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Grammarly’s Expert Reviews Feature Comes with a Scary Realization

Author: Ciente Editorial Team

Grammarlys-News
News

Grammarly’s Expert Reviews Feature Comes with a Scary Realization

Grammarly’s Expert Reviews Feature Comes with a Scary Realization

AI tools are moving from correcting sentences to simulating expertise. That shift is starting to worry the people being simulated.

Grammarly built its reputation fixing grammar mistakes. Now it wants to replicate expertise.

The company recently introduced an “Expert Review” feature that analyzes a document and generates feedback, inspired by” well-known writers, academics, and journalists. The idea is simple: your draft gets reviewed through the lens of recognized authorities in a field.

The problem is that those experts were never involved.

Reports found the system generating comments that seem to come from real individuals without their permission. Some users even saw feedback attributed to editors from substantial publications like The Verge and The New York Times.

Its feature relies on publicly available work and does not claim endorsement from the named experts, says Grammarly. But the presentation is where things get uncomfortable.

In tools like Google Docs, the suggestions appear visually similar to comments from a real editor. That design choice blurs the line between AI-generated advice and human critique.

For technology leaders, the controversy highlights a deeper tension in generative AI.

Large language models learn patterns from public text. That includes the tone, logic, and rhetorical habits of individual writers. Turning those patterns into a product- especially one that attaches a real person’s name- moves the conversation from training data to identity.

And identity is harder to defend as “fair use.”

The feature also exposes a practical limitation of AI expertise. Writing style can be modeled. Editorial judgment is harder. A system trained on published articles may mimic how someone writes, but that does not mean it understands how they think.

That difference matters.

AI is rapidly becoming a collaborator in professional work, from code reviews to legal drafts. But the Grammarly episode shows how quickly assistance can slip into simulation.

And once software starts simulating people, the debate is no longer about productivity. It becomes about ownership- of voice, reputation, and expertise.

SaaS Performance Marketing
Blogs

The Rules of the Game Are Different in SaaS Performance Marketing

The Rules of the Game Are Different in SaaS Performance Marketing

What if the reason your SaaS growth is stalling has nothing to do with ad spend, and everything to do with how you define performance in the first place?

Most marketing advice is curated for a world where you sell a product once and move on. It works until the transaction is complete. The relationship, however, is over.

SaaS does not work that way. And that single difference breaks almost every conventional performance marketing playbook written before 2015.

If you are a CEO, a CGO, or a board member trying to make sense of why your CAC keeps rising while your growth rate plateaus, the answer is rarely a bad ad.

It’s generally a structural misunderstanding of what performance marketing actually means inside a subscription business. This piece is not about clicks and conversions. It’s about why SaaS demands an entirely different performance philosophy and what leadership must do about it.

Why Traditional Performance Marketing Fails SaaS Businesses

Traditional performance marketing is precise and clean- that’s why marketers still opt for it. You spend a dollar, track its impact, and then optimize. CPC, CPL, and CPA- the metrics are clean. The feedback loops are fast.

But in SaaS, acquisition is not the end of the value chain. It is the beginning. And this is where most companies, even well-funded ones, build their marketing strategy on a fundamental lie.

The lie goes like this: if we drive enough trials or signups at an acceptable CAC, we are performing.

Leadership approves the channel mix, the budget gets allocated, the demand gen team reports green dashboards, and yet the business quietly bleeds through churn. Revenue retention is at 85%, while it should be at 110%. NRR becomes a talking point in board meetings that nobody quite wants to confront head-on.

The problem is not the spending. The problem is the definition of performance itself, which becomes clearer when companies deeply understand core SaaS metrics that drive long-term growth.

In SaaS, a customer who churns in month three is not an acquisition success with a retention problem. It is a performance marketing failure. You can separate acquisition performance from retention outcomes, but then you create a structural incentive to optimize for the wrong thing.

And that misalignment is how growth-stage companies stall, something frequently discussed when analyzing broader SaaS growth frameworks.

SaaS Performance Marketing Metrics That Actually Drive Revenue

Let’s be direct about the metrics the SaaS performance marketing must focus on.

1. CAC Payback Period

The first metric that matters is not CAC in isolation. It is the CAC payback period in the context of your average contract value and churn rate. A 14-month payback period looks acceptable on paper until you layer in a 25% annual churn rate. You are now acquiring customers you will never fully monetize. The unit economics do not lie; most teams choose not to read them this carefully, even though understanding SaaS marketing ROI and performance metrics is fundamental for leadership decisions.

2. Pipeline Quality vs. Pipeline Volume

Any competent demand gen function can fill a CRM with leads, which is why many organizations invest heavily in lead generation strategies tailored for SaaS businesses.

The harder question is what percentage of those leads convert to customers who expand their contracts in year two, which ultimately depends on effective SaaS customer segmentation strategies. That number tells you whether your targeting is precise or whether you are burning budget acquiring accounts that were never a genuine fit.

3. LTV: CAC Ratio

LTV projections that assume indefinite retention without modeling real churn curves are fiction, especially when compared against realistic B2B SaaS funnel conversion benchmarks. Leadership that makes channel investment decisions based on inflated LTV assumptions is essentially gambling with a spreadsheet that feels like a strategy.

SaaS performance marketing, done correctly, forces the entire organization to reckon with these numbers together. Marketing does not hand off a lead and disappear. Finance does not sit downstream calculating damage after the fact.

The metrics connect acquisition to activation to retention to expansion, and every channel decision is evaluated against the full arc.

How SaaS Marketing Channels Behave Differently from Every Other Business Model

The channel economics in SaaS are genuinely distinct from e-commerce or direct response. Understanding this distinction directly determines where you should allocate your next million dollars and how it fits into your broader B2B SaaS growth marketing strategy.

Paid Search in SaaS

Paid search captures existing demand. If someone is searching for “project management software for engineering teams,” they are already in consideration mode.

The conversion path is shorter, but the competitive density is extreme, and CPCs in mature SaaS categories have become punishing. Spending aggressively on branded and category keywords makes sense when your conversion rates and contract values justify it.

And when they don’t, you are subsidizing Google’s revenue growth, not your own.

SEO and Content Marketing

Content and SEO compound in ways that paid channels simply cannot, especially when supported by a strong SaaS content marketing playbook. A well-executed SEO strategy for a SaaS product targets buyers at the problem-awareness stage, way before they have formed vendor shortlists.

It’s where you build category authority beyond awareness.

The distinction matters because category authority shortens sales cycles and reduces the cost of comparison shopping. Buyers who find you through organic search and consume your content before evaluation walk into demos with fundamentally different purchase intent.

Product-Led Growth as a SaaS Performance Marketing Channel

PLG has reshaped the performance marketing conversation entirely in the last five years and is now a central component of modern SaaS product marketing strategies.

When your product itself becomes a distribution channel or when a free trial creates a pipeline of educated, activated users, the performance metrics shift. CAC drops because the product does acquisition work.

But the measurement complexity increases because you now need to track conversion from free to paid with the same rigor you apply to paid channel funnels.

Many companies celebrate their PLG motion without ever properly instrumenting it.

Partner and Ecosystem Marketing

Partnerships and ecosystem plays are underused in SaaS performance strategy because they resist easy attribution, despite being powerful channels similar to SaaS affiliate marketing and referral programs.

But in enterprise SaaS, especially, distribution through trusted partners often delivers customers with higher ACV and lower churn than any owned channel.

The performance marketing team that only measures what it can attribute directly in a last-touch model will systematically underinvest in this case.

SaaS Marketing Attribution Is an Organizational Problem

Attribution in SaaS is not a technical problem. It’s a political one, and it often requires aligning the organization around core B2B SaaS marketing principles. And until leadership decides to solve it honestly, performance marketing will continue to be measured in ways that flatter individual channels while obscuring the truth about what’s actually driving revenue.

A typical B2B SaaS buying journey involves eight to twelve touchpoints across a 3 to 6-month period.

A prospect reads a thought leadership piece in January. They see a retargeting ad in February. They watched a competitor comparison webinar in March. A peer mentions your product in a Slack community in April. They signed up for a demo in May.

Which channel gets credit?

Last-touch attribution gives it to the demo request form. Multi-touch models distribute credit based on mathematical assumptions that are largely arbitrary. Marketing mix modeling requires data volumes that most growth-stage companies don’t have.

The honest answer is that none of these solves the problem completely.

What they can do is give leadership a directionally accurate picture of how influence accumulates across the funnel. The goal is not perfect attribution.

The goal is a resource allocation strategy that reflects the actual buying journey of your best customers, not the one that happens to be easiest to measure.

That requires qualitative and quantitative work.

Post-sale interview is the most valuable data point in understanding SaaS channel performance and refining SaaS marketing lead scoring methods. Ask your highest-LTV customers and most recent customers how they actually found you, what content they consumed, and what tipped their decision.

The answers will frequently contradict your attribution dashboard. That contradiction is information.

C-Suite Decisions That Define SaaS Performance Marketing Outcomes

Performance marketing strategy is ultimately about organizational design and must align with the broader SaaS marketing playbook followed by growth-stage companies.

The CMO cannot own this alone. The CFO needs to agree on how LTV is calculated. The CRO needs to align on what a qualified pipeline looks like. The CPO needs to ensure that product experience reinforces the promises made in acquisition channels.

The structural decision that has the highest leverage is where you draw the line of marketing accountability.

If marketing is accountable only to MQLs, you will optimize for MQLs. If marketing is accountable for revenue retained at twelve months, the entire team starts thinking differently about who they target, what they promise, and which segments they pursue.

Budget allocation is the other lever.

Most SaaS companies over-index on acquisition channels and under-invest in motions that compound over time, even though thought leadership in SaaS marketing can build durable long-term demand. That’s understandable because the board wants to see growth in the next quarter, and compounding takes longer to show up in a slide.

But the companies that build durable SaaS growth are almost always the ones that maintain investment in long-cycle channels even when short-term pressure pushes in the other direction.

The Stakes for SaaS Performance Marketing are High in a Saturated Market

The SaaS market has matured, and understanding the total addressable market in SaaS has become critical for sustainable expansion strategies. The days when a decent product and a functioning demand gen operation were enough to achieve venture-scale growth are behind us. Buyers today are more sophisticated, which is reflected in the evolving SaaS market trends shaping the industry. Categories are more crowded. The cost of paid acquisition has increased substantially across virtually every major channel.

In that environment, SaaS performance marketing is no longer a growth lever. It is a competitive differentiator. And a majority of businesses still overlook it.

But those who understand it deeply, measure it honestly, and align their organizations around it will grow efficiently by following a structured B2B SaaS market strategy. The ones that keep applying generic performance marketing principles to a fundamentally different business model will keep wondering why the CAC keeps climbing, and the growth keeps stalling.

The correct SaaS performance marketing playbook exists. It just requires leadership willing to read it clearly.

Pentagon Labels Anthropic as Supply Chain Risk
News

Pentagon Labels Anthropic as Supply Chain Risk

Pentagon Labels Anthropic as Supply Chain Risk

AI is accelerating innovation across industries. But the same acceleration is beginning to worry national security experts.

A new warning from the UK government is forcing a difficult question into the open. What happens when powerful AI systems start lowering the barrier to building biological weapons?

According to a government assessment, advanced AI tools could enable individuals with limited scientific training to design biological weapons within the next two years. The concern is not that AI will create pathogens on its own. The concern is that it could dramatically reduce the expertise required to do it.

Large language models are already capable of synthesizing scientific literature, explaining complex lab techniques, and guiding research workflows. In the right hands, that capability speeds up medical breakthroughs. In the wrong hands, it could compress the learning curve required to misuse biotechnology.

It’s where the technology risk becomes systemic.

Modern biotech research is highly distributed. Your labs, universities, and startups can already access gene-editing tools and cloud-based research databases. AI adds another layer by acting as an always-on research assistant capable of navigating vast scientific knowledge.

That combination worries security analysts.

Can AI systems help design experiments, suggest biological targets, or interpret genetic data? They could inadvertently make dangerous research more accessible. Not because the models intend harm, but because they optimize for answering questions and solving problems.

For technology leaders, the issue goes beyond AI safety debates. It touches governance, model capabilities, and the responsibilities of companies building frontier systems.

The industry has focused heavily on economic transformation- productivity, automation, and new digital platforms. But the same models driving that transformation are also expanding access to knowledge that once required years of training.

The UK’s warning reflects a growing realization.

AI is not just a software platform. It is a knowledge accelerator. And when knowledge becomes easier to access, both innovation and risk scale at the same time.

SoftBank Might Take a $40 Billion Loan to Double Down on OpenAI
News

SoftBank Might Take a $40 Billion Loan to Double Down on OpenAI

SoftBank Might Take a $40 Billion Loan to Double Down on OpenAI

Softbank is currently seeking out a loan of around $40 billion to fund its OpenAI investments. Who said the AI race was about building models?

SoftBank’s “alleged” loan is racking up all the headlines. It’s exploring a loan of up to $40 billion to fund quite a substantial investment in OpenAI. If the deal moves forward? It could rank among the heftiest borrowings ever tied to a single AI bet.

And the reasoning is not complicated.

AI has become the most aggressive capital race in tech. Training models requires enormous computing infrastructure. Running them requires even more. The companies that want influence in this ecosystem must fund both.

SoftBank appears ready to do exactly that.

The Japanese investment giant is discussing a short-term bridge loan with major banks, potentially including JPMorgan. The funds would ultimately help finance its growing stake in OpenAI.

This is not a cautious investment strategy.

SoftBank founder Masayoshi Son has built his reputation on making enormous bets when a technological shift becomes visible. Sometimes those bets worked spectacularly. Sometimes they didn’t. But the philosophy has always been the same: when a platform shift arrives, scale matters more than timing.

AI fits that pattern perfectly.

OpenAI has become one of the vital gravitational centers for the AI economy. That influence attracts capital from everywhere, i.e., cloud providers, chipmakers, and global investors. But SoftBank does not want to sit on the sidelines.

The risk, of course, is obvious. Borrowing tens of billions to invest in a single AI company assumes that the current momentum continues. It assumes AI adoption expands rapidly. And it assumes the economics of large models eventually stabilize.

None of that is guaranteed.

But the broader shift is becoming difficult to ignore. AI is no longer just a software industry. It’s become a capital industry.

Forget about algorithms. Infrastructure, compute, and financing are just as important in 2026. And those willing to deploy the largest amount of capital may shape how the entire AI ecosystem evolves.

Go to market strategy
Blogs

Go to Market Strategy B2B SaaS: The Most Overused Term That Nobody Actually Does

Go to Market Strategy B2B SaaS: The Most Overused Term That Nobody Actually Does

Every SaaS company has a go-to-market strategy. Almost none of them have actually built one. Here is what the term really means, why it keeps failing, and what understanding your buyer has to do with any of it.

Ask any SaaS founder what their go-to-market strategy is.

You will get one of two answers. A confident recitation of channels, a launch plan, maybe a persona deck. Or a vague gesture toward growth that has been dressed up in GTM language to sound like it was intentional.

Neither of those is a go-to-market strategy.

GTM has become one of those terms that gets used so often that it has stopped meaning anything. Marketers say it in planning meetings. VCs ask for it in pitch decks. RevOps teams build dashboards around it while debating different SaaS marketing approaches and playbooks. And somewhere in all of that repetition, the actual idea got hollowed out.

So let us start from the beginning.

What Go-to-Market Strategy Actually Means in B2B SaaS

GTM

Go-to-market does not mean a launch plan

A launch plan is a sequence of events. GTM is a theory of why a specific buyer will choose you, how they will find you, and what will make them stay.

Those are completely different things.

A launch plan says: we will post on LinkedIn, email our list, do a ProductHunt launch, run SaaS social media marketing campaigns, and send promotional sequences in week one. That is a campaign. That is not a strategy.

A go-to-market strategy says: this is who we are selling to and exactly why they have this problem right now, which starts with clearly defining your SaaS customer segments. This is the moment in their world when the problem becomes urgent. This is how they look for solutions. This is what makes our approach the one that fits their situation better than anything else available. This is how we reach them before they have already made a decision.

That requires you to actually understand the buyer. Not the persona card. The buyer.

It Is Engineering a Desired Outcome

Here is the frame that changes everything.

GTM is reverse engineering. You start with the outcome you want, which is a specific type of customer adopting your product and staying, and you work backwards to figure out every condition that needs to be true for that to happen.

What does the buyer need to believe? What objections will they arrive with? What internal champion do they need to have? What competitor will they compare you to, and why will you win that comparison? What does their buying committee care about that your champion does not?

Every single one of those questions has an answer. And your go-to-market strategy is the system that uses those answers to engineer the path from problem to purchase across the entire SaaS marketing funnel.

When you treat GTM as a launch plan, you skip the engineering. You go straight to broadcasting. And then you wonder why the pipeline is thin and the sales cycle is long.

Why B2B SaaS GTM Keeps Failing

The B2B Buyer Problem

There is a pattern in SaaS where the market gets blamed for not responding, even though the real issue often lies in poorly designed SaaS growth strategies.

The ICP is wrong. The timing is off. Buyers are not ready yet. The category needs more education.

Sometimes that is true. Most of the time, it is not.

Most of the time, the GTM failed because the team did not do the work to understand what the buyer actually needs to hear, from whom, at what moment, in what context. They made assumptions, built campaigns on those assumptions, measured the wrong things, and called it a strategy.

The buyer was never the problem. The understanding of the buyer was.

Flashy Content and Mass Ads Are Symptoms, Not Strategies

You can tell when a SaaS company does not have a real GTM because of what their marketing looks like.

Generic LinkedIn thought leadership that could apply to any company in any category instead of meaningful thought leadership in SaaS marketing that actually speaks to buyer problems. Case studies that say a lot without saying anything specific. Webinars that are demos wearing a disguise. Ads that lead to landing pages that explain features instead of solving the problem the buyer typed into Google at eleven at night while researching solutions through SEO for SaaS.

This is what happens when GTM gets reduced to output instead of thinking.

The content exists because it is on the checklist. The ads exist because the budget needs to be spent. The campaigns exist because someone needed to show activity.

None of it was built backwards from a deep understanding of who the buyer is and what would actually move them.

What Understanding the Buyer Really Requires

It Requires Sitting With Uncomfortable Specificity

Most teams do buyer research once, declare victory, and move on instead of continuously refining SaaS product-market alignment. They have a persona. They know the job title. They have the pain points listed in a slide deck somewhere.

That is surface level. It is not enough to build a GTM on.

Real buyer understanding means you know what the buyer’s world looks like on the day they realize they have your problem. You know who they talk to about it first. You know what they search for. You know what they read. You know what the conversation in the buying committee sounds like when your product comes up. You know what the person championing you internally has to say to get the budget approved.

You know what failure looks like for them if they choose wrong. You know what success looks like if they choose right. You know which of those two motivations is stronger.

That level of specificity feels excessive until you use it. Then it feels like the only way to build anything that actually works.

It Requires Sales and Marketing to Stop Living in Separate Buildings

Go to market strategy in B2B SaaS breaks down most consistently at the handoff between marketing and sales.

Marketing builds campaigns based on what they think buyers care about instead of grounding them in real lead generation for SaaS insights from the market. Sales has conversations with actual buyers every day and learns something completely different. Those two bodies of knowledge almost never talk to each other in a structured way.

So marketing keeps running campaigns built on assumptions that sales could correct in ten minutes. And sales keep having conversations that marketing could arm them for better if they knew what was actually happening in those calls.

GTM is supposed to be the system that connects those two realities. When it is treated as a launch plan that belongs to marketing, that connection never gets built.

It Requires You to Know Why You Win and Why You Lose

Not in the abstract. Specifically.

What are the three types of deals you win most reliably, and how do those wins reflect the core principles of B2B SaaS marketing you are applying? What do those buyers have in common? What did they believe before they met you that made them ready to buy?

What are the three types of deals you consistently lose? What objection comes up every time? What competitor keeps beating you in those situations and why?

A real go-to-market strategy is built around the answers to those questions. It amplifies the conditions that produce wins and avoids creating the conditions that produce losses.

Most SaaS companies do not have clean answers to those questions. Which means they are running a GTM motion without the most important data it needs.

What a Real B2B SaaS GTM Strategy Looks Like in Practice

Buyer

Start With the Moment of Urgency

Every buyer has a moment when the problem you solve becomes urgent enough to act on. Something changes in their world. A new regulation. A failed audit. A competitor is doing something they cannot ignore. A new hire who is pushing for a solution. A board conversation that suddenly made the problem visible.

Your GTM strategy needs to be built around that moment. Not around your product launch calendar.

What triggers urgency for your buyer, and how does that connect to the total addressable market for your SaaS product? What event, condition, or context takes your problem from background noise to top priority? That is your entry point. That is where your messaging should meet them.

Define the Channel as a Consequence of the Buyer, Not a Default

Most SaaS companies pick channels based on what everyone else in their category is doing rather than building a B2B SaaS growth marketing strategy around the buyer. Everybody does LinkedIn. Everybody does content. Everybody does outbound sequences.

The channel should be chosen because that is where your specific buyer is, in the specific moment of urgency, looking for the specific information that moves them toward a decision.

If your buyer is a CISO who only trusts peer recommendations, your channel is community and referral. If your buyer is a RevOps manager who researches obsessively before talking to anyone, your channel is search and deep educational content. If your buyer is an enterprise CFO, your channel is the conversation your champion has in the room you will never be in.

Channel follows the buyer. Not the other way around.

Measure What Moves the Buyer, Not Just What the Dashboard Shows

The metrics SaaS companies typically track in GTM are activity metrics dressed up as outcome metrics.

Leads generated. MQLs. Opportunities created. These are only a small subset of the SaaS metrics that actually matter. These tell you what the funnel looks like. They do not tell you whether your GTM is actually working.

What moves a buyer from unaware to purchased to retained and eventually reduces churn across the lifecycle through effective churn reduction strategies in SaaS. Map that journey specifically. Then measure whether your GTM is actually producing the conditions that advance buyers along it.

That is a harder measurement problem. It requires conversations with customers, win-loss interviews, and honest analysis of why deals stall. It is worth doing. It is the only way to know if your strategy is working or if you are just generating activity.

GTM is not a marketing deliverable. It is not a launch plan. It is not a set of channels or a content calendar or a paid acquisition strategy.

It is the answer to a single question.

Why will a specific buyer, in a specific situation, choose us, right now, and stay?

If you cannot answer that question with specificity, you do not have a go-to-market strategy. You have a motion. And motions burn the budget without compounding.

The SaaS companies that are actually winning right now are the ones that did the slow, uncomfortable work of understanding their buyer at a level that most teams never bother to reach. They built the GTM backwards from that understanding. And the output looks different from the noise because it was made for a specific person, not a generic market.

That is the whole thing.

Go understand the buyer. The strategy follows from that. Everything else is just execution

X Chat Launches on Apple TestFlight A Piece on Human Connection
Uncategorized

X-Chat Launches on Apple TestFlight: A Piece on Human Connection

X-Chat Launches on Apple TestFlight: A Piece on Human Connection

X launched a standalone messaging app on Apple’s TestFlight last week. The first 1,000 beta spots filled in under two hours. What a demand.

By the end of the day, that cap had been expanded to 5,000. Whatever people feel about the platform it comes from, they signed up fast.

That appetite is worth taking seriously because the product itself is not unreasonable. A clean, dedicated messaging interface that syncs across X’s web platform and app, separated from the noise of the public feed, addresses something real. The public forum and the private conversation are different acts, and cramming them into a single product has always created friction. Splitting them out is the kind of sensible, user-oriented decision that tends to get obscured when the person making it has spent the past two years making the platform objectively harder to trust.

The trust problem is specific. Security researchers are already asking whether X Chat’s encryption holds up against Signal or WhatsApp. Clear answers have not arrived. For a standalone messaging app, that is not a footnote question. It is the question. Private messaging carries a different weight than a public post. People use it for things they mean only one person to see. The infrastructure that protects that deserves scrutiny that is proportional to what is at risk, and right now, the scrutiny is outpacing the transparency.

The deeper thing this product surfaces, though, is not about X at all. It is about what we have collectively decided communication means.

We now have messaging apps, group apps, social feeds, work channels, community forums, and comment threads, each pulling on attention simultaneously, each optimised to keep the conversation going rather than to make the conversation good. X Chat will add another layer to that. The first 5,000 testers are almost certainly people who already manage six other inboxes. The question nobody is asking when a new chat product launches is not whether it works. It is whether more connection is the same thing as better connection, and whether the expectation of constant availability has quietly replaced the experience of actually being present with another person.

Human beings communicated across distance for centuries before any of this existed. They wrote letters that took weeks to arrive and thought carefully about what they wanted to say. That is not nostalgia for a slower world. It is a data point about what communication costs when it costs something, and what it becomes when it costs nothing.

X Chat may be a perfectly functional product. The beta enthusiasm suggests it might even be a good one. What it will not solve is the thing it is also making worse.