Anthropic Fable 5.1 is seeing materially weaker initial traction than Fable 5 across the first 48 hours after launch.

OpenRouter token usage for Fable 5.1 was 66.6% lower than Fable 5 over the comparable two-day launch window.

TickerTrends social post tracking showed a similar result, with Fable 5.1 discussion volume 56.3% lower than Fable 5 over the same relative launch period.

For now, the initial launch comparison is weak: Fable 5.1 is materially behind Fable 5 on both token usage and social interest.

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On August 30, TickerTrends tracked:

643 PS5 units sold

$51,400 in gross revenue

Approximately 57% of the June peak

Daily updates will be available to TickerTrends KPI Tracking Suite subscribers starting tomorrow.

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The latest tracked readings show:

• 33.5% month-over-month ARR growth

• 4.6% week-over-week ARR growth

• $1 billion in latest reported ARR

Weekly updates beginning in September

Starting in September, TickerTrends subscribers will receive a new ChatGPT Ads ARR reading by email every Wednesday.

Each update will include:

• The latest tracked ARR

• Week-over-week growth

• Month-over-month growth

• Changes in the underlying adoption signals

The latest chart and historical data will also be available on the TickerTrends platform.

TickerTrends figures reflect proprietary tracking and are not company-issued financial guidance.

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OpenAI’s Codex is separating from Anthropic’s Claude Code in B2B adoption.

TickerTrends’ weighted trailing 12-month adoption data shows Codex reaching approximately 550% year-over-year growth in August, up from roughly 365% in May. That represents a 183-point increase in its year-over-year adoption rate over the past three months.

Claude Code has moved in the opposite direction. After reaching approximately 595% year-over-year growth in May, its adoption rate declined to roughly 450% in August, representing a 157-point decrease over the same period.

Claude Code maintained the stronger year-over-year growth rate earlier in the period, but its momentum peaked in May and has weakened each month since. Codex continued accelerating, overtook Claude Code in June, and widened its lead through August.

Both products continue to show substantial growth compared with the prior year. However, the latest data suggests that incremental B2B adoption is increasingly shifting toward Codex.

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OpenAI: GPT-5.6 adoption accelerates

OpenAI model mentions reached 13K/day, with GPT-5.6 representing 84% of current model-family mentions.

GPT-5.6 reached 11K mentions/day, up 119.9% WoW and 300.9% MoM. Within the GPT-5.6 family, Sol leads at 3K/day, increasing 113.4% WoW and 76.5% MoM.

Meanwhile, older model families have declined, with GPT-5.5 down 72.0% MoM and GPT-5.4 down 49.0% MoM.

Anthropic: Opus leads while Sonnet gains

Anthropic model mentions reached 526K/day, led by Claude Opus at 325K/day.

Claude Sonnet reached 109K/day, up 18.3% WoW and 18.9% MoM, the strongest recent growth among the major Anthropic models. Claude Fable reached 90K/day, while Claude Haiku stood at 2K/day.

The new TickerTrends GitHub Commit Mentions dataset is available to clients and will be rolling out across associated names through new GitHub commit trackers.

Source: TickerTrends GitHub Commit Mentions Analysis

Methodology: TickerTrends tracks references to individual AI models across GitHub commit messages. The dataset is filtered to reduce automated, duplicated, and low-quality activity, with results presented as weekly averages of daily commit mentions.

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Claude adoption within major organizations

Salesforce currently has the largest number of cumulative Claude adoption observations among the organizations shown, reaching 690 observations as of August 21, 2026.

Microsoft ranks second with 374 observations, followed closely by NVIDIA with 335. Oracle and Apple have each reached approximately 115 observations, while Amazon and Google currently show lower cumulative totals.

Current cumulative Claude adoption observations:

• Salesforce: 690

• Microsoft: 374

• NVIDIA: 335

• Oracle: 116

• Apple: 115

• Amazon: 39

• Google: 27

OpenAI adoption within major organizations

Microsoft currently leads the group shown with 332 cumulative observations associated with ChatGPT adoption. Microsoft’s adoption evidence began appearing earlier than the other series and continued to increase through August 2026.

For Codex, Salesforce and NVIDIA have nearly identical cumulative totals, with 157 and 155 observations, respectively. Oracle follows with 104 Codex observations, while Microsoft has 92.

The chart also shows 46 cumulative observations associated with ChatGPT adoption at Oracle.

Current cumulative OpenAI product adoption observations:

• Microsoft, ChatGPT: 332

• Salesforce, Codex: 157

• NVIDIA, Codex: 155

• Oracle, Codex: 104

• Microsoft, Codex: 92

• Oracle, ChatGPT: 46

Expanding enterprise software adoption tracking

TickerTrends is building broader visibility into the adoption of software products within large enterprises.

The result is a standardized, continuously updated view of enterprise software adoption that can be compared across companies, products, and time periods.

TickerTrends will continue expanding this coverage across AI platforms and other enterprise software products.

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As of August 10, Claude Code’s tracked ARR was $15.12 billion compared with $8.83 billion for OpenAI Codex. The difference between the two products was $6.30 billion, with Claude Code at 1.71 times the tracked ARR of Codex.

The tracked ARR difference was $8.38 billion on July 6, $7.07 billion on July 13, $6.73 billion on July 20, $6.25 billion on July 27, $6.06 billion on August 3, and $6.30 billion on August 10.

Between July 6 and August 10, Claude Code tracked ARR increased from $14.26 billion to $15.12 billion. Over the same period, Codex tracked ARR increased from $5.88 billion to $8.83 billion.

TickerTrends estimates OpenAI Codex reached $8.83 billion in annualized revenue run rate for the week ending August 10, 2026. Under the central allocation case, Codex represents an estimated 45.0% of OpenAI’s enterprise ARR.

Codex tracked ARR increased from approximately $1.0 billion at the beginning of February to $3.0 billion in April, $5.6 billion in June, and $8.83 billion in August.

Recent tracked ARR was $5.88 billion on July 6, $7.31 billion on July 13, $7.74 billion on July 20, $8.10 billion on July 27, $8.44 billion on August 3, and $8.83 billion on August 10.

The latest month-over-month growth rate, measured against four weeks earlier, was 20.8%.

TickerTrends’ latest tracking estimates Claude Code reached $15.12 billion in annualized revenue run rate for the week ending August 10, 2026. Claude Code represents an estimated 21.9% of Anthropic’s total tracked ARR.

The tracker is anchored to Anthropic’s reported $2.5 billion Claude Code run rate on February 12, 2026. Tracked ARR subsequently increased to approximately $6.3 billion by the end of March, $10.3 billion by the end of April, and more than $14 billion by June.

Claude Code tracked ARR was $14.26 billion on July 6, $14.38 billion on July 13, $14.47 billion on July 20, $14.35 billion on July 27, $14.50 billion on August 3, and $15.12 billion on August 10.

The latest month-over-month growth rate, measured against four weeks earlier, was 5.2%.

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TickerTrends tracked OpenAI ARR reaching $44.3B as of Aug. 12, up from $42.6B on Jul. 29.

Bloomberg reported OpenAI had surpassed a $40B revenue run rate as of Jul. 31, providing an external reference point for the TickerTrends tracking.

The lower chart shows the shorter-term trend, with tracked ARR currently growing 11.6% month over month.

Claude Code continues to lead over Codex in enterprise account adoption.

As of August 8, TickerTrends tracks Claude Code across 5,004 enterprise accounts, compared with 1,606 for Codex. Since February 28, Claude Code has added 3,052 accounts, nearly three times the 1,105 added by Codex.

Claude Code added 83 net new enterprise accounts, compared with 41 for Codex. While adoption has moderated from its June highs, Claude Code continues to add accounts at roughly twice the pace.

Codex adoption remains positive and has gradually accelerated from its earlier levels. However, OpenAI has yet to materially close the gap in either total tracked accounts or weekly additions.

Enterprise account adoption does not directly measure revenue, paid seats, or product usage. It does provide a high-cadence view into how quickly each product is entering corporate environments.

TickerTrends’ data points to a continued slowdown in $CAVA brand momentum. U.S. consumer usage remains positive year over year, but growth has decelerated from the levels seen through much of 2025 and early 2026. Social interest has also cooled, while recent product launches have generated short-term spikes in attention without producing a sustained reacceleration in the broader brand.

The lettuce-related outbreak appears to have added further pressure, and the slowdown was already underway before the incident. That makes the current weakness look broader than a temporary reaction to a single event.

TickerTrends’ second-quarter-forward revenue forecast for CAVA has been revised down by 220 bps over the past three weeks, from +27.3% YoY growth to +25.1%. Our forward view has continued to move lower as underlying consumer and social signals have weakened.

Consumer Usage Has Slowed

CAVA’s U.S. Consumer Usage growth has fallen to +6.3% YoY, down from a +114% peak in August 2025. The recent deterioration has been especially sharp, with growth down roughly 1,489 bps month over month and now sitting near a cycle low.

The outbreak may have intensified the latest weakness, but the broader trajectory had already been deteriorating before then. That suggests the slowdown is not purely event-driven and may reflect a more meaningful normalization in underlying demand.

Social Interest Is Losing Momentum

CAVA’s Social Interest growth has slowed to +14.4% YoY, down from a +25.1% high in May 2025.

New Menu Launches Are Producing Limited Impact Spikes, Not Sustained Momentum

CAVA has continued introducing new menu items in an effort to drive consumer engagement, but recent launches have not yet changed the broader trajectory. Glazed Salmon generated a meaningful spike in social discussion following launch, but that attention has already begun to fade.

Harissa BBQ followed a similar pattern, producing an initial increase in conversation without developing into a sustained source of momentum.

TickerTrends View

CAVA’s demand signals have deteriorated materially. U.S. consumer usage has slowed from +114% at its peak to +6.3% YoY, while social interest has cooled from +25.1% to +14.4%. The lettuce-related outbreak may have intensified the recent weakness, but the slowdown predates the incident.

Recent menu launches have generated bursts of attention without reversing the broader trend. Until those launches begin producing sustained improvement in consumer usage and social momentum, TickerTrends data suggests CAVA’s underlying growth profile remains under pressure.

OpenAI CFO Sarah Friar recently told employees that annualized recurring revenue in July exceeded all of Q2.

Our latest OpenAI ARR read reached $42.6B on July 29, up from $37.3B on June 25 and $33.0B on May 30.

YoY ARR growth accelerated sharply entering July:

• May 30: $33.0B ARR, 223% YoY

• June 25: $37.3B ARR, 245% YoY

• July 2: $38.5B ARR, 281% YoY

• July 9: $40.3B ARR, 282% YoY

• July 29: $42.6B ARR, 259% YoY

The data shows a clear acceleration in OpenAI’s revenue trajectory entering July. Although YoY growth moderated from its July 9 peak, ARR continued to expand, adding approximately $5.2B between June 25 and July 29.

We updated both our OpenAI and Anthropic ARR tracking today.

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TickerTrends’ latest private-market tracking puts Anthropic at roughly $74.1 billion in annualized revenue run rate, versus $41.3 billion for OpenAI.

Anthropic is currently tracking at about 1.79 times OpenAI’s ARR, a gap of roughly $32.8 billion. Since the companies’ last reported figures, Anthropic has increased 57.7%, while OpenAI has increased 65.2%.

Anthropic’s tracked ARR rose from $10.2 billion in January to $35.6 billion in April, $69.6 billion in June, and $74.1 billion in July.

OpenAI’s tracked ARR increased from $21.4 billion in January to $33.0 billion in May and $41.3 billion in July.

Anthropic remains materially ahead in absolute ARR, while OpenAI has posted the stronger increase since its latest reported figure.

$RDDT sold off after reports that Reddit is reconsidering its Google data partnership as the existing agreement approaches renewal. The agreement, reportedly worth approximately $60 million annually, gives Google access to Reddit content for AI use. Reddit has discussed restricting that access as publishers become increasingly concerned that Google’s AI-generated answers use their content while reducing the need for consumers to visit the original source.

The market has largely framed the dispute as a debate over licensing revenue. We think the more important question is who controls the consumer relationship.

Historically, Reddit produced valuable content, Google indexed it and users clicked through to Reddit. Reddit then controlled the engagement and monetized those visits through advertising. AI search changes that exchange. Google can increasingly use Reddit’s conversations to generate answers within its own products, allowing Reddit to supply the information while Google retains the consumer.

This shift becomes more important as Google’s consumer AI distribution expands. TickerTrends’ proprietary Gemini consumer-usage index is up 211% YoY and remains near record highs, demonstrating the growing scale of the AI interface through which Google can organize and deliver information without sending consumers elsewhere.

Reddit’s data has unquestionably become more valuable in this environment. Authentic opinions, product recommendations and real-time discussions are difficult for AI systems to reproduce without access to continuously refreshed platforms.

But owning a scarce asset is not the same as having negotiating leverage. Leverage depends on whether Reddit can restrict Google’s access without materially weakening its own user acquisition and advertising business.

What the TickerTrends Data Shows

TickerTrends data does not show evidence of a broad collapse in Reddit’s web activity. Reddit web traffic remains +8.8% YoY, while its trailing 12-week average remains positive at +11.9%. Every tracked week in 2026 has remained above zero.

This suggests Reddit continues to benefit from substantial web-based discovery despite the expansion of AI-generated answers. It does not prove that Google is responsible for that growth, but it indicates that Reddit’s broader web funnel remains healthy.

The more important signal is the divergence between Reddit’s website activity and its most direct consumer channel.

Reddit app usage remains negative on a year-over-year basis, with the latest weekly reading at -9.3% and the trailing four-week average at -9.4%.

This divergence matters because the app is the environment where Reddit has the greatest control over distribution, identity, engagement and monetization. Website users can arrive through Google or another external source, consume a single discussion and leave. App users represent a more deliberate and habitual relationship with Reddit itself.

Average session time has also deteriorated, moving from roughly flat at the start of the year to -4.3% YoY, indicating that Reddit is not only seeing weaker app usage but also declining engagement among the users who remain.

If Reddit were successfully converting its cultural relevance and search visibility into a stronger direct consumer relationship, we would expect app engagement to strengthen alongside web traffic. Instead, TickerTrends data shows positive web activity but persistently negative mobile engagement.

That suggests Reddit’s content remains highly valuable across the open web, while its ability to own the consumer relationship remains less developed.

Google Holds the Near-Term Leverage

Reddit is correct that the original agreement likely underprices the long-term strategic value of its data. Google’s reported payment was negotiated in 2024, before its current AI interfaces had reached their present scale. The more Google uses Reddit discussions to improve AI-generated answers, the stronger Reddit’s argument becomes for higher or usage-linked compensation.

Reddit is also financially stronger than it was when the agreement was signed. In Q1 2026, revenue increased 69% YoY to $663 million, while Daily Active Uniques increased 17% to 126.8 million. The company therefore has more capacity to challenge the original economics than the headline contract value implies.

However, our proprietary data suggests Google currently holds the stronger near-term position. Reddit’s web traffic remains positive, but direct app engagement is declining. Reddit therefore risks disrupting part of a healthy web-discovery funnel before establishing an equally strong direct alternative.

Google’s position is not risk-free. Reddit discussions remain an unusually valuable source of current human context, particularly for product research, recommendations and experience-based queries. Losing structured access could weaken answer quality in precisely the categories where model-generated responses benefit most from real human experiences.

But Google has the larger consumer interface and more ways to source, organize and distribute information. Reddit has the scarcer content, but it appears less able to walk away without potentially placing part of its broader user-acquisition funnel at risk.

TickerTrends View

The market is focused on whether Reddit retains approximately $60 million of high-margin licensing revenue. We believe the larger issue is whether Reddit can convert the strategic value of its conversations into control of the consumer relationship.

TickerTrends data shows a clear divergence: Reddit’s web traffic remains positive, while app usage and session time are declining. This suggests Reddit continues to benefit from broad web discovery but has not yet built a sufficiently strong direct channel to negotiate from a position of full independence.

Reddit is right to demand better economics. Its continuously refreshed human conversations are becoming more valuable as Google’s AI distribution scales, and a static agreement may no longer reflect the value Reddit contributes.

However, the current evidence favors Google.

Reddit owns the scarcer data, but Google controls the larger consumer interface. Until Reddit strengthens direct usage and engagement, Google holds the greater near-term leverage.

Havaianas, owned by Alpargatas, is gaining traction beyond its traditional beachwear positioning as celebrity adoption and the Isabel Marant collaboration push the brand further into everyday fashion. The partnership has positioned its signature rubber sandal as a fashion accessory, reinforcing management’s view that growth is increasingly being driven by changing consumer behavior rather than seasonal beach demand.

TickerTrends’ alternative data supports that transition and shows the current momentum is materially stronger than in prior years, including the period surrounding last year’s Dolce & Gabbana collaboration. Social discussion is increasingly focused on styling, designer partnerships, and daily wear rather than vacations, suggesting Havaianas is expanding both its addressable market and the duration of its demand cycle.

What the Alternative Data Says:

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TickerTrends’ demand trackers for Havaianas are all pointing the same direction:

Havaianas’ Global Ecom Demand: +83.5% YoY (current value), up from a 4-week average of +69.8%, near its cycle high after bottoming at a February low of -20.2%.

Havaianas’ Consumer Interest: +95.2% YoY (current value), up from a 4-week average of +84.1%, and a sharp reversal from its February low of -9.3%.

Key Dates in Havaianas’ Virality and Growth Cycle:

• May 11, 2026 - Vogue publishes a feature on Havaianas’ resurgence in fashion circles

• May 20, 2026 - Isabel Marant x Havaianas collaboration reportedly announced

• May 22,2026 Isabel Marant x Havaianas collaboration officially launched globally

• Late May 2026 - Summer capsule launch with Aritzia

• June 05, 2026 - The Isabel Marant x Havaianas collection officially restocks in the U.S. market

Social Discussion:

Social discussion surrounding Havaianas flip-flops has accelerated sharply, signaling renewed consumer awareness as the category returns to fashion relevance.

The Isabel Marant partnership generated a significant increase in discussion, illustrating how cultural catalysts can accelerate consumer awareness before broader demand is reflected in traditional financial metrics.

Financial Context:

Havaianas’ collaboration with Isabel Marant has generated a materially stronger commercial response than last year’s Dolce & Gabbana partnership. TickerTrends’ global e-commerce demand tracking shows that the 2025 cycle peaked at 57.80 in mid-May before rolling over, ending June at 43.69, a 21.3% decline from early May. This year, demand began May at a much higher 70.57 and increased another 20.4% to reach 84.97 by the end of June. That puts the current cycle roughly 47.0% above last year’s entire seasonal peak. Rather than producing a short-lived launch spike, the Isabel Marant collaboration appears to be supporting a broader and more sustained increase in online demand.

Conclusion:

Alternative data indicates that Havaianas’ fashion resurgence is translating into measurable consumer demand. Social discussion has reached record seasonal levels while e-commerce demand and consumer interest continue to trend higher, suggesting that increased awareness is extending beyond media attention into broader consumer engagement.

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Moonshot AI appears close to releasing Kimi K3, potentially its largest model upgrade to date.

GLM-5.2 was released in June with public weights and a 1 million-token context window. Its strength in coding and long-horizon agent tasks quickly drove attention outside China, with the model gaining usage on OpenRouter and being discussed as one of the first open-weight models that worked well as a general coding agent.

K3 has the potential to create a similar reaction. Early tester feedback has compared it with Fable-level models, while some individual Arena examples show it competing with or leading existing frontier models. The evidence remains selective, but the bar appears to be much higher than a normal incremental Kimi release.

For listed exposure, China Merchants China Direct Investments, or 133.HK, owns a meaningful Moonshot stake. The position gives the fund leverage to both K3 adoption and any resulting reset in Moonshot’s private-market valuation.

Kimi K3

Moonshot released a trailer for K3 on 16 July, but has not yet published a model card, official benchmarks, API pricing, weights or licensing terms. The Kimi API platform still lists K2.7 Code as its latest model, while Moonshot’s main site continues to feature K2.6.

Reported specifications include a roughly 2.5 trillion-parameter mixture-of-experts architecture and a context window of around 1 million tokens. These figures remain unconfirmed and appear to come from a small number of reports that have since been repeated across other outlets. Until Moonshot publishes official details, the architecture and parameter count should be treated as reported rather than confirmed.

The early outputs suggest K3 may be focused on long-horizon agent and coding tasks. This would put it directly against GLM-5.2, Opus and Fable in the areas currently driving the most developer interest: extended coding tasks, frontend generation and autonomous multi-step workflows.

One early frontend test described K3’s output as among the best the tester had seen from the prompt, although it reportedly took 35 minutes to complete. In another Arena comparison, Fable 5 finished faster and was described as having cleaner controls, while K3 produced a broader result.

The early examples are positive but mixed. Some comparisons show K3 leading, while others still favor existing models. Without broader benchmarks, standardized testing and official pricing, it is worth being cautious around the pre-release hype.

Moonshot’s momentum into the release

K3 is arriving as Moonshot’s commercial and developer momentum has already accelerated. Moonshot’s ARR reportedly moved from more than US$100 million in March to above US$200 million in April. Later reports put ARR above US$300 million by mid-June, with API revenue representing more than 70% of the total. The US$300 million figure comes from secondary reporting and has not been disclosed in Moonshot financial statements.

TickerTrends Developer Activity rose rapidly during this shift, increasing 223% over the past three months. Activity has since plateaued slightly below its late-June high, making K3 the next major potential catalyst.

The listed exposure

Moonshot is private, but 133.HK owns a direct stake through its participation in Moonshot’s December 2023 Series A. The fund initially invested US$10 million for a 1.29% interest, although subsequent rounds have diluted the position.

On 15 July, the fund published its asset breakdown as of 30 June and increased the Moonshot carrying value from US$17 million to US$55 million. Moonshot now represents 5.68% of the fund’s US$967 million of total assets, up from 1.67% at the end of March.

The US$55 million mark implies a Moonshot valuation of approximately US$9.1 billion based on the fund’s estimated 0.60% stake. That is broadly consistent with Moonshot’s early-2026 financing level but remains below the May round, when Moonshot raised approximately US$2 billion at a post-money valuation above US$20 billion. That round was led by Meituan’s investment arm.

Moonshot has since reportedly been seeking another round at around US$30 billion, with some reporting placing the proposed pre-money valuation at US$31.5 billion. The round remains under discussion, so that figure should not be treated as a completed valuation.

At the May round price, the position would be worth approximately US$121 million after allowing for dilution, or around 11.7% of the fund’s assets. At a US$30 billion valuation, the position would be worth approximately US$169 million and represent around 15.6% of assets.

The latest filing

The Moonshot markup was already one of the largest movements in the fund’s portfolio last quarter. It added approximately HK$1.96 per share to NAV, but declines in other positions more than offset the increase. The fund reported NAV of US$4.954, or HK$38.83, per share as of 30 June.

This is relevant because Moonshot has moved from a small option in the portfolio to a position capable of affecting the reported NAV on its own. If K3 drives another valuation reset, the next Moonshot mark could be materially larger than the one reported this quarter.

What matters after launch

The first question is whether K3 is actually released as an open-weight model and under what license. GLM-5.2’s impact was amplified by its MIT license, downloadable weights and immediate availability across developer platforms. A strong closed API model would still matter for Moonshot, but it would not create the same open-weight adoption cycle.

The second is performance. K3 needs to hold up across coding, agent tasks, long-context reasoning and real-world frontend generation once broader testing begins. Individual Arena comparisons are useful early signals, but the model will need consistent performance across larger benchmark sets.

The third is economics. Pricing and inference speed could determine whether K3 becomes widely used even if its raw performance is strong. The current early feedback suggests output quality may be high, but latency could be a limitation due to the large parameter size.

Finally, TickerTrends Developer Activity and Kimi Consumer Usage should show whether the launch creates sustained adoption.

Risks

K3 may not match the early tester comparisons once it is broadly available. The reported specifications may also prove inaccurate, and Moonshot may release the model with restrictions that limit open-weight adoption.

Strong model performance may not translate into higher consumer usage or API revenue, particularly if inference is slow or expensive. The proposed US$30 billion to US$31.5 billion financing may also close at a lower valuation or not close at all.

For 133.HK, Moonshot remains one position within a broader closed-end portfolio. Even a meaningful Moonshot markup can be offset by declines elsewhere, as occurred last quarter.

Conclusion

Moonshot appears to be preparing a much larger agent-focused model at a time when open-weight models are rapidly closing the gap with the leading closed systems.

GLM-5.2 showed how quickly a strong Chinese open-weight release can move from limited awareness into global developer adoption. Early K3 outputs suggest Moonshot may have a credible chance of creating a similar moment, although the evidence remains too selective to reach that conclusion before launch.

133.HK provides the only meaningful listed exposure to that outcome. Its Moonshot position has already risen from 1.67% to 5.68% of assets and could move above 10% if marked to the May financing level.

The question is whether K3 becomes the next major open-weight model. If it does, the likely sequence is a re-acceleration in developer activity and usage, stronger API revenue, a higher Moonshot financing valuation and, eventually, a larger carrying value inside 133.HK.

Sinch appears exposed to the same demand supporting CPaaS peers like Twilio and Bandwidth: business formation, digital-first customer engagement, and growing demand for messaging, email, voice, and authentication infrastructure.

As more support, sales, and customer-service workflows become AI-assisted or AI-native, the need for SMS, email, voice, verification, routing, and compliance infrastructure continues to grow. Sinch is leaning into this directly through Agentic Conversations for AI-powered customer engagement across channels and Voice Relay, which connects AI agents into live phone calls.

TickerTrends data suggests this is beginning to show up in Sinch-specific activity, with developer activity, login activity, and pricing activity all moving higher into Q2.

TickerTrends Total Revenue Forecast Q2 FY2026: [Available in full note]
Analyst Consensus: $682M

TickerTrends Tracking

Developer Activity has strengthened materially. The Sinch Developer Activity tracker reached 68.4 in early June, the highest reading in the series, up 27.3% month over month and 21.3% on a 4-week moving average basis. That suggests increased implementation activity and points to stronger onboarding demand.

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has seen strength into FQ1E, with TickerTrends revenue forecasting, Consumer Interest, and Cart Activity all improving into quarter-end.

TickerTrends Data

TickerTrends’ total revenue forecast remains above consensus heading into FQ1E, with the latest forecast at $2.39B vs. $2.3B Street consensus.

Forecast revisions have moved higher since early May, now implying a ~2.0% beat vs. consensus with a historical model MOE of 0.95%.

$FAST Consumer Interest has sharply inflected positive into quarter-end, with YoY growth accelerating to +32.7% as of June 30.

$FAST Cart Activity has also inflected positive into quarter-end, with YoY growth reaching +10.5% as of June 29.

Conclusion

TickerTrends data suggests Fastenal demand momentum strengthened late in the quarter, with both top-of-funnel interest and conversion-related activity inflecting higher.

Revenue forecast revisions have moved upward into the print, with the latest forecast sitting above consensus by more than 2x the historical MOE.

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Crocs is showing a demand inflection into Q2. Crocs Brand and HeyDude are both benefiting from TikTok Shop traction, while core Crocs demand indicators are accelerating and emerging product conversations around sandals and buckles are reaching new highs.

Preview Tracking

HeyDude consumer interest has inflected positive, with YoY growth at +8.5%.

Crocs segment consumer interest continues to accelerate, now up +13.3% YoY, supporting improving DTC momentum.

Preview Social Discussion

Crocs social momentum is broadening beyond the core clog, with both sandals and buckles hitting new highs into July. Crocs sandals social performance reached 62.1k, up +148% YoY.

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Investors and businesses want a reliable way to understand what demand is doing before it shows up in lagging purchase signals. However every traditional approach comes with a tradeoff.

• Panels offer structure, but they break down in the long tail.

• Expert interviews are credible, but they are sparse and lagging.

• Internal digital tracking is fast, though on its own, with no standard behind it, it tends to create more noise than signal.

TickerTrends is building the standardized demand intelligence layer for investors and businesses. A system that turns fragmented digital behavior into a consistent framework for understanding where demand is forming, accelerating, or fading before it appears in lagging purchase data.

Panel-like scale without the panel-size constraint

Panel data works when the behavior being measured is already large enough to show up in the sample. But virtually all of the best signals start in the long tail - emerging brands, niche products, viral launches, regional inflections, and early category shifts.

A 100,000-person panel may be representative of the U.S. population, but for a product with only 10,000 users nationally, the observable sample is too small to generate a usable signal. The issue is not the headline sample size; It is the effective sample size for the specific behavior being measured.

TickerTrends solves for this by measuring intent across the open internet. Instead of waiting for niche demand to appear inside a fixed sample, we standardize broad digital behavior into comparable demand signals.

The credibility of an expert interview

Expert interviews are valuable because of the credibility and real-world validation to a thesis. They allow investors to connect an idea to an industry voice, which matters because market participants care about who is saying something. The credibility is why investors are willing to accept the limitations of expert interviews. The data is sparse, episodic, and lagging, but the reputation of the voice behind the insight gives the thesis more weight.

TickerTrends brings that same credibility to digital demand signals. In the same way an expert interview is not just “someone talking,” TickerTrends is not just “internet data.” It is the signal layer that transforms noisy digital behavior into credible, repeatable, and actionable demand intelligence.

We give investors what they value from expert research, but at greater scale: a framework for deciding whether a thesis is supported by digital evidence.

Tracking demand before the outcome

Before someone buys a product, signs up for software, deploys infrastructure, adopts a workflow, or chooses a vendor, they first hear about it. Then they research it. Then they decide whether to act.

Traditional datasets capture the final outcome after the fact. TickerTrends is built to track the journey before the outcome materializes.

By measuring awareness, consideration, and action across the economy, TickerTrends gives investors and businesses an earlier view of demand formation before it becomes visible in revenue, transactions, or market share.

The standardization that turns noise into signal

Anyone can try to build infrastructure to track digital signals. But doing this in isolation creates noise, inconsistency, and uncertainty. It creates a disadvantage.

The challenge is not only collecting data. It is knowing which signals matter, how to normalize them, how to compare them across sectors, and how to turn fragmented internet behavior into actionable conclusions across any company, category, or market.

TickerTrends creates the standard framework for turning fragmented demand signals into actionable intelligence.

We provide a consistent framework for measuring demand across consumer, B2B, technology, regional markets, and niche categories. Instead of fragmented one-off analysis, investors and businesses get a repeatable intelligence layer for proving or disproving a thesis across any market.

The adoption flywheel

As TickerTrends becomes a trusted standard for measuring demand, the platform creates an adoption flywheel.

• Investors use TickerTrends to spot emerging theses earlier and underwrite them with more conviction. Over time, those theses influence where capital, attention, and research focus begin to flow.

• Businesses then use TickerTrends to understand how demand is forming, where customers are shifting, and which trends are gaining credibility. Corporations can use TickerTrends to guide product, marketing, strategy, and resource allocation.

This creates a reinforcing loop.

As more investors and companies rely on the same framework for measuring demand, it becomes the common reference point for understanding what is gaining traction.

TickerTrends is the standardized demand intelligence layer

The future of business tracking is not a larger panel, another expert call, or another internal dashboard. It is a system that combines the scale of broad digital coverage, the credibility of a trusted research process, the speed of real-time demand tracking, and the structure needed to turn noise into signal.

TickerTrends is building that layer.

The system that gives investors and businesses a way to track, prove, or disprove a thesis across any area of demand: consumer or B2B, global or regional, mass-market or niche.

TickerTrends is the infrastructure for measuring the long tail of demand.

Inference performance is increasingly determined by the system around the model: routing, scheduling, KV-cache movement, quantization, multi-token prediction, and the ability of clusters to handle bursty east-west traffic.

That shift increases the strategic value of the network layer. Large inference clusters require high-density 400G/800G Ethernet fabrics, low-latency switching, deep buffers, VOQ, adaptive routing, congestion management, and automation that can support constant configuration, validation, and tuning. This is where Arista becomes one of the second-order beneficiaries

Disaggregated inference accelerates the network’s importance

The Baseten GLM-5.2 result is a good example of where the market is moving. The 280+ tokens/sec output came from the serving system around the model: quantization, KV-aware routing, disaggregated inference, multi-token prediction, and NVIDIA Dynamo tooling. NVIDIA remains central, but more of the incremental performance gain is coming from the broader inference architecture.

TickerTrends data shows the same shift. Disaggregated inference developer activity increased 1,515% in Q2 2026 versus Q1, with the Q3 run rate already 55.5% above the Q2 average. This reflects the industry separating prefill from decode, moving KV caches across GPU pools, and optimizing routing and scheduling for long-context and reasoning workloads.

As workloads are split across resources, inference becomes more of a networking problem. Large KV transfers, RDMA/RoCE efficiency, incast, congestion, multi-tenant isolation, and cluster-wide scheduling all become more important. Arista’s advantage is that it gives customers a cost-efficient, flexible Ethernet fabric for these AI clusters while preserving openness and avoiding dependence on a single integrated infrastructure stack.

TickerTrends Arista signals also point to demand pull-through

Arista switch configuration activity is up 137% YoY, with Q2 averaging +132% YoY. Large AI fabrics require more configuration, validation, and tuning, which makes this a useful proxy for deployment intensity.

Hiring: returned to positive growth, latest reading +31.2% YoY, Q2 averaging +25.2% YoY, consistent with demand pull-through as AI networking takes a larger share of datacenter spend.

AVD developer activity, Arista’s automation ecosystem, is up 1,362% YoY, with Q2 running 365% above year-ago levels. Elevated AVD usage points to deeper engagement with open Ethernet builds and more operational complexity inside large AI fabrics.

Risks and counterpoints

The main risk is that NVIDIA Spectrum-X continues to win deployments where customers prioritize speed, integration, and a single-vendor path. Cisco, Broadcom-powered platforms, and whitebox Ethernet also create pricing pressure. Memory inflation is another near-term margin risk if higher component costs cannot be fully passed through.

But the broader direction is still favorable for Arista. As inference scales, customers are likely to care more about performance per dollar, operational flexibility, and avoiding overly closed infrastructure stacks. Higher memory and accelerator costs may actually increase the importance of cluster utilization, because idle GPUs and inefficient KV-cache movement become more expensive.

Conclusion

Arista is not the only beneficiary of disaggregated inference. NVIDIA and Broadcom are more direct beneficiaries at the stack and silicon layers. The Arista thesis is more specific: as inference becomes a distributed systems problem, customers that want open, lower-cost Ethernet fabrics should increase spend on switching, automation, and configuration-heavy AI networks. The TickerTrends signals suggest that this second-order pull-through is already showing up.