Zeitgeist #13Saturday, October 31, 2026
Underlying Desire
At a deeper level, this trend is about control. Companies want the upside of autonomy, speed, and scale, but they do not want to surrender accountability, trust, or the feeling that a human is still in charge. Agents promise leverage, yet governance is what makes that leverage psychologically and operationally acceptable. The real human need here is not automation, it is confidence that the machine can act without creating chaos.
Key Evidence
McKinsey says 44% of organizations now report AI is scaling across the enterprise, up from 38% a year earlier, a clear sign the technology is moving from experimentation into operations. Anthropic reports that 60% of enterprise agent use cases are data analysis and report generation, 48% are internal process automation, and 56% plan to deploy agents for research and reporting in the next year. G2 says AI agent adoption among B2B software buyers rose from 69% to 86% year over year, while 66% of companies now run agents in limited or full production.
Why Now
Two things changed. First, agents crossed the threshold from novelty to workflow utility, especially in analysis, reporting, and internal automation. Second, enterprise teams are now discovering the messy part: permissions, handoffs, QA, and audits do not disappear just because the interface is conversational. Microsoft’s report suggests the market is already responding by formalizing workflows and standards. That is the unlock for startups: the need for governance is arriving exactly as agent adoption becomes real, measurable, and budgeted.
Zenity builds an end to end security and governance platform for AI agents and enterprise copilots.
Outcome: Raised $125 million in August 2026, after a $38 million Series B in October 2024. The company says it has tripled revenue in each of the past two years and is on track to triple revenue again this year. ([zenity.io](https://zenity.io/company))
Noma Security provides an AI security and governance platform that discovers, governs, and protects AI agents across enterprise environments.
Outcome: Raised $100 million in Series B funding in July 2025 and says it now secures more than 2.6 million agents across enterprise customers. It is also trusted by over 10% of the Fortune 100. ([noma.security](https://noma.security/blog/noma-security-raises-100m-to-drive-adoption-of-ai-agent-security?utm_source=openai))
A governance layer for companies deploying AI agents across support, finance, ops, and knowledge work. It would sit between the agent and the business systems it can touch, enforcing permissions, approvals, policy rules, logs, and rollback. The target customer is any midmarket or enterprise team that wants to ship agents without creating a compliance nightmare. It works because the fastest-growing buyer need is not generation, it is proof: who did what, why it happened, and whether a human signed off.
A rules engine that decides whether an agent can act, must ask a human, or should be blocked entirely based on the task, data sensitivity, and business unit. The customer is the internal platform or IT team inside companies adopting multiple AI agents from different vendors. It would work because most organizations do not need more AI, they need a single control plane that turns vague policy into executable workflow logic.
Underlying Desire
At the deepest level, this trend is about trust under uncertainty. Businesses want the upside of AI without the social, legal, and reputational risk of feeling like they are flying blind. Compliance software wins when it gives people something more valuable than a pass-fail answer: the feeling that they can move fast, explain themselves, and stay in control while using a technology they do not fully understand.
Key Evidence
The EU AI Act’s transparency obligations became enforceable on 2 August 2026, and most broader enforcement also begins then, according to the EU AI Act Service Desk. Some synthetic-content marking and detection requirements have a transition period until 2 December 2026, per the AI Act Service Desk. The Commission’s implementation timeline says high-risk system rules arrive later, in 2027 and 2028, which stretches compliance demand over several years rather than one deadline.
Why Now
What changed is that the law stopped being abstract and became operational. The European Commission has now published specific transparency guidance, so vendors have enough detail to productize requirements instead of guessing. At the same time, the staggered rollout creates immediate urgency and long tail demand. Companies need tooling now for transparency and synthetic-content rules, while higher-risk obligations are still ahead, which means buyers are budgeting for a compliance stack, not a temporary project.
AI risk and compliance management platform for governing AI systems, mapping them to the EU AI Act and other frameworks.
Outcome: Raised a €6 million Series A in July 2026 led by Ventech; the company says it serves large enterprises and mid-market customers. ([naaia.ai](https://naaia.ai/en/naaia-raises-6-million-euros-trusted-ai-europe/?utm_source=openai))
Compliance-by-design AI platform that turns regulations into executable logic for regulated enterprise workflows.
Outcome: Raised $8.6 million in seed funding in April 2026 from Chalfen Ventures, Osage Venture Partners, Accenture Ventures, and Rock Yard Ventures. The company and investor materials describe it as founded in 2024, and its site says it is built for GxP-regulated environments. ([iridius.ai](https://iridius.ai/blog/iridius-seed-funding/?utm_source=openai))
A compliance workspace for companies shipping AI features in Europe. It maps each model and feature to the relevant EU AI Act obligations, then auto-generates transparency notices, internal controls, audit logs, and evidence packs for legal and security teams. It would work because most companies do not need another policy PDF, they need a living system that connects product changes to obligations before launch. The buyer is the head of legal, compliance, security, or product at any company deploying AI in customer-facing workflows.
A developer tool that plugs into AI applications and automatically adds the right user-facing disclosures, provenance labels, synthetic-content markings, and policy checks. The target customer is product teams building AI assistants, content tools, and media workflows that need to ship fast without creating legal debt. It would win by turning compliance into an API-level feature instead of a manual review process, which makes it easy to adopt across multiple teams and products.
Underlying Desire
At the core, this trend is about regaining control over a body and identity that have felt hard to manage. GLP-1s promise not just weight loss, but relief: less friction around eating, fewer cravings, more predictability, and a new sense of self that feels legible again. The software opportunity emerges because people do not just want to lose weight, they want tools that help them live inside a changing version of themselves without having to rebuild every habit manually.
Key Evidence
EY’s October 2026 report says 54% of U.S. GLP-1 users now take the medication primarily for weight management, showing the use case has shifted beyond clinical treatment. EY projects that by 2033, 35% to 40% of U.S. adults will have used a GLP-1, which implies mass-market adoption. PwC’s 2026 research found apparel spend rose 9.9% among users after 6 to 8 months on the drug, a concrete signal that consumer behavior is already changing.
Why Now
The shift became actionable because GLP-1 usage moved from a medical edge case to a broad consumer behavior. EY’s forecast of 35% to 40% adoption by 2033 means founders can no longer treat these users as a narrow segment. At the same time, PwC’s spending data shows the changes are measurable now, which makes it possible to build products around real behavioral shifts instead of speculation.
A GLP-1 companion app that helps people track injections, weight, side effects, and progress on weight-loss medications. ([shotsyapp.com](https://shotsyapp.com/?utm_source=openai))
Outcome: Founded in 2024, it has raised about $2.25 million in seed funding and crossed 100,000 downloads, with one report citing a $1 million revenue run rate and another noting close to one million downloads. ([b17news.com](https://b17news.com/a-software-engineer-built-an-app-for-tracking-her-weight-loss-shots-in-8-months-its-crossed-100000-downloads-and-has-2-million-from-vcs/?utm_source=openai))
A digital weight-management app that pairs GLP-1 treatment with medical guidance, nutrition coaching, and ongoing support. ([hexa.com](https://www.hexa.com/blog/news/introducing-batch-2024-hx24?utm_source=openai))
Outcome: Annette was founded in 2024 and raised a EUR 2 million pre-seed round in September 2025, according to Preqin, with Hexa and investors saying the company is being built to support tens of thousands of patients. ([preqin.com](https://www.preqin.com/data/profile/asset/annette-sas/786012?utm_source=openai))
PortionPilot is a meal planning and grocery recommendation SaaS for GLP-1 users and the dietitians, coaches, and clinics that support them. It personalizes recipes, grocery lists, and portion sizes based on appetite changes, side effects, protein targets, and budget, so users can keep eating well as their habits shift. It would work because GLP-1 users do not just need calorie tracking, they need a system that adapts week by week as their relationship with food changes.
ShiftSignal is a consumer intelligence platform for retailers, brands, and ecommerce teams that detects when customers are likely on GLP-1s based on basket changes, reorder patterns, and category switching. It helps brands adjust offers, merchandising, and retention messaging for smaller baskets, different apparel demand, and new replenishment cycles. It would work because the impact is already showing up in spending data, and retailers need a way to respond before their dashboards explain the change too late.
Underlying Desire
The deeper desire is trust without friction. Businesses and consumers want money to move instantly, globally, and cheaply, but they do not want to think about blockchains, private keys, regulatory exposure, or operational edge cases. Stablecoin rails are attractive because they promise the ancient promise of money, portability and finality, with modern software speed. Underneath the economics, this trend is about people and companies wanting to send value across borders and between systems without uncertainty, delay, or bureaucratic drag.
Key Evidence
The Federal Reserve requested public comment on a regulatory framework for payment stablecoin issuers under the GENIUS Act on September 24, 2026, a clear sign that U.S. policy is moving from debate to implementation, according to the Federal Reserve. The OCC issued a final rule in late October 2026 to implement the GENIUS Act and said it will closely monitor payment stablecoin issuers, according to the OCC. The BIS 2026 Annual Economic Report says regulation-compliant stablecoin markets are part of the evolving cross-border payments landscape and flags uneven performance as a core issue, according to the BIS.
Why Now
The key shift is regulatory clarity. With the Federal Reserve and OCC both taking formal action in 2026, stablecoin usage is no longer just tolerated by early adopters, it is being shaped for mainstream financial infrastructure. That unlocks procurement, vendor onboarding, and partnerships that were previously blocked by compliance uncertainty. What changed is not the technology, it is the buying behavior of regulated institutions and the confidence of the people who write the checks.
Stablecoin infrastructure and APIs for businesses that need to receive, store, issue, convert, and spend digital dollars.
Outcome: Bridge was acquired by Stripe in a $1.1 billion deal, and Bridge says it has issued over $1 billion across dozens of stablecoins. ([stripe.com](https://stripe.com/newsroom/news/stripe-completes-bridge-acquisition?utm_source=openai))
Enterprise stablecoin payments infrastructure that helps businesses move money across fiat, crypto, and blockchain rails.
Outcome: BVNK reported $50 billion in annualized volume on its site, and Mastercard completed its acquisition of BVNK in August 2026. ([bvnk.com](https://www.bvnk.com/about-us?utm_source=openai))
RailGuard is a compliance and risk monitoring SaaS for banks, fintechs, and payment companies launching stablecoin products. It would automate issuer due diligence, wallet screening, transaction monitoring, sanctions checks, and audit-ready reporting, giving regulated teams a single control plane for stablecoin operations. This works because the biggest blocker to adoption is not token issuance, it is making the workflow safe enough for legal, compliance, and risk teams to approve.
SettleFlow is a merchant settlement and treasury platform for marketplaces, PSPs, and cross-border commerce platforms that want to accept stablecoins without rebuilding finance ops. It would handle invoice generation, payment routing, automatic conversion, reconciliation, payout scheduling, and treasury optimization across fiat and stablecoin balances. This wins because merchants do not want crypto exposure, they want faster settlement and lower fees with clean accounting.
Underlying Desire
People are not really demanding less AI. They are demanding certainty. In a media environment where anyone can generate a convincing face, voice, post, or video in seconds, humans are reaching for an old instinct: they want to know who made this, why it exists, and whether they can believe it. Underneath the fraud fears and influencer skepticism is a deeper need for orientation, social proof, and accountability. Trust is how people reduce cognitive load when everything online starts to look synthetic.
Key Evidence
Adobe’s 2026 Creators’ Toolkit report says 87% of creators using creative AI believe it accelerated their business or audience growth, and 75% say it is integrated or essential to how they work. Gartner found in September 2026 that 35% of consumers rely on influencers less because of AI. The World Economic Forum reported in October 2026 that content credentials and invisible watermarking are becoming important tools for identifying AI-generated content and preserving authenticity.
Why Now
AI content moved from novelty to default faster than trust infrastructure did. Creators and brands adopted generative tools first, and now platforms, consumers, and regulators are scrambling to catch up. What changed recently is not the existence of synthetic media, it is scale: enough AI content is now flowing through everyday feeds that provenance and authenticity are becoming operational requirements, not abstract policy debates.
Deepfake detection platform for enterprise, government, and media teams that analyzes audio, video, images, and text for synthetic manipulation.
Outcome: Raised $33M in total funding, including a $15M Series A in 2023 and an expanded Series A to $33M in 2024; the company says it has detected millions of deepfakes and works with major enterprises, governments, and platforms. ([prnewswire.com](https://www.prnewswire.com/news-releases/reality-defender-expands-series-a-to-33-million-to-enhance-ai-detection-capabilities-302283098.html?utm_source=openai))
Cybersecurity company that provides real-time deepfake detection, continuous identity verification, and digital media forensics.
Outcome: Raised $17.5M in Series A funding in March 2025, backed by Forgepoint Capital, Ballistic Ventures, Evolution Equity, Cisco Investments, IQT, and Capital One Ventures; its trust center also shows SOC 2 Type 2 status in 2026. ([g2.com](https://www.g2.com/sellers/reality-defender?utm_source=openai))
A SaaS platform that adds provenance and authenticity metadata to creator, brand, and newsroom content across image, video, audio, and text. The target customer is creator platforms, media companies, and enterprise marketing teams that need to prove what is human-made, AI-assisted, or fully synthetic before content is published. It would work because trust is becoming a distribution requirement, and teams need a simple workflow that plugs into existing CMS, DAM, and social publishing tools instead of forcing a rebuild.
A browser extension and API that shows a real-time trust score for posts, profiles, ads, and videos across the web. The target customer is consumers, brand safety teams, and marketplaces that want a fast signal for synthetic risk, manipulated media, and creator authenticity. It would work because skepticism is rising, and a visible trust layer can become a default part of browsing, ad review, and influencer vetting.
Underlying Desire
At the deepest level, this trend is about control over scarcity. Large-load customers want certainty that they can get power when they need it, utilities want visibility into what is coming so they can avoid overloads, and regulators want a system that feels fair instead of arbitrary. Software becomes valuable here because it reduces uncertainty, turns black-box queues into understandable decisions, and gives every participant a sense of agency in a system that has historically felt slow, opaque, and politically brittle.
Key Evidence
Lawrence Berkeley National Laboratory said in its June 2026 report that rapid growth in data centers and other large loads is creating new grid-planning bottlenecks that slow large-load connections. FERC ordered regional grid operators in June 2026 to justify or reform tariffs for data centers and other large users because of soaring large-load demand. The IEA’s 2026 grid report says AI data centers are a fast-growing source of demand that can worsen congestion, while digital tools can improve grid planning and operations.
Why Now
This is actionable now because regulators are actively intervening, which means the market is moving from passive planning to forced modernization. At the same time, AI-driven load growth is creating a concrete pain point that utilities can no longer ignore, so software that speeds interconnection, improves forecasting, or optimizes connection terms has a direct buyer.
Grid software for utilities that helps them plan and operate flexible grid connections for large loads, distributed energy resources, and data centers.
Outcome: Camus surpassed $25 million in Series A funding in February 2024, after closing a $10 million extension. It also says its software reaches seven million utility customers through partners and was selected for Google for Startups Accelerator in August 2026. ([camus.energy](https://www.camus.energy/blog/camus-surpasses-25m-in-series-a-funding?utm_source=openai))
An AI-powered power company and software platform that forecasts prices and optimizes batteries, flexible loads, and energy procurement.
Outcome: Gridmatic announced a $50 million energy storage fund in November 2023, and in March 2026 it launched a daily settlement and automated load control solution for large flexible loads. It says it is active across six U.S. ISOs. ([gridmatic.com](https://www.gridmatic.com/news/?utm_source=openai))
A workflow SaaS for utilities, grid operators, and large-load developers that maps interconnection queues, capacity constraints, and approval milestones in one place. It would help developers see where power is actually available, help utilities prioritize projects, and help both sides model the impact of new load on local grid conditions. It works because the current process is fragmented across emails, PDFs, spreadsheets, and hand-built assumptions, which creates delay and mistrust. QueueSense turns that process into a shared system of record.
A decision-support tool for data center operators and industrial customers that helps them choose sites, negotiate connection terms, and model tariff outcomes before they commit capital. It would combine utility data, queue data, and scenario modeling to estimate time-to-power, total cost, and curtailment risk. This would win because every hyperscaler, colo operator, and large enterprise now faces the same problem: power is becoming a strategic input, not a utility afterthought. If the software can shave months off site selection or avoid a bad location, it pays for itself immediately.
Underlying Desire
At its core, this trend is about trust surviving a systems upgrade. Organizations do not actually want “post-quantum crypto,” they want continuity: the ability to keep identities valid, data private, signatures trusted, and operations stable even as the underlying security assumptions change. The deeper human need is protection from invisible future failure, because the scariest bugs are the ones that sit quietly for years before suddenly invalidating everything you built.
Key Evidence
NIST published its transition roadmap on 27 October 2026 and explicitly says organizations must move from quantum-vulnerable algorithms to quantum-resistant standards, according to NIST’s report. NIST’s PQC site says “now is the time to migrate,” signaling that finalized standards are ready for implementation, per NIST. NIST also released 2026 working drafts to update PIV standards for PQC, showing the migration is already reaching federal operational systems, according to NIST’s June 2026 announcement.
Why Now
The key change is that the standards layer is now mature enough to build against. NIST has moved from recommendation to migration guidance, which means security teams can no longer treat PQC as speculative research. At the same time, procurement and compliance are beginning to encode these standards into real systems, especially in federal and regulated environments.
Post-quantum cryptography company that helps organizations replace legacy RSA and elliptic curve cryptography with quantum-resistant alternatives.
Outcome: Raised $37 million in Series B funding in June 2024, led by Addition, with participation from Chevron Technology Ventures, Legal & General, Braavos Capital, and Oxford Science Enterprises. PQShield also says it has more than 100 experts across 11 countries. ([prnewswire.com](https://www.prnewswire.com/news-releases/pqshield-raises-37m-in-series-b-funding-to-deliver-the-widespread-commercial-adoption-of-quantum-resistant-cryptography-302175607.html?utm_source=openai))
Enterprise AI and quantum company that sells cryptography management tools, including post-quantum migration support.
Outcome: SandboxAQ says it raised over $450 million in its Series E round, closed in April 2025, and its earlier launch announcement named customers including Vodafone Business, SoftBank Mobile, Mt. Sinai Health System, and Wix. ([sandboxaq.com](https://www.sandboxaq.com/sandboxaq-insights/what-is-sandboxaq))
A SaaS platform that scans an organization’s infrastructure, codebases, certificates, VPNs, identity providers, and vendor stack to identify every quantum-vulnerable dependency. The target customer is the CISO, security architect, and compliance lead at mid-market and enterprise companies, especially those in finance, government, healthcare, and critical infrastructure. It would work because most teams do not know where legacy cryptography is embedded, and the first sale in any migration is visibility. The product can expand into prioritization, remediation workflows, and executive reporting once discovery is complete.
A B2B tool that helps enterprises assess vendor readiness for post-quantum migration using questionnaires, attestations, evidence collection, and risk scoring. The target customer is procurement, vendor risk, and security compliance teams that need to determine which suppliers are actually ready for PQC and which are just marketing it. It would work because migration will not succeed if third-party dependencies remain broken, and enterprise buyers need a repeatable way to pressure vendors, compare responses, and document decisions.
Lev turns an idea into the things a company actually runs on: positioning, market and competitive research, a pitch deck, and a go-to-market plan.
Start building for free