn-Borrow" ecosystem, it integrates earnings, trading, and turnover into a single capital management system, reducing switching costs and enabling more efficient capital movement across different scenarios, breaking the limitations of traditional finance products with low returns and low liquidity.
For users, depositing into MEXC Earn is no longer just passive storage. Instead, it empowers the same capital to generate substantial yields and serve as a buffer during drawdowns, while remaining ready for flexible deployment based on market trends. This allows users to allocate and rotate funds with greater confidence and composure even in a sluggish market. The platform's Assets Under Management (AUM) has grown by approximately 43% this year, with user base growth of about 64%. This indicates that more users are recognizing MEXC's capital management products and are willing to deposit their assets here.
Currently, MEXC Earn precisely meets the core needs of diverse users through four key dimensions: Ultra-High Returns returns, capital efficiency, zero-cost liquidity, and a seamless experience.
To reduce the learning curve for new users trying financial products for the first time, MEXC offers entry-level products with the highest market yield rates. First-time users can enjoy up to 600% APR for the first 2 days.
For Futures traders, margin assets often serve merely as collateral during holding or waiting periods, limiting capital efficiency. MEXC's "Futures Earn" incorporates Futures account assets into the earning mechanism, allowing Futures assets to earn up to 20% APR while meeting position value requirements. This design allows Futures capital to generate additional income while providing margin protection, creating a parallel "trading + earning" capital management approach. Additionally, earnings accrue daily and are automatically added to the margin balance, providing balance enhancement and risk buffering for the account.
For long-term holders with short-term liquidity needs, MEXC offers zero-interest flexible borrowing during promotional periods. Users can pledge specified assets to borrow stablecoins without paying interest. This product resolves the conflict between "not wanting to sell coins" and "needing cash", allowing users to prioritize interest-free borrowing over selling, maintaining the appreciation potential of long-term positions while gaining liquidity.
MEXC's integrated "Deposit-Earn-Borrow" design bridges the gap between financial services and trading processes. Users don't need manual transfers, as flexible financial funds can be directly used for Spot or Futures trading, improving fund availability while reducing operational costs. With Flexible Earn, assets are automatically enrolled in the yield mechanism without manual subscription or locking. Crucially, assets continue to earn interest even during pending orders or transfers, making the earning process truly frictionless.
MEXC's financial system has evolved from "idle money appreciation" to covering "capital efficiency" across all scenarios. It achieves a seamless connection between wealth management and trading, keeping deposited funds highly liquid to meet the diverse needs of passive savers, active traders, and arbitrageurs.
Under this framework, the path from "capital management" to "quick order placement" is significantly shortened, greatly reducing the risk of missing market opportunities due to operational delays. Data validates this model's success. The contribution of financial products to the platform's Futures trading volume has increased from 2% to 10%. This proves that MEXC Earn is not merely an asset management gateway, but a growth engine that continuously supplies liquidity and momentum to the core trading business.
