Shiraz Nazeer writes about enterprise transformation and the challenges of putting AI to work, from managing organisational change to turning AI product ideas into secure, scalable services. The posts describe work with the Fifth9 team.
Useful for accessible thought leadership on enterprise AI risk, change and industrialisation for regulated transformation audiences.
Best for
Resilience, AI governance or security vendors targeting transformation, risk and security leaders at large regulated enterprises.
Not for
AI product or consumer tech brands needing guaranteed reach, rate card and verified audience breakdown.
Key takeaways
Autonomous AI may already be inside regulated enterprises, so redesign operations rather than only harden defences.
Stalled transformation is mass psychology, not just a tooling or communications problem.
Small AI teams must invent and industrialise at once, including security, governance and reliable scale.
Check first: Ask for commercial terms and audience composition; posts show no sponsorship offer or rate card.
Main ideas
Assume autonomous AI is already inside the walls · Stalled transformation is mass psychology, not a tooling problem · Small AI teams must invent and industrialise at once
Shiraz Nazeer writes about enterprise transformation and the challenge of putting AI to work. They connect fast-moving security risks, organisational behaviour and the practical work of building AI products, using vivid humour to show where ambitious plans meet operational reality.
Nazeer’s work makes the tensions behind enterprise change easy to grasp.
In a post about autonomous AI and security, they contrast strengthening defences with planning as though vulnerabilities are already exposed—and favour the more operationally minded response (AI, enterprise risk and security).
A comic picture of a change programme still using the old procurement form becomes a sharper argument: transformation is a problem of organisational psychology, not just tools or communications (why enterprise change stalls).
They apply the same practical lens to AI product development, arguing that invention needs the less glamorous work of security, governance and reliable industrialisation to become useful at scale (from AI invention to industrialisation).
Follow them for accessible arguments about the gap between technology ambition and what organisations can actually operate.
Nazeer's posts argue from one premise: in large organisations, the hard part of AI is not the technology but the human and structural response.
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Three arguments recur: regulated enterprises should plan on the assumption that autonomous AI systems have already breached their walls; stalled transformation programmes are a problem of mass psychology rather than tooling or communications; and small AI product teams are squeezed because they have to invent and industrialise at the same time. Nazeer states a position plainly, develops it through a widely recognised analogy, and closes by asking other practitioners how they see the same shift.
Assume autonomous AI is already inside the walls
Nazeer describes two camps forming in large, heavily regulated enterprises as autonomous AI systems become capable of probing, exploiting, reasoning and adapting at machine speed.
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One wants to red-team aggressively: pressure-test vulnerabilities, simulate attacks and strengthen defences before the threat arrives.
The other assumes the AI is already inside, with every process gap, human loophole and architectural shortcut exposed, and asks how the organisation should operate from that position.
They say the second reading looks more realistic, treating the divergence as a psychological reaction to a shared threat landscape rather than a technical disagreement.
The post compares the situation to old Godzilla films: everyone in the city knows something massive is coming, but some teams build bigger fences, some redesign the city, and some are still debating procurement frameworks while the water starts vibrating. Nazeer jokes about a 70-page steering committee deck in Canary Wharf titled "Strategic AI Resilience Transformation Programme v14_FINAL2."
Stalled transformation is mass psychology, not a tooling problem
Nazeer argues that change programmes fail because they are treated as tooling, communications or buy-in problems when the real difficulty is mass psychology.
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AI agents that can sense adoption patterns, trace influence networks and nudge teams are, in this account, the first real help available on the inside. Once the inside is manageable, differentiation shifts to the outside.
The post opens with a vision deck, a roadmap that could double as Death Star blueprints, a war room of very senior people, and someone promising this time it will work. Six months later, the old procurement form is still in use, staff are running rogue spreadsheets in the cloud, and an AI assistant has approved a leave request from Darth V.
Set beside the first argument, the same technology is framed two ways: in one post, autonomous AI is an incoming threat organisations must survive; in the other, agents are a practical instrument for managing adoption from inside.
Small AI teams must invent and industrialise at once
Writing as an independent boutique, Nazeer describes the gap between creating something genuinely new at 1am and then facing security, governance, scalability, compliance and whatever a client's procurement department asks next.
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Big companies split invention and industrialisation across departments, while lean teams have to do both simultaneously.
Reliable production, they argue, is a capital-intensive moat a small smart team cannot simply jump over.
The two failure modes are clear: invention without industrialisation is a cool demo; industrialisation without invention is another company repackaging what everyone else already sells.
Henry Ford did not just invent cars, the post notes; he worked out how to mass-produce them reliably, and Nazeer calls that the hard part of AI products. The post closes by inviting readers to comment or get in touch and references the Fifth9 team.
This is the clearest statement of the question Shiraz Nazeer returns to: how large, heavily regulated organisations should respond when AI systems can probe, exploit and adapt faster than their change cycles.
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Start here: it lays out both sides of the argument, not just the one Nazeer favours.
Nazeer describes two schools of thought emerging inside large enterprises.
One advocates aggressive red-teaming: pressure-testing vulnerabilities, simulating attacks and strengthening defences before highly autonomous AI arrives (referred to in the post as "Mythos").
The other says to assume AI is already inside the walls, every process gap and architectural shortcut is exposed, and work out how to operate from that position.
They find the second more realistic.
The post compares the situation to Godzilla: everyone sees something massive coming, but some teams build bigger fences, some redesign the city, and some are still debating procurement frameworks.
It closes with a joke about a 70-page steering committee deck in Canary Wharf, then invites practitioners to describe how their organisations are responding.
For brands planning a campaign
Useful for campaigns aimed at security, resilience or risk buyers in regulated sectors. A vendor message about defending the perimeter may land badly with readers who already accept that internal gaps exist; framing the message around operating under the assumption of compromise aligns with this post’s argument.
It explains why transformation programmes stall even when the technology works, and shows Nazeer’s comic register — useful for assessing whether they can carry a serious argument without writing densely.
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Nazeer opens with a parody of how change programmes begin: a vision deck, a roadmap like Death Star blueprints, a war room of senior people and someone promising this time it will work.
Six months later, the organisation is still using the old procurement form, staff are running rogue spreadsheets in the cloud, and an AI assistant has approved a leave request from Darth V.
The argument beneath the humour: change is not a tooling, communications or buy-in problem, but a mass-psychology problem.
Nazeer points to AI agents that can sense adoption patterns, trace influence networks and nudge teams, describing the inside of an organisation as finally manageable — which, in their framing, shifts differentiation to the outside.
The post links to a longer piece and tags the Fifth9 team.
For brands planning a campaign
Relevant to internal communications, change management and adoption-focused campaigns. The post suggests an audience sceptical of launch-deck messaging, so creative that acknowledges failed rollouts and focuses on behaviour rather than tooling is likely to resonate more than a standard implementation story.
This is the most concrete account of Nazeer’s own working context — an independent boutique building AI products — and explains the constraint shaping the other posts: small teams face invention and industrialisation at the same time.
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Nazeer describes the gap between inventing an AI product at 1am and then facing security, governance, scalability, compliance and procurement hurdles.
The Henry Ford comparison carries the argument: Ford did not merely invent cars; he made them mass-producible.
Nazeer calls that reliable production step the hard part of AI products — a capital-intensive advantage, in their words, that a lean team of smart people cannot simply jump over.
The post lays out a trade-off: invention without industrialisation is a cool demo, while industrialisation without invention is another company repackaging the same tools.
Large companies can split those jobs across departments; smaller teams have to do both at once.
Nazeer invites readers to comment or get in touch, and mentions the Fifth9 team.
For brands planning a campaign
Suited to campaigns targeting founders, product leaders or buyers of AI delivery services. The post names the practical objections a lean provider faces — compliance, scalability and procurement — so an offer that takes those burdens off a small team addresses the barrier it describes.
Approach: analogy-led field notes with an open question3
AI risk posture in regulated enterprises1
Enterprise change as mass psychology, not tooling1
Invention versus industrialisation in AI products1
AI risk posture in regulated enterprises
Nazeer describes two positions taking shape inside large, heavily regulated organisations as autonomous AI systems become capable of probing, exploiting, reasoning and adapting at machine speed.
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One group wants to red-team environments, pressure-test vulnerabilities and strengthen defences before a threat arrives.
The other assumes every weakness, process gap and architectural shortcut is already exposed, and asks how the organisation should operate instead.
They favour the second position as more realistic, casting the split as some teams building bigger fences while others redesign the city.
Turns a live policy question into two emerging schools of thought, then offers a personal view rather than a neutral summary.
For brands planning a campaign
A fit for campaigns targeting security, risk, resilience or governance buyers.
The post sets up a concrete choice — harden defences before an attack or assume breach and redesign operations — that a vendor, consultancy or platform can address.
Nazeer argues that transformation programmes fail when organisations treat them as tooling, communications or buy-in problems rather than challenges in mass psychology.
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The post opens with the familiar signs of a stalled programme: an ambitious vision deck, a roadmap, a war room of senior people and a promise that this time will be different.
Six months later, the old procurement form is still in use.
They argue that AI agents which sense adoption patterns, trace influence networks and nudge teams offer the first real help available on the inside, shifting differentiation to the outside.
Makes a serious argument about behaviour change through Star Wars imagery and self-deprecating corporate humour, rather than data or case studies.
For brands planning a campaign
A fit for consultancies, change-management platforms or internal-comms vendors. The post makes a quotable case that adoption is behavioural, not technical, and claims agents can read adoption patterns and influence networks — a capability vendors can support, extend or challenge.
Writing as an independent boutique, Nazeer explores the tension between creating something new and making it viable: security, governance, scalability, compliance and the procurement hurdles in a client's buying process.
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They argue that big companies split invention and industrialisation across departments, while lean teams must do both at once.
Industrialisation is a cash-intensive moat that a small, smart team cannot simply jump over.
Invention without industrialisation is a cool demo; industrialisation without invention is repackaging what everyone else already sells.
For brands planning a campaign
A fit for platform, infrastructure, security or compliance vendors targeting small AI studios and boutique product teams. The post names specific blockers — procurement, governance, scalability and cash — and frames the audience as capable teams constrained by scale, not skill.
Approach: analogy-led field notes with an open question
Across the three posts, Nazeer starts with an observation from enterprise or boutique client work, develops it through a widely recognised analogy (Godzilla, Star Wars, Henry Ford), states a view plainly and invites comment or conversation.
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The short opinion pieces close with the Fifth9 team and a recurring hashtag.
An analogy-led structure, a stated opinion and a direct question to the audience.
The posts frame the same technology in two ways: as an incoming threat organisations must survive, and as a practical instrument for managing adoption inside the organisation. A campaign should choose which framing it supports rather than treating them as one position.
For brands planning a campaign
A fit for opinion, panel or viewpoint campaigns: the post uses metaphor-driven thought leadership rather than an instructional or evidence-led format. Its closing invitations signal openness to dialogue, while recurring team references point to a shared practice rather than a solo commentator.
The posts name one business: Fifth9, which Shiraz Nazeer describes as their team and as the group carrying out the enterprise AI and transformation work discussed in the posts.
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The posts establish an affiliation with Fifth9, but not an ownership stake.
Fifth9
Employment
Nazeer writes about work being done “from the team at Fifth9,” calls Fifth9 “my team,” and says they are testing agent-based approaches to enterprise change.
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The posts establish an affiliation with Fifth9, but not their job title or ownership stake; ask for those details if they matter to a partnership.
Shiraz Nazeer's published material is a short, consistent run of LinkedIn field notes on how large organisations absorb AI.
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Read the three posts in any order as one argument: the first sets out two camps in regulated enterprises, one hardening defences before autonomous AI arrives and one assuming it is already inside; the second argues that stalled transformation programmes are a mass-psychology problem, not a tooling or communications one; the third turns to small AI product teams that must invent and industrialise at the same time.
Each post starts from something observed in client or team work and carries the point through a familiar analogy (Godzilla, Star Wars, Henry Ford).
Nazeer states a view plainly and ends by asking other practitioners how they see the same shift.
Read together, the posts reveal recurring vocabulary — Mythos, agents, procurement, governance, behavioural operating model — and a habit of closing with a question, making the comments useful alongside the posts.
Both write short LinkedIn posts arguing that AI has changed the security equation for large organisations, rather than merely adding a tool.
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Nazeer's two-camps post and Geffen's posts press the same question: if AI can probe, exploit and reason at machine speed, defenders need a matching step-change, not just more headcount. Both use a recent industry signal — a public model release or a news report — to anchor the argument.
Geffen writes from inside a security vendor, making the case for continuous AI pentesting at machine speed.
The posts name specific models, a news report and a company, then move quickly to a capability claim.
Nazeer writes from an enterprise-change and boutique-consulting vantage point, focusing on organisational posture and psychology without naming a product or client.
Follow Geffen for defensive tooling specifics; follow Nazeer for the adoption and change-management framing.
Both are independent professional-services voices using the same LinkedIn format: a short first-person observation, one governing idea, a closing question and a repost or follow prompt.
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Abramson's pitch-panel post and Nazeer's transformation post share a diagnosis: firms and programmes fail because of human perception, not credentials or tooling. Both use a memorable analogy to carry the point.
Abramson focuses on client origination and partner-led growth in the London professional-services market; AI is not the topic.
Their posts are more directional, with numbered next-step prompts for readers, while Nazeer's stay closer to field observation and invite open discussion.
Follow Abramson for business-development craft in services firms; follow Nazeer for AI and enterprise-change commentary.
Both post about AI and cybersecurity for enterprise audiences, sharing the premise that attackers are automating faster than most teams can respond.
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Nazeer's 'assume the AI is already inside' framing and Watkins's phishing and firewall posts reach the same conclusion from different directions: defence must be continuous and automated rather than periodic.
Watkins's long episode summaries feature guest interviews, a downloadable open-source automation build, timestamps and direct watch links, giving readers something to install.
Nazeer writes a few hundred words of opinion, without assets, how-to guidance or tooling.
Follow Watkins for practical automation builds and practitioner interviews; follow Nazeer for the strategic and cultural argument around the same technology.
Shiraz Nazeer has 3,027 LinkedIn followers, observed in October 2026.
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Three posts focus on enterprise AI adoption, organisational change and AI product development, suggesting appeal to people working in transformation, risk and technology delivery. To assess relevance for a campaign targeting enterprise AI adoption, ask for audience survey results or campaign reports showing the job levels and industries of people engaging with comparable posts.
No sponsorship offer, rate card, or partnership terms appear in the posts.
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Shiraz Nazeer’s posts read as personal commentary on enterprise AI adoption and organisational change rather than paid placements.
The only commercial name attached to them is Fifth9, described as the team they work with on this thinking—not as a disclosed sponsor.
The recurring #HowWeSeeIt tag runs across the posts as a series label, not a campaign marker.
Any arrangement would need to be negotiated through the LinkedIn profile, the only contact point mentioned here; there is no media kit, partner page, or booking route.
Ask for terms, audience data, rates for a sponsored post or co-authored piece, and examples of past paid collaborations before committing spend.
A resilience, AI-governance or security-posture vendor wants a sponsored thought-leadership placement for transformation, risk and security leaders at large regulated enterprises. The vendor wants to tie its position to a live industry question rather than a product demonstration.
Reasons
Shiraz Nazeer frames the question this kind of campaign needs. Their strongest post lays out two competing responses to autonomous AI in heavily regulated organisations: harden the perimeter with red-teaming and attack simulation, or assume the AI is already inside the walls and redesign how the organisation operates. They prefer the second, describing teams that build bigger fences while others redesign the city. A vendor can join that argument, extend it or argue against it, giving the campaign a ready-made either/or structure.
The post describes the audience the brief targets: large, heavily regulated enterprises where change moves slowly, plus the risk and operational-resilience functions around that decision. This is a narrower, more specific claim than follower count, drawn from the content itself.
Nazeer's argument about stalled programmes gives a resilience or governance vendor a second usable angle. The post on enterprise change says the failure is not tooling, communications or buy-in but mass psychology. It names the artefacts of a dead programme: a vision deck, a roadmap, a war room of senior people, and someone promising this time will be different. A vendor selling adoption or governance tooling can position its offer against that diagnosis.
The register suits sponsored content. Each post starts with an observation from enterprise or boutique work, develops it through a widely recognised analogy, states a view plainly and closes by asking readers what they think. A brand can commission this format again for a subsequent placement; it invites comments rather than ending with a product link.
Nazeer also writes credibly about the constraints on AI delivery, naming procurement, governance, scalability, compliance and cash as obstacles a lean product team cannot simply jump over. A vendor whose offer removes those burdens has a direct connection to the piece that describes them.
Conditions
This is an editorial-fit assessment. Nazeer's posts include no sponsorship offer, rate card, media kit or booking route. Negotiate terms through the LinkedIn profile, the only verified contact point.
Ask for audience-composition data before committing spend. The available audience evidence is a follower count of 3,027, observed in October 2026. Job titles, seniority, industry and geography are unknown, so a buyer cannot confirm that transformation officers are in the room.
The corpus consists of three short posts. Fit rests on a consistent point of view and a recognisable register, not measured campaign results. Budget for a paid test rather than a long commitment.
When to choose a specialist
Poor fit as briefed
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Brief
An AI product or consumer technology brand wants a short-deadline sponsored post with a guaranteed impressions or engagement target, a documented rate card and a verified audience breakdown by job title, seniority and industry.
Reasons
Nazeer's posts provide no commercial groundwork for a campaign with a fixed launch date and guaranteed reach. There is no published sponsorship offer, rate card, partner page or booking route. The only commercial name attached to the posts is Fifth9, described as the team Nazeer works with on this thinking; it reads as the source of the observations, not a disclosed sponsor. Ask for rates, availability and the basis for any reach commitment before setting a launch date.
The audience evidence cannot support a targeting promise. The only measured figure is a LinkedIn follower count of 3,027. There is no survey, campaign report or breakdown of follower characteristics. A vendor buying access to chief transformation officers or product leaders would be buying an inferred audience, not a verified one. Ask for audience data before committing spend.
The content offers no performance proof to build on. None of Nazeer's posts contains data, client names, outcome figures or a case-study result. A campaign built around measured return, benchmark results or a documented implementation story would need a separate example.
The format is another consideration: these are short opinion posts, not tutorials. Nazeer's posts include no step-by-step method, dataset, long-form article, video or transcript. A campaign that needs instructional content, a product walkthrough or a technical explainer would require a format not demonstrated here.
A cautious buyer should also consider a framing conflict. One post treats the same technology as an incoming threat organisations must survive; another presents it as a practical instrument for managing adoption inside the organisation. A brand seeking a single, consistent position on AI may prefer a writer who argues from one side.
Conditions
Nazeer's posts publish no partnership information. Ask directly about commercial terms; a conversation could change the picture.
If the brand can drop the guaranteed-reach requirement and accept an editorial placement with unverified audience composition, the brief moves closer to the good-fit case above.
Treat the creator as unverified for partnership purposes until they share terms, rates and audience data. Reassess against a shortlisted alternative in the meantime.
A worked campaign example
Angle
Build the campaign around the choice Nazeer already frames, then attach the platform to the side that argues for it. The placement states the split plainly: one camp strengthens the walls before autonomous AI arrives; the other assumes every process gap and architectural shortcut is already exposed and redesigns how the organisation operates. Nazeer calls the second more realistic. The brand can fund the argument and show how its tooling supports operating under that assumption.
Brief
A change-management and AI-adoption platform wants to reach transformation leads in regulated enterprises. The brand sells tooling that maps how adoption spreads through a large organisation. Unable to outbid incumbents on awareness, it wants a viewpoint placement that positions it as the vendor with the clearest diagnosis of why programmes stall.
Deliverables
One LinkedIn post in Nazeer's established register: open with a field observation from enterprise work, develop the argument through a widely recognised analogy, state a view plainly rather than summarising neutrally, and close with a direct question to practitioners.
A linked longer piece that develops the same argument for readers who follow through, retaining the analogy-led opening and behavioural framing rather than switching to a feature list.
A comment-thread follow-up in which the brand responds to practitioners describing how their organisations are reacting, turning the placement into a conversation rather than a single impression.
Measurement and terms
There is no verified engagement data for comparable posts, so reach and interaction targets cannot be evidence-based. Agree on measurement after settling terms, and treat any pre-agreed impression target as a commercial ask, not one supported by current evidence.
Next steps
Contact Nazeer through the LinkedIn profile, the only verified route, and ask for rates, audience data and examples of past paid collaborations.
Ask for job-title and industry breakdowns among people engaging with comparable enterprise-AI posts before approving a budget.
If terms and audience data come back, start with one placement in the register described above as a test; the reviewed corpus is three short posts, so ask for evidence before committing to a longer campaign.
If no response is forthcoming, treat the creator as unverified for partnership purposes and reassess against a shortlisted alternative.
Why this angle
The two posts on enterprise change supply both halves of the pitch. One gives the threat framing and regulated-enterprise setting, including the two schools of thought and the line about teams building bigger fences while others redesign the city. The other offers a diagnosis a change-management vendor can own: programmes fail because they are treated as tooling, communications or buy-in problems when the real difficulty is mass psychology. Their closing invitations to comment or get in touch mean the format already ends where a campaign wants the reader to land.
Choose Dylan Watkins if the Chief Transformation Officer wants a practical view of how AI-enabled security workflows can be built and applied, rather than a discussion centered on how enterprise culture responds to AI.
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Their posts describe a live phishing-analysis workflow, incident-data and playbook use, and security steps for small teams and home networks. That makes Watkins a better fit for a campaign focused on operational examples of AI in security.
The posts focus on cybersecurity workflows, not enterprise-wide AI adoption or cultural resistance. They do not show how to lead transformation across a large regulated organisation, so Shiraz Nazeer is a narrower choice for a campaign aimed at that audience.
Choose Ido Geffen if the decision is specifically about the changing cybersecurity threat and defensive response to AI.
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Their posts discuss AI agents finding vulnerabilities, collaborating on attack paths, and the need for machine-speed defence. That gives a security leader more directly threat-focused material than Shiraz Nazeer's broader treatment of organisational change and AI risk posture.
The posts concentrate on cybersecurity capability, not cultural resistance or the behavioural side of enterprise transformation. They are a weaker match if the CTO wants to assess how to bring people and operating practices through AI adoption.
Three LinkedIn posts by Shiraz Nazeer: a post on two enterprise responses to autonomous AI risk in regulated industries, a post arguing enterprise change is a mass-psychology problem, and a post on the invention-versus-industrialisation challenge for independent AI product teams.
The posts do not say when they were published, so no recency or chronological claim is made.
A LinkedIn profile snapshot observed 11 October 2026 reported 3,027 followers; no audience breakdown was available.
Shiraz Nazeer's posts do not include long-form articles, talks, transcripts, client names, outcome figures, media kit or rate card.
Similar-creator comparisons draw on two posts each from Ido Geffen, Steve Abramson and Dylan Watkins.
